- Editor's Choices
- Current Issue
- Archive
- Most Downloaded
Display Method:
Abstract:
Aiming at the problem that indoor positioning technology based on wireless ultra-wideband pulse technology is susceptible to non-line-of-sight effects and multipath effects in confined spaces and weak signal environments, a high-precision positioning system based on UWB and IMU in a confined environment is designed. The STM32 chip is used as the main control, and the data information of IMU and UWB is fused by the fusion filtering algorithm. Finally, the real-time information of the positioning is transmitted to the host computer and the cloud. The experimental results show that the positioning accuracy and positioning stability of the system have been improved in the non-line-of-sight case of closed environment. The system has high positioning accuracy in a closed environment, and the components used are consumer-grade, which has strong practicability.
Aiming at the problem that indoor positioning technology based on wireless ultra-wideband pulse technology is susceptible to non-line-of-sight effects and multipath effects in confined spaces and weak signal environments, a high-precision positioning system based on UWB and IMU in a confined environment is designed. The STM32 chip is used as the main control, and the data information of IMU and UWB is fused by the fusion filtering algorithm. Finally, the real-time information of the positioning is transmitted to the host computer and the cloud. The experimental results show that the positioning accuracy and positioning stability of the system have been improved in the non-line-of-sight case of closed environment. The system has high positioning accuracy in a closed environment, and the components used are consumer-grade, which has strong practicability.
Abstract:
The process of wafer polishing is known to be highly demanding, and even small deviations in the processing parameters can have a significant impact on the quality of the wafers obtained. During the process of wafer polishing, maintaining a constant pressure value applied by the polishing head is essential to achieve the desired flatness of the wafer. The accuracy of the downward pressure output by the polishing head is a crucial factor in producing flat wafers. In this paper, the uncertainty component of downward pressure is calculated and its measurement uncertainty is evaluated, and a method for calculating downward pressure uncertainty traceable to international basic unit is established. Therefore, the reliability of double side polishing machine has been significantly improved.
The process of wafer polishing is known to be highly demanding, and even small deviations in the processing parameters can have a significant impact on the quality of the wafers obtained. During the process of wafer polishing, maintaining a constant pressure value applied by the polishing head is essential to achieve the desired flatness of the wafer. The accuracy of the downward pressure output by the polishing head is a crucial factor in producing flat wafers. In this paper, the uncertainty component of downward pressure is calculated and its measurement uncertainty is evaluated, and a method for calculating downward pressure uncertainty traceable to international basic unit is established. Therefore, the reliability of double side polishing machine has been significantly improved.
Abstract:
Aiming at the scattering and absorption of light in the water body, which causes the problems of color shift, uneven brightness, poor sharpness and missing details in the acquired underwater images, an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed. Firstly, the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction; secondly, the image is converted to HSV color space, and the segmentation exponential algorithm is used to process the S component to enhance the image saturation; finally, multi-scale Retinex is used to decompose the V component image into detail layer and base layer, and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness, while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information. The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations, and the information entropy of the image is improved by 6.3% on average, and the UIQM and UCIQE indexes are improved by 12.9% and 20.3% on average.
Aiming at the scattering and absorption of light in the water body, which causes the problems of color shift, uneven brightness, poor sharpness and missing details in the acquired underwater images, an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed. Firstly, the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction; secondly, the image is converted to HSV color space, and the segmentation exponential algorithm is used to process the S component to enhance the image saturation; finally, multi-scale Retinex is used to decompose the V component image into detail layer and base layer, and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness, while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information. The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations, and the information entropy of the image is improved by 6.3% on average, and the UIQM and UCIQE indexes are improved by 12.9% and 20.3% on average.
Abstract:
With the rapid development of social economy, transportation has become faster and more efficient. As an important part of goods transportation, the safe maintenance of tunnel highways has become particularly important. The maintenance of tunnel roads has become more difficult due to problems such as sealing, narrowness and lack of light. Currently, target detection methods are advantageous in detecting tunnel vehicles in a timely manner through monitoring. Therefore, in order to prevent vehicle misdetection and missed detection in this complex environment, we propose a YOLOv5-Vehicle model based on the YOLOv5 network. This model is improved in three ways. Firstly, The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features, which reduces the number of model parameters; Next, all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation, and improve the detection speed of the model; Finally, to ensure the accuracy of the model, the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the model. Experiments results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.
With the rapid development of social economy, transportation has become faster and more efficient. As an important part of goods transportation, the safe maintenance of tunnel highways has become particularly important. The maintenance of tunnel roads has become more difficult due to problems such as sealing, narrowness and lack of light. Currently, target detection methods are advantageous in detecting tunnel vehicles in a timely manner through monitoring. Therefore, in order to prevent vehicle misdetection and missed detection in this complex environment, we propose a YOLOv5-Vehicle model based on the YOLOv5 network. This model is improved in three ways. Firstly, The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features, which reduces the number of model parameters; Next, all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation, and improve the detection speed of the model; Finally, to ensure the accuracy of the model, the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the model. Experiments results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.
Abstract:
As an important part of water level warning in water conservancy projects, often due to the influence of environmental factors such as light and stains, the acquired water gauge images have sticky, broken and bright spot conditions, which affect the identification of water gauges. To solve this problem, a water gauge image denoising model based on improved adaptive total variation is proposed. Firstly, the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function; secondly, the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points; finally, according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering, the New model can adaptively denoise in the smooth area and protect the edge area, so as to have the characteristics of both edge-preserving denoising. The experimental results show that the New model has a great improvement in image vision, higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio, and an average increase of 9% in structural similarity, which is more beneficial to practical applications.
As an important part of water level warning in water conservancy projects, often due to the influence of environmental factors such as light and stains, the acquired water gauge images have sticky, broken and bright spot conditions, which affect the identification of water gauges. To solve this problem, a water gauge image denoising model based on improved adaptive total variation is proposed. Firstly, the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function; secondly, the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points; finally, according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering, the New model can adaptively denoise in the smooth area and protect the edge area, so as to have the characteristics of both edge-preserving denoising. The experimental results show that the New model has a great improvement in image vision, higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio, and an average increase of 9% in structural similarity, which is more beneficial to practical applications.
Abstract:
Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning. Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods, a new multimodal medical image fusion method is proposed. This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients, then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients, and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients. Finally, based on the automatic setting of parameters, the optimization method configuration of the time decay factor 𝛼𝑒 is carried out. The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images, and at the same time, it has achieved great improvement in visual quality and objective evaluation indicators.
Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning. Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods, a new multimodal medical image fusion method is proposed. This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients, then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients, and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients. Finally, based on the automatic setting of parameters, the optimization method configuration of the time decay factor 𝛼𝑒 is carried out. The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images, and at the same time, it has achieved great improvement in visual quality and objective evaluation indicators.
Abstract:
It is well known that the accuracy of camera calibration is constrained by the size of the reference plate, it is difficult to fabricate large reference plates with high precision. Therefore, it is non-trivial to calibrate a camera with large field of view (FOV). In this paper, a method is proposed to construct a virtual large reference plate with high precision. Firstly, a high precision datum plane is constructed with a laser interferometer and one-dimensional air guideway, and then the reference plate is positioned at different locations and orientations in the FOV of the camera. The feature points of reference plate is projected to the datum plane to obtain a virtual large reference plate with high-precision. The camera are moved to several positions to get different virtual reference plates, and the camera is calibrated with the virtual reference plates. The experimental results show that the mean re-projection error of the camera calibrated with the proposed method is 0.062 pixels. The length of a scale bar with standard length of 959.778mm was measured with a vision system composed of two calibrated cameras, and the length measurement error is 0.389mm.
It is well known that the accuracy of camera calibration is constrained by the size of the reference plate, it is difficult to fabricate large reference plates with high precision. Therefore, it is non-trivial to calibrate a camera with large field of view (FOV). In this paper, a method is proposed to construct a virtual large reference plate with high precision. Firstly, a high precision datum plane is constructed with a laser interferometer and one-dimensional air guideway, and then the reference plate is positioned at different locations and orientations in the FOV of the camera. The feature points of reference plate is projected to the datum plane to obtain a virtual large reference plate with high-precision. The camera are moved to several positions to get different virtual reference plates, and the camera is calibrated with the virtual reference plates. The experimental results show that the mean re-projection error of the camera calibrated with the proposed method is 0.062 pixels. The length of a scale bar with standard length of 959.778mm was measured with a vision system composed of two calibrated cameras, and the length measurement error is 0.389mm.
Abstract:
Speech recognition is a hot topic in the field of artificial intelligence. Generally, speech recognition models can only run on large servers or dedicated chips. This paper presents a keyword speech recognition system based on a neural network and a conventional STM32 chip. To address the limited Flash and ROM resources on the STM32 MCU chip, the deployment of the speech recognition model is optimized to meet the requirements of keyword recognition. Firstly, the audio information obtained through sensors is subjected to MFCC (Mel Frequency Cepstral Coefficient) feature extraction, and the extracted MFCC features are input into a CNN (Convolutional Neural Network) for deep feature extraction. Then, the features are input into a fully connected layer, and finally, the speech keyword is classified and predicted. Deploying the model to the STM32F429, the prediction model achieves an accuracy of 90.58%, a decrease of less than 1% compared to the accuracy of 91.49% running on a computer, with good performance.
Speech recognition is a hot topic in the field of artificial intelligence. Generally, speech recognition models can only run on large servers or dedicated chips. This paper presents a keyword speech recognition system based on a neural network and a conventional STM32 chip. To address the limited Flash and ROM resources on the STM32 MCU chip, the deployment of the speech recognition model is optimized to meet the requirements of keyword recognition. Firstly, the audio information obtained through sensors is subjected to MFCC (Mel Frequency Cepstral Coefficient) feature extraction, and the extracted MFCC features are input into a CNN (Convolutional Neural Network) for deep feature extraction. Then, the features are input into a fully connected layer, and finally, the speech keyword is classified and predicted. Deploying the model to the STM32F429, the prediction model achieves an accuracy of 90.58%, a decrease of less than 1% compared to the accuracy of 91.49% running on a computer, with good performance.
Abstract:
In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance, a fatigue detection method based on multi-feature fusion is proposed. Firstly, the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth, fast track the detected faces and extract continuous and stable target faces for more efficient extraction. Then the head pose algorithm is introduced to detect the driver"s head in real time and obtain the driver"s head state information. Finally, a multi-feature fusion fatigue detection method is proposed based on the state of the eyes, mouth and head. According to the experimental results, the proposed method can detect the driver"s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.
In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance, a fatigue detection method based on multi-feature fusion is proposed. Firstly, the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth, fast track the detected faces and extract continuous and stable target faces for more efficient extraction. Then the head pose algorithm is introduced to detect the driver"s head in real time and obtain the driver"s head state information. Finally, a multi-feature fusion fatigue detection method is proposed based on the state of the eyes, mouth and head. According to the experimental results, the proposed method can detect the driver"s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.
Abstract:
Tongue diagnosis is a non-invasive, efficient, and accurate method for determining a person''s physical condition, and plays an essential role in disease diagnosis and health management. However, tongue diagnosis is easily influenced by the subjective experience of the practitioner and the light environment. In addition, tongue diagnosis lacks clear quantitative indicators and objective records. This all limits the transmission and development of tongue diagnosis. Therefore, the acquisition and analysis of tongue information using image equipment, image processing and computer vision have become a hot research topic for the objectification of tongue diagnosis. This paper reviews the research progress of tongue diagnosis objectification in Traditional Chinese medicine. The tongue image acquisition, color correction, segmentation, feature extraction and analysis, and disease prediction included in the study of tongue diagnosis objectification are reviewed. The shortcomings of current automated tongue diagnosis systems and future research ideas are also summarized to provide a reference for further development of tongue diagnosis objectification.
Tongue diagnosis is a non-invasive, efficient, and accurate method for determining a person''s physical condition, and plays an essential role in disease diagnosis and health management. However, tongue diagnosis is easily influenced by the subjective experience of the practitioner and the light environment. In addition, tongue diagnosis lacks clear quantitative indicators and objective records. This all limits the transmission and development of tongue diagnosis. Therefore, the acquisition and analysis of tongue information using image equipment, image processing and computer vision have become a hot research topic for the objectification of tongue diagnosis. This paper reviews the research progress of tongue diagnosis objectification in Traditional Chinese medicine. The tongue image acquisition, color correction, segmentation, feature extraction and analysis, and disease prediction included in the study of tongue diagnosis objectification are reviewed. The shortcomings of current automated tongue diagnosis systems and future research ideas are also summarized to provide a reference for further development of tongue diagnosis objectification.
Abstract:
On account of the traditional multiple signal classification (MUSIC) algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR. In this paper, the traditional MUSIC algorithm is improved. The algorithm combines the idea of spatial smoothing, constructs a new covariance matrix using the covariance information of the measurement data, and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum. Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation. The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.
On account of the traditional multiple signal classification (MUSIC) algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR. In this paper, the traditional MUSIC algorithm is improved. The algorithm combines the idea of spatial smoothing, constructs a new covariance matrix using the covariance information of the measurement data, and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum. Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation. The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.
Abstract:
Metal substance detection plays an extremely important role in daily life, industrial manufacturing and even industrial security. The traditional methods include optical detection, X-ray detection, microwave detection and ultrasonic detection. These methods, playing a vital role in the field of non-destructive testing, can not only judge the presence or absence of metal, but also accurately detect the type and size of metal defects. For microwave detection, the detection efficiency of metal materials is limited by the response sensitivity of the detector to microwaves. In recent years, scientists have discovered a quantum sensing system based on the diamond nitrogen-vacancy (NV) color center. The system obtains optical detection magnetic resonance (ODMR) fluorescence spectra under the combined action of a 532nm laser and a certain frequency band of microwaves, and the signal contrast changes significantly with the microwave power. Based on the NV color center quantum sensing system, this paper studies its application in the field of metal detection, and takes steel detection as an example to detect the size of steel bars according to the changes in the spectral line, providing a new method for non-destructive testing such as metal substance detection.
Metal substance detection plays an extremely important role in daily life, industrial manufacturing and even industrial security. The traditional methods include optical detection, X-ray detection, microwave detection and ultrasonic detection. These methods, playing a vital role in the field of non-destructive testing, can not only judge the presence or absence of metal, but also accurately detect the type and size of metal defects. For microwave detection, the detection efficiency of metal materials is limited by the response sensitivity of the detector to microwaves. In recent years, scientists have discovered a quantum sensing system based on the diamond nitrogen-vacancy (NV) color center. The system obtains optical detection magnetic resonance (ODMR) fluorescence spectra under the combined action of a 532nm laser and a certain frequency band of microwaves, and the signal contrast changes significantly with the microwave power. Based on the NV color center quantum sensing system, this paper studies its application in the field of metal detection, and takes steel detection as an example to detect the size of steel bars according to the changes in the spectral line, providing a new method for non-destructive testing such as metal substance detection.
Abstract:
Deep stochastic configuration networks (DSCNs) produce redundant hidden nodes and connections during training, which complicates their model structures. Aiming at the above problems, this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance. During the training process, the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner, the node pruning rate of each layer is set according to the depth of the DSCN at the current time, the nodes that contribute little to the model are deleted, and the network-related parameters are updated. When the model completes the configuration procedure, the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections; then, the network parameters are updated after pruning is completed. The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed; the model accuracy loss is small, and fine-tuning for accuracy restoration is not needed. The obtained DSCN model has certain application value in the field of regression analysis.
Deep stochastic configuration networks (DSCNs) produce redundant hidden nodes and connections during training, which complicates their model structures. Aiming at the above problems, this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance. During the training process, the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner, the node pruning rate of each layer is set according to the depth of the DSCN at the current time, the nodes that contribute little to the model are deleted, and the network-related parameters are updated. When the model completes the configuration procedure, the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections; then, the network parameters are updated after pruning is completed. The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed; the model accuracy loss is small, and fine-tuning for accuracy restoration is not needed. The obtained DSCN model has certain application value in the field of regression analysis.
Abstract:
For the current robotic grasping scenario, the market for single gripper grippers to grasp items is limited, expensive, difficult to use, after-sales cumbersome, and other problems. This paper designs a multi-functional gripper, integrating electro-pneumatic functions and designing multi-functional flanges, which can be used for all kinds of robots and multi-angle mounting, and designs multiple suction cups on the basis of the electric gripper to solve the problem that some items cannot be grasped, and designs various finger grippers at the end of the gripper to solve the problem of grasping items of different shapes. In this paper, the jaws are analyzed using the ANSYS Workbench for static simulations and also tested for gripping stability with a dozen terms. The versatile gripper has the advantages of compact design, reliable grip, easy maintenance, high-cost performance, and multi-scene use.
For the current robotic grasping scenario, the market for single gripper grippers to grasp items is limited, expensive, difficult to use, after-sales cumbersome, and other problems. This paper designs a multi-functional gripper, integrating electro-pneumatic functions and designing multi-functional flanges, which can be used for all kinds of robots and multi-angle mounting, and designs multiple suction cups on the basis of the electric gripper to solve the problem that some items cannot be grasped, and designs various finger grippers at the end of the gripper to solve the problem of grasping items of different shapes. In this paper, the jaws are analyzed using the ANSYS Workbench for static simulations and also tested for gripping stability with a dozen terms. The versatile gripper has the advantages of compact design, reliable grip, easy maintenance, high-cost performance, and multi-scene use.
Abstract:
Discrete Cosine Transform (DCT) is the most widely used technique in image and video compression. In this paper, the structure of DCT and Inverse DCT (IDCT) algorithm is split in the form of COordinate Rotation DIgital Computer (CORDIC) rotation matrix. The two-dimensional (2-D) 88 DCT/IDCT units based on the improved rotation CORDIC algorithm is proposed. The shift and addition operations of the CORDIC algorithm are used to replace the cosine multiplication operations in the algorithm. The design does not contain any multiplier unit, which reduces the complexity of the hardware unit. The row-column transform unit composed of register arrays connects two 1-D 8-point DCT units to complete the calculation of 2-D 88 DCT. The pipeline latency of proposed architecture is 28 clock cycles. The proposed efficient two-dimensional DCT architecture has been synthesized on the Xilinx’s Kintex-7 FPGA. The resource utilization is 17.36 % for Slice LUTs, 3.49 % for Slice Registers, and the maximum operating frequency is 172 MHz. It takes only 0.161s to complete a process of block of 88 samples. A frame of image is processed by the designed DCT unit and then reconstructed by the IDCT unit to verify the function. The Peak Signal to Noise Ratio (PSNR) can reach 51.99 dB.
Discrete Cosine Transform (DCT) is the most widely used technique in image and video compression. In this paper, the structure of DCT and Inverse DCT (IDCT) algorithm is split in the form of COordinate Rotation DIgital Computer (CORDIC) rotation matrix. The two-dimensional (2-D) 88 DCT/IDCT units based on the improved rotation CORDIC algorithm is proposed. The shift and addition operations of the CORDIC algorithm are used to replace the cosine multiplication operations in the algorithm. The design does not contain any multiplier unit, which reduces the complexity of the hardware unit. The row-column transform unit composed of register arrays connects two 1-D 8-point DCT units to complete the calculation of 2-D 88 DCT. The pipeline latency of proposed architecture is 28 clock cycles. The proposed efficient two-dimensional DCT architecture has been synthesized on the Xilinx’s Kintex-7 FPGA. The resource utilization is 17.36 % for Slice LUTs, 3.49 % for Slice Registers, and the maximum operating frequency is 172 MHz. It takes only 0.161s to complete a process of block of 88 samples. A frame of image is processed by the designed DCT unit and then reconstructed by the IDCT unit to verify the function. The Peak Signal to Noise Ratio (PSNR) can reach 51.99 dB.
Abstract:
The propagation of shock wave pressure in the tunnel is greatly affected by the tunnel structure, shape, material and other factors, and there are great differences in the propagation law of shock wave pressure in different kinds of tunnels. In order to study the propagation law of shock wave pressure in tunnels with different materials, taking the long straight tunnel with the square section as an example, the AUTODYN software is used to simulate the explosion of TNT in the concrete, steel and granite tunnel, and study on the variation law of shock wave pressure in tunnels with different materials. By using dimensional analysis and combined with the results of numerical simulation, a mathematical model of the propagation law of shock wave pressure in the tunnel is established, and the effectiveness of the mathematical model is verified by making the explosion test of the warhead in the reinforce concrete tunnel. The results show that the same mass of TNT explodes in the tunnel with different materials, and the shock wave overpressure peak at the same measuring point is approximate in the near field. However, there is a significant difference in the middle-far fields from the explosion center, the shock wave overpressure peak in the steel tunnel is 20.76% and 34.82% higher than that of the concrete and the granite tunnel respectively, and the shock wave overpressure peak in the concrete tunnel is 24.91% higher than that in the granite tunnel. Through the experimental verification, getting the result that the maximum relative deviation between the measured value and the calculated value of the shock wave overpressure peak is 11.85%. Therefore, it is proved that the mathematical model can be used to predict the shock wave overpressure peak in the tunnel with different materials, and it can provide some reference for the power evaluation of warhead explosion in the tunnel.
The propagation of shock wave pressure in the tunnel is greatly affected by the tunnel structure, shape, material and other factors, and there are great differences in the propagation law of shock wave pressure in different kinds of tunnels. In order to study the propagation law of shock wave pressure in tunnels with different materials, taking the long straight tunnel with the square section as an example, the AUTODYN software is used to simulate the explosion of TNT in the concrete, steel and granite tunnel, and study on the variation law of shock wave pressure in tunnels with different materials. By using dimensional analysis and combined with the results of numerical simulation, a mathematical model of the propagation law of shock wave pressure in the tunnel is established, and the effectiveness of the mathematical model is verified by making the explosion test of the warhead in the reinforce concrete tunnel. The results show that the same mass of TNT explodes in the tunnel with different materials, and the shock wave overpressure peak at the same measuring point is approximate in the near field. However, there is a significant difference in the middle-far fields from the explosion center, the shock wave overpressure peak in the steel tunnel is 20.76% and 34.82% higher than that of the concrete and the granite tunnel respectively, and the shock wave overpressure peak in the concrete tunnel is 24.91% higher than that in the granite tunnel. Through the experimental verification, getting the result that the maximum relative deviation between the measured value and the calculated value of the shock wave overpressure peak is 11.85%. Therefore, it is proved that the mathematical model can be used to predict the shock wave overpressure peak in the tunnel with different materials, and it can provide some reference for the power evaluation of warhead explosion in the tunnel.
Abstract:
Low-order wavefront error account for a large proportion of wave aberrations. A compensation method for low order aberration of projection lithography objective based on Interior Point Method is presented. Compensation model between wavefront error and degree of movable lens freedom is established. Converting over-determined system to underdetermined system, the compensation is solved by Interior Point Method (IPM). The presented method is compared with direct solve the over-determined system. Then, other algorithm GA, EA and PS is compared with IPM. Simulation and experimental results show that the presented compensation method can obtained compensa-tion with less residuals compared with direct solve the over-determined system. Also, the presented compensation method can reduce computation time and obtain results with less residuals compare with AGA, EA and PS. Moreover, after compensation, RMS of wavefront error of the experimental lithography projection objective decrease from 56.05 nm to 17.88 nm.
Low-order wavefront error account for a large proportion of wave aberrations. A compensation method for low order aberration of projection lithography objective based on Interior Point Method is presented. Compensation model between wavefront error and degree of movable lens freedom is established. Converting over-determined system to underdetermined system, the compensation is solved by Interior Point Method (IPM). The presented method is compared with direct solve the over-determined system. Then, other algorithm GA, EA and PS is compared with IPM. Simulation and experimental results show that the presented compensation method can obtained compensa-tion with less residuals compared with direct solve the over-determined system. Also, the presented compensation method can reduce computation time and obtain results with less residuals compare with AGA, EA and PS. Moreover, after compensation, RMS of wavefront error of the experimental lithography projection objective decrease from 56.05 nm to 17.88 nm.
Abstract:
Recently, people have been paying more and more attention to mental health, such as depression, autism, and other common mental diseases. In order to achieve a mental disease diagnosis, intelligent methods have been actively studied. However, the existing models suffer the accuracy degradation caused by the clarity and occlusion of human faces in practical applications. This paper, thus, proposes a multi-scale feature fusion network that obtains feature information at three scales by locating the sentiment region in the image, and integrates global feature information and local feature information. In addition, a focal cross-entropy loss function is designed to improve the network's focus on difficult samples during training, enhance the training effect, and increase the model recognition accuracy. Experimental results on the challenging RAF_DB dataset show that the proposed model exhibits better facial expression recognition accuracy than existing techniques.
Recently, people have been paying more and more attention to mental health, such as depression, autism, and other common mental diseases. In order to achieve a mental disease diagnosis, intelligent methods have been actively studied. However, the existing models suffer the accuracy degradation caused by the clarity and occlusion of human faces in practical applications. This paper, thus, proposes a multi-scale feature fusion network that obtains feature information at three scales by locating the sentiment region in the image, and integrates global feature information and local feature information. In addition, a focal cross-entropy loss function is designed to improve the network's focus on difficult samples during training, enhance the training effect, and increase the model recognition accuracy. Experimental results on the challenging RAF_DB dataset show that the proposed model exhibits better facial expression recognition accuracy than existing techniques.
Abstract:
A local path optimization model and obstacle avoidance strategy based on Actor-Critic algorithm is proposed for the local obstacle avoidance problem of automatic guided vehicles in a complex workshop environment. In the complex working environment of the production workshop, we analyze the automatic obstacle avoidance problem of AGV trolley, establish the front and both sides of the AGV tentacle model and Markov decision process, and describe the local obstacle avoidance path in the form of virtual tentacles. And based on deep reinforcement learning to solve the path obstacle avoidance strategy, it is applied to the AGV self-navigation system. The dynamic obstacle avoidance performance of AGV is tested through simulation experiments, and the effectiveness of the proposed algorithm is verified by completing local obstacle avoidance path planning under global path guidance.
A local path optimization model and obstacle avoidance strategy based on Actor-Critic algorithm is proposed for the local obstacle avoidance problem of automatic guided vehicles in a complex workshop environment. In the complex working environment of the production workshop, we analyze the automatic obstacle avoidance problem of AGV trolley, establish the front and both sides of the AGV tentacle model and Markov decision process, and describe the local obstacle avoidance path in the form of virtual tentacles. And based on deep reinforcement learning to solve the path obstacle avoidance strategy, it is applied to the AGV self-navigation system. The dynamic obstacle avoidance performance of AGV is tested through simulation experiments, and the effectiveness of the proposed algorithm is verified by completing local obstacle avoidance path planning under global path guidance.
Abstract:
At present, the over-travel measurement of high-voltage circuit breakers is mostly carried out after the circuit breaker is completely dismantled. The disadvantage of the measurement is that it can only be measured when the circuit breaker is dismantled and overhauled. Defects are diagnosed in a timely and effective manner, and capacity causes hidden troubles. This article provides a method for direct measurement of hydraulic circuit breakers without any adjustment or disassembly. The method can be carried out in various situations such as minor repairs, major repairs, and temporary repairs, which not only saves time, but also improves accuracy of the measurement. The comparative test results show that this method can be simple, convenient and effective to measure the overtravel of the high-voltage circuit breaker.
At present, the over-travel measurement of high-voltage circuit breakers is mostly carried out after the circuit breaker is completely dismantled. The disadvantage of the measurement is that it can only be measured when the circuit breaker is dismantled and overhauled. Defects are diagnosed in a timely and effective manner, and capacity causes hidden troubles. This article provides a method for direct measurement of hydraulic circuit breakers without any adjustment or disassembly. The method can be carried out in various situations such as minor repairs, major repairs, and temporary repairs, which not only saves time, but also improves accuracy of the measurement. The comparative test results show that this method can be simple, convenient and effective to measure the overtravel of the high-voltage circuit breaker.
Abstract:
To address the problems of low detection accuracy and slow speed of traditional vision in the pharmaceutical industry, a YOLOv5s-EBD defect detection algorithm: Based on YOLOv5 network, firstly, the channel attention mechanism is introduced into the network to focus the network on defects similar to the pill background, reducing the time-consuming scanning of invalid backgrounds; the PANet module in the network is then replaced with BiFPN for differential fusion of different features; finally, Depth-wise separable convolution is used instead of standard convolution to achieve the output Finally, Depth-wise separable convolution is used instead of standard convolution to achieve the output feature map requirements of standard convolution with less number of parameters and computation, and improve detection speed. the improved model is able to detect all types of defects in tablets with an accuracy of over 94% and a detection speed of 123.8 fps, which is 4.27% higher than The unimproved YOLOv5 network model with 5.2 fps.
To address the problems of low detection accuracy and slow speed of traditional vision in the pharmaceutical industry, a YOLOv5s-EBD defect detection algorithm: Based on YOLOv5 network, firstly, the channel attention mechanism is introduced into the network to focus the network on defects similar to the pill background, reducing the time-consuming scanning of invalid backgrounds; the PANet module in the network is then replaced with BiFPN for differential fusion of different features; finally, Depth-wise separable convolution is used instead of standard convolution to achieve the output Finally, Depth-wise separable convolution is used instead of standard convolution to achieve the output feature map requirements of standard convolution with less number of parameters and computation, and improve detection speed. the improved model is able to detect all types of defects in tablets with an accuracy of over 94% and a detection speed of 123.8 fps, which is 4.27% higher than The unimproved YOLOv5 network model with 5.2 fps.
Abstract:
In order to improve the accuracy and efficiency of the measurement of the Shell Case size, a measurement system based on machine vision is introduced. Through the preprocessing, threshold segmentationSalgorithm and edge detection of the shell case image. Correlated size measurement of lines and circles in contour using binary search method and Hough transform. The experiment of detecting four sizes of 100 qualified shells shows that the system can realize the rapid and accurate detection of shell casings, and can be used to assess their quality performance. It effectively improves the detection efficiency and further improves the automation level of detection, which has a good application prospect.
In order to improve the accuracy and efficiency of the measurement of the Shell Case size, a measurement system based on machine vision is introduced. Through the preprocessing, threshold segmentationSalgorithm and edge detection of the shell case image. Correlated size measurement of lines and circles in contour using binary search method and Hough transform. The experiment of detecting four sizes of 100 qualified shells shows that the system can realize the rapid and accurate detection of shell casings, and can be used to assess their quality performance. It effectively improves the detection efficiency and further improves the automation level of detection, which has a good application prospect.
Abstract:
Automatic gauge control (AGC in the article) is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill. Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements. This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill. The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control, the relevant control strategy is provided to eliminate the adverse effects. At the same time, the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared, the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control. Finally, the output of controllers is sent to actuator though a constant rate smoothing. The proposed strategy is compared with conventional AGC control on Experimental platform and project application, the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.
Automatic gauge control (AGC in the article) is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill. Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements. This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill. The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control, the relevant control strategy is provided to eliminate the adverse effects. At the same time, the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared, the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control. Finally, the output of controllers is sent to actuator though a constant rate smoothing. The proposed strategy is compared with conventional AGC control on Experimental platform and project application, the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.
Abstract:
Proposed is a two-dimensional (2D) spectrum analysis system for acquiring the statistical information of radioactive particles on two dimensions, i.e. energy and time. Based on pulse width modulation readout circuit, such a system with 4-channels is designed, which converts the radiation signal into a rectangular pulse signal with pulse width modulated. The pulse width, occurrence time, and pulse count of the rectangular pulses are measured simultaneously with digital counters, so that the 2D spectra on energy and time of the radioactive particles can be obtained efficiently based on bi-parameter statistical analysis. A prototype of this 2D system is tested with gamma rays from 241Am isotopes, from which both the correlated 2D spectra and the independent spectra on energy and time are obtained. The energy spectra of four channels shows all characteristic peaks of 241Am gamma rays, among which the full-energy peak at 59.5keV exhibits energy resolution of about 5-6%, suggesting a good energy resolution and channel uniformity of the system. The regression of the time spectra of the characteristic peaks can give the time constants of each characteristic peak, revealing the time characteristics of the nuclear reactions in the radiative source.
Proposed is a two-dimensional (2D) spectrum analysis system for acquiring the statistical information of radioactive particles on two dimensions, i.e. energy and time. Based on pulse width modulation readout circuit, such a system with 4-channels is designed, which converts the radiation signal into a rectangular pulse signal with pulse width modulated. The pulse width, occurrence time, and pulse count of the rectangular pulses are measured simultaneously with digital counters, so that the 2D spectra on energy and time of the radioactive particles can be obtained efficiently based on bi-parameter statistical analysis. A prototype of this 2D system is tested with gamma rays from 241Am isotopes, from which both the correlated 2D spectra and the independent spectra on energy and time are obtained. The energy spectra of four channels shows all characteristic peaks of 241Am gamma rays, among which the full-energy peak at 59.5keV exhibits energy resolution of about 5-6%, suggesting a good energy resolution and channel uniformity of the system. The regression of the time spectra of the characteristic peaks can give the time constants of each characteristic peak, revealing the time characteristics of the nuclear reactions in the radiative source.
Abstract:
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics, low contrast and complex background texture. Firstly, by analyzing the spectral component distribution and spatial contour feature of the image, a salient feature model is established in spatial-frequency domain. Then, the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain. Finally, the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target, and the target is segmented by seeded region growing. Experiments have been performed on Berkeley Segmentation Data Set, as well as sample images of online detection, to verify the effectiveness of the algorithm. The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%, which indicates that the proposed algorithm can availably extract the target feature information, suppress the background texture and resist noise interference. Besides, the Hausdorff Distance of the segmentation is less than 10, which infers that the proposed algorithm obtains a high evaluation on the target contour preservation. The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics, low contrast and complex background texture. Firstly, by analyzing the spectral component distribution and spatial contour feature of the image, a salient feature model is established in spatial-frequency domain. Then, the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain. Finally, the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target, and the target is segmented by seeded region growing. Experiments have been performed on Berkeley Segmentation Data Set, as well as sample images of online detection, to verify the effectiveness of the algorithm. The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%, which indicates that the proposed algorithm can availably extract the target feature information, suppress the background texture and resist noise interference. Besides, the Hausdorff Distance of the segmentation is less than 10, which infers that the proposed algorithm obtains a high evaluation on the target contour preservation. The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
Abstract:
The country strongly supports the development of new energy industries, with the clean energy wind power industry developing rapidly and the market maturing, the scale of wind farms and installed capacity expanding, and the blade length increasing to 60-70m. The increased blade length and weight increase the probability of damage. the manual inspection method is time-consuming and laborious, with a high economic cost, low inspection efficiency, and high safety risks, and cannot meet the current wind turbine fast and efficient inspection requirements.This paper introduces the characteristics of the type of UAV, its working status, and mode, and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field, to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection. It is believed that with the development of UAV technology, UAVs will play a more important role in the field of inspection.
The country strongly supports the development of new energy industries, with the clean energy wind power industry developing rapidly and the market maturing, the scale of wind farms and installed capacity expanding, and the blade length increasing to 60-70m. The increased blade length and weight increase the probability of damage. the manual inspection method is time-consuming and laborious, with a high economic cost, low inspection efficiency, and high safety risks, and cannot meet the current wind turbine fast and efficient inspection requirements.This paper introduces the characteristics of the type of UAV, its working status, and mode, and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field, to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection. It is believed that with the development of UAV technology, UAVs will play a more important role in the field of inspection.
Abstract:
测试数据自动生成是实现自动化测试的关键步骤。大多数针对单元测试的自动化测试工具只提供测试用例的执行驱动，不能生成满足覆盖率要求的测试数据。本文提出了一种改进的鲸鱼遗传算法，用于生成单元测试MC/DC覆盖所需的测试数据。为避免遗传算法陷入迭代退化，引入精英保留策略；同时引入鲸鱼算法的变异阈值，平衡遗传算法的全局探索和局部开采能力，并根据当前的种群多样性动态调整该阈值，正向引导种群进化；最后，提出了一种改进的交叉策略来加速算法的收敛。将改进的鲸鱼遗传算法与遗传算法、鲸鱼算法、粒子群算法在两个基准程序上进行对比实验，结果表明改进算法生成测试数据的速度更快，覆盖率更高，评估次数更少，在生成MC/DC覆盖测试数据生成方面具有很大优势。
测试数据自动生成是实现自动化测试的关键步骤。大多数针对单元测试的自动化测试工具只提供测试用例的执行驱动，不能生成满足覆盖率要求的测试数据。本文提出了一种改进的鲸鱼遗传算法，用于生成单元测试MC/DC覆盖所需的测试数据。为避免遗传算法陷入迭代退化，引入精英保留策略；同时引入鲸鱼算法的变异阈值，平衡遗传算法的全局探索和局部开采能力，并根据当前的种群多样性动态调整该阈值，正向引导种群进化；最后，提出了一种改进的交叉策略来加速算法的收敛。将改进的鲸鱼遗传算法与遗传算法、鲸鱼算法、粒子群算法在两个基准程序上进行对比实验，结果表明改进算法生成测试数据的速度更快，覆盖率更高，评估次数更少，在生成MC/DC覆盖测试数据生成方面具有很大优势。
Abstract:
The laser device is the core component of coherent Doppler wind lidar. The peak power and pulse width of laser transmitting pulse have important effects on SNR. Based on coherent Doppler wind pulse lidar, the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis, and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar. But when the peak power is too large, the laser pulse may appear nonlinear phenomenon, which cause the damage of the laser. So the peak power must be less than the stimulated brillouin scattering power threshold. Increasing the pulse width can make the laser device to output more energy, but it will also make the spatial resolution lower, and the influence of turbulence on SNR will be greater. After a series of simulation analyses, it can be concluded that when the peak power is 650W and the pulse width is 340ns, the SNR of the system can be maximized. In addition, the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification. The experimental results are consistent with the theoretical analysis and simulation, which verifies the correctness of the theoretical analysis and simulation results. It provides theoretical basis and practical experience for the design of laser transmitting pulse in coherent Doppler wind lidar system.
The laser device is the core component of coherent Doppler wind lidar. The peak power and pulse width of laser transmitting pulse have important effects on SNR. Based on coherent Doppler wind pulse lidar, the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis, and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar. But when the peak power is too large, the laser pulse may appear nonlinear phenomenon, which cause the damage of the laser. So the peak power must be less than the stimulated brillouin scattering power threshold. Increasing the pulse width can make the laser device to output more energy, but it will also make the spatial resolution lower, and the influence of turbulence on SNR will be greater. After a series of simulation analyses, it can be concluded that when the peak power is 650W and the pulse width is 340ns, the SNR of the system can be maximized. In addition, the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification. The experimental results are consistent with the theoretical analysis and simulation, which verifies the correctness of the theoretical analysis and simulation results. It provides theoretical basis and practical experience for the design of laser transmitting pulse in coherent Doppler wind lidar system.
Abstract:
By designing a forklift-type AGV logistics trolley, in order to realize unmanned warehousing logistics. For the complex operation of traditional AGV forklifts, we directly use Android to control the movement of the vehicle, and innovatively introduce mobile phone control into the work control link of AGV, which greatly improves the work efficiency of AGV. In order to solve the problems of complex structure and difficult maintenance of AGVs in the current market, this paper selects ARDUINO UNO R3 as the main control chip, and improves the model to cope with the complex working environment. By installing various sensors on the car body, it can meet the requirements of field use. The designed experiment verifies the ultrasonic obstacle avoidance module, the infrared obstacle avoidance module and the overall performance test to verify that the vehicle can achieve the target function.
By designing a forklift-type AGV logistics trolley, in order to realize unmanned warehousing logistics. For the complex operation of traditional AGV forklifts, we directly use Android to control the movement of the vehicle, and innovatively introduce mobile phone control into the work control link of AGV, which greatly improves the work efficiency of AGV. In order to solve the problems of complex structure and difficult maintenance of AGVs in the current market, this paper selects ARDUINO UNO R3 as the main control chip, and improves the model to cope with the complex working environment. By installing various sensors on the car body, it can meet the requirements of field use. The designed experiment verifies the ultrasonic obstacle avoidance module, the infrared obstacle avoidance module and the overall performance test to verify that the vehicle can achieve the target function.
Abstract:
In this paper, we aim to propose a novel and effective iris segmentation method that is robust to uneven light intensity and different kinds of noises such as occlusion by light spots, eyelashes, eyelids, spectacle-frame, etc. Unlike previous methods, the proposed method makes full use of gray intensities of the iris image. Inspired by the matting algorithm, a premier assumption is made that the foreground and background images of the iris image are both locally smooth. According to the RST algorithm, trimaps are built to provide priori information. Under the assumption and priori, the optimal alpha matte can be obtained by least square loss function. A series of effective post processing methods are applied to the alpha image to obtain a more precise iris segmentation. The experiment on CASIA-iris-thousand database shows that the proposed method achieves a much better performance than conventional methods. The stability and validity of the proposed method is further demonstrated through.the complementary experiments on the challenging iris images.
In this paper, we aim to propose a novel and effective iris segmentation method that is robust to uneven light intensity and different kinds of noises such as occlusion by light spots, eyelashes, eyelids, spectacle-frame, etc. Unlike previous methods, the proposed method makes full use of gray intensities of the iris image. Inspired by the matting algorithm, a premier assumption is made that the foreground and background images of the iris image are both locally smooth. According to the RST algorithm, trimaps are built to provide priori information. Under the assumption and priori, the optimal alpha matte can be obtained by least square loss function. A series of effective post processing methods are applied to the alpha image to obtain a more precise iris segmentation. The experiment on CASIA-iris-thousand database shows that the proposed method achieves a much better performance than conventional methods. The stability and validity of the proposed method is further demonstrated through.the complementary experiments on the challenging iris images.
Abstract:
Magnetic field measurement plays an extremely important role in material science, electronic engineering, power system and even industrial fields. In particular, magnetic field measurement provides a safe and reliable tool for industrial non-destructive testing. The sensitivity of magnetic field measurement determines the highest level of detection. The diamond nitrogen-vacancy (NV) color center is a new type of quantum sensor developed in recent years. The external magnetic field will cause Zeeman splitting of the ground state energy level of the diamond NV color center. Optical detection magnetic resonance (ODMR), using a microwave source and a lock-in amplifier to detect the resonant frequency of the NV color center, and finally the change of the resonant frequency can accurately calculate the size of the external magnetic field and the sensitivity of the external magnetic field change. In the experiment, a diamond containing a high concentration of NV color centers is coupled with an optical fiber to realize the preparation of a magnetic field scanning probe. Then, the surface cracks of the magnetized iron plate weld are scanned, and the scanning results are drawn into a two-dimensional magnetic force distribution map , according to the magnetic field gradient change of the magnetic force distribution map, the position and size of the crack can be judged very accurately, which provides a very effective diagnostic tool for industrial safety.
Magnetic field measurement plays an extremely important role in material science, electronic engineering, power system and even industrial fields. In particular, magnetic field measurement provides a safe and reliable tool for industrial non-destructive testing. The sensitivity of magnetic field measurement determines the highest level of detection. The diamond nitrogen-vacancy (NV) color center is a new type of quantum sensor developed in recent years. The external magnetic field will cause Zeeman splitting of the ground state energy level of the diamond NV color center. Optical detection magnetic resonance (ODMR), using a microwave source and a lock-in amplifier to detect the resonant frequency of the NV color center, and finally the change of the resonant frequency can accurately calculate the size of the external magnetic field and the sensitivity of the external magnetic field change. In the experiment, a diamond containing a high concentration of NV color centers is coupled with an optical fiber to realize the preparation of a magnetic field scanning probe. Then, the surface cracks of the magnetized iron plate weld are scanned, and the scanning results are drawn into a two-dimensional magnetic force distribution map , according to the magnetic field gradient change of the magnetic force distribution map, the position and size of the crack can be judged very accurately, which provides a very effective diagnostic tool for industrial safety.
Abstract:
To improve intelligent vehicle drive performance and avoid vehicle’s side-slip during target path tracking, a linearized four wheel vehicle model is adopted as a predictive control model, and an intelligent vehicle target path tracking method based on competitive-cooperative game is proposed. By calculating the affecting factors of the design variables to objective functions and fuzzy clustering, the design variables are divided into different strategic spaces owned by each player. Based on competitive-cooperative game model, each game player takes its payoff as mono-objective to optimize its own strategic spaces and obtains the best strategy to deal with the others. All the best strategies are combined as a game strategy set. Considering the front wheel angle and side-slip angle increment constraint, tire side-slip angle and tire side-slip deflection dynamics, it took the path tracking state model as objective function, and validated the calculation by competitive-cooperative game theory. The results show the effectiveness of the algorithm. The experimental results show that this method can track the intelligent vehicle quickly and steadily, and has good real-time performance.
To improve intelligent vehicle drive performance and avoid vehicle’s side-slip during target path tracking, a linearized four wheel vehicle model is adopted as a predictive control model, and an intelligent vehicle target path tracking method based on competitive-cooperative game is proposed. By calculating the affecting factors of the design variables to objective functions and fuzzy clustering, the design variables are divided into different strategic spaces owned by each player. Based on competitive-cooperative game model, each game player takes its payoff as mono-objective to optimize its own strategic spaces and obtains the best strategy to deal with the others. All the best strategies are combined as a game strategy set. Considering the front wheel angle and side-slip angle increment constraint, tire side-slip angle and tire side-slip deflection dynamics, it took the path tracking state model as objective function, and validated the calculation by competitive-cooperative game theory. The results show the effectiveness of the algorithm. The experimental results show that this method can track the intelligent vehicle quickly and steadily, and has good real-time performance.
Abstract:
Aiming at the hand-in-eye calibration of underwater welding robot visual servo tracking system, the traditional calibration method is difficult to achieve due to the shortcomings of underwater environment, such as light refraction, difficulty in manual target placement, water interference and so on, and the existing calibration method that have the problems of complex process, difficult operation and low positioning accuracy. Therefore, a nonlinear calibration method of underwater active vision based on single reference point is proposed. In this method, firstly, the camera carried by the robot which should be precisely controlled to do more than five times of translation and more than two times of rotation respectively, and the image of a fixed reference point in the scene should be taken, and the camera parameters and hand-eye relationship should be calibrated by using the linear camera model; then, the nonlinear equations should be established according to the homography matrix of the two images, and the accurate camera parameters and hand-eye relationship can be optimized. Finally, the experimental platform of binocular stereo vision system for underwater welding robot is built, and the underwater hand-eye calibration and application experiments are carried out. The experimental results show that the underwater active vision calibration method is easy to select a single reference point, easy to operate, and the algorithm is simple. The positioning accuracy meets the requirements of welding accuracy, which verifies the effectiveness of the algorithm, and provides conditions for underwater welding robot visual servo automatic tracking weld.
Aiming at the hand-in-eye calibration of underwater welding robot visual servo tracking system, the traditional calibration method is difficult to achieve due to the shortcomings of underwater environment, such as light refraction, difficulty in manual target placement, water interference and so on, and the existing calibration method that have the problems of complex process, difficult operation and low positioning accuracy. Therefore, a nonlinear calibration method of underwater active vision based on single reference point is proposed. In this method, firstly, the camera carried by the robot which should be precisely controlled to do more than five times of translation and more than two times of rotation respectively, and the image of a fixed reference point in the scene should be taken, and the camera parameters and hand-eye relationship should be calibrated by using the linear camera model; then, the nonlinear equations should be established according to the homography matrix of the two images, and the accurate camera parameters and hand-eye relationship can be optimized. Finally, the experimental platform of binocular stereo vision system for underwater welding robot is built, and the underwater hand-eye calibration and application experiments are carried out. The experimental results show that the underwater active vision calibration method is easy to select a single reference point, easy to operate, and the algorithm is simple. The positioning accuracy meets the requirements of welding accuracy, which verifies the effectiveness of the algorithm, and provides conditions for underwater welding robot visual servo automatic tracking weld.
Abstract:
With the development of human–computer interaction technology, brain–computer interface (BCI) has been widely used in medical, entertainment, military, and other fields. Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements. Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination. Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features. This study proposes a classification method of imagined speech Electroencephalogram (EEG) signals with discrete wavelet transform (DWT) and support vector machine (SVM). An open dataset that consists of 15 subjects imagining speaking six different words, namely, up, down, left, right, backward, and forward, is used. The objective is to improve the classification accuracy of imagined speech BCI system. The features of EEG signals are first extracted by DWT, and the imagined words are classified by SVM with the above features. Experimental results show that the proposed method achieves an average accuracy of 61.69%, which is better than those of existing methods for classifying imagined speech tasks.
With the development of human–computer interaction technology, brain–computer interface (BCI) has been widely used in medical, entertainment, military, and other fields. Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements. Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination. Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features. This study proposes a classification method of imagined speech Electroencephalogram (EEG) signals with discrete wavelet transform (DWT) and support vector machine (SVM). An open dataset that consists of 15 subjects imagining speaking six different words, namely, up, down, left, right, backward, and forward, is used. The objective is to improve the classification accuracy of imagined speech BCI system. The features of EEG signals are first extracted by DWT, and the imagined words are classified by SVM with the above features. Experimental results show that the proposed method achieves an average accuracy of 61.69%, which is better than those of existing methods for classifying imagined speech tasks.
Abstract:
The wireless power transmission system based on nonlinear parity time symmetry is a robust system that can maintain high-efficiency transmission at a certain distance. Parity-Time Symmetry (PT symmetry) wireless power transfer system, due to its insensitivity to the position of the coupled resonant coil over a large range, can carry out constant power transfer to the load, and through coupled mode theory The PT symmetrical wireless power transmission circuit with S-P structure is analyzed, and the system has different transmission efficiencies in different coupling intervals, and the transmission effect of the structure at different distances is studied with the change of coupling coefficient. . Then, the simulation is carried out by matlab and origin software. The final results show that the transmission efficiency does not change with the coupling coefficient in the strong coupling region and can maintain high-efficiency transmission. In the weak coupling region, the coupling coefficient has a great influence on the transmission efficiency of the system.
The wireless power transmission system based on nonlinear parity time symmetry is a robust system that can maintain high-efficiency transmission at a certain distance. Parity-Time Symmetry (PT symmetry) wireless power transfer system, due to its insensitivity to the position of the coupled resonant coil over a large range, can carry out constant power transfer to the load, and through coupled mode theory The PT symmetrical wireless power transmission circuit with S-P structure is analyzed, and the system has different transmission efficiencies in different coupling intervals, and the transmission effect of the structure at different distances is studied with the change of coupling coefficient. . Then, the simulation is carried out by matlab and origin software. The final results show that the transmission efficiency does not change with the coupling coefficient in the strong coupling region and can maintain high-efficiency transmission. In the weak coupling region, the coupling coefficient has a great influence on the transmission efficiency of the system.
Abstract:
Building extraction from high resolution remote sensing image is a key technology of digital city construction. In order to solve the problems of low efficiency and low precision of traditional remote sensing image segmentation, an improved U-NET network structure is adopted in this paper. Firstly, in order to extract efficient building characteristic information, FPN structure was introduced to improve the ability of integrating multi-scale information in U-NET model; Secondly, to solve the problem that feature information weakens with the deepening of network depth, an efficient residual block network is introduced; Finally, In order to better distinguish the target area and background area in the image and improve the precision of building target edge detection, the cross entropy loss and Dice loss were linearly combined and weighted. Experimental results show that the algorithm can improve the image segmentation effect and improve the image accuracy by 18%.
Building extraction from high resolution remote sensing image is a key technology of digital city construction. In order to solve the problems of low efficiency and low precision of traditional remote sensing image segmentation, an improved U-NET network structure is adopted in this paper. Firstly, in order to extract efficient building characteristic information, FPN structure was introduced to improve the ability of integrating multi-scale information in U-NET model; Secondly, to solve the problem that feature information weakens with the deepening of network depth, an efficient residual block network is introduced; Finally, In order to better distinguish the target area and background area in the image and improve the precision of building target edge detection, the cross entropy loss and Dice loss were linearly combined and weighted. Experimental results show that the algorithm can improve the image segmentation effect and improve the image accuracy by 18%.
Abstract:
In the background of “double carbon,”Svigorously developing new energy is particularly important. Wind power is an important clean energy source. In the field of new energy, wind power scale is also expanding. With the wind turbine, the probability of large-scale blade damage is also increasing. Because the large wind turbine blade crack detection cost is high and because of the poor working environment, this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image processing. The application of weighted averages to achieve grayscale processing, followed by median filtering to achieve image noise reduction, and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images, which enhances the image by limiting the contrast adaptive histogram equalization algorithm to make the details at the target area and defects more clear and complete, and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from theSdefects through image foreground segmentation, threshold processing, and framing by the connected domain. The validity and accuracy of the proposed method in leaf detection were verified by experiments.
In the background of “double carbon,”Svigorously developing new energy is particularly important. Wind power is an important clean energy source. In the field of new energy, wind power scale is also expanding. With the wind turbine, the probability of large-scale blade damage is also increasing. Because the large wind turbine blade crack detection cost is high and because of the poor working environment, this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image processing. The application of weighted averages to achieve grayscale processing, followed by median filtering to achieve image noise reduction, and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images, which enhances the image by limiting the contrast adaptive histogram equalization algorithm to make the details at the target area and defects more clear and complete, and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from theSdefects through image foreground segmentation, threshold processing, and framing by the connected domain. The validity and accuracy of the proposed method in leaf detection were verified by experiments.
Abstract:
According to recent research statistics, approximately 30% of people who experienced falls are over the age of 65. Therefore, it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs. In this paper, a fall detection model based on improved human posture estimation algorithm is proposed. The improved human posture estimation algorithm is implemented on the basis of Openpose. An improved strategy based on depthwise separable convolution combined with HDC structure is proposed. The depthwise separable convolution is used to replace the convolution neural network structure, which makes the network lightweight and reduces the redundant layer in the network. At the same time, in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points, HDC structure is introduced. Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection. Then, human posture estimation is applied to fall detection research, and fall event modeling is carried out through fall feature extraction. The designed convolution neural network model is used to classify and distinguish falls. The experimental results show that our method achieves 98.53%, 97.71% and 97.20% accuracy on three public fall detection data sets. Compared with the experimental results of other methods on the same data set, the model designed in this paper has a certain improvement in system accuracy. The sensitivity is also improved, which will reduce the error detection probability of the system. In addition, this paper also verifies the real-time performance of the model. Even if researchers are experimenting with low-level hardware, it can ensure a certain detection speed without too much delay.
According to recent research statistics, approximately 30% of people who experienced falls are over the age of 65. Therefore, it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs. In this paper, a fall detection model based on improved human posture estimation algorithm is proposed. The improved human posture estimation algorithm is implemented on the basis of Openpose. An improved strategy based on depthwise separable convolution combined with HDC structure is proposed. The depthwise separable convolution is used to replace the convolution neural network structure, which makes the network lightweight and reduces the redundant layer in the network. At the same time, in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points, HDC structure is introduced. Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection. Then, human posture estimation is applied to fall detection research, and fall event modeling is carried out through fall feature extraction. The designed convolution neural network model is used to classify and distinguish falls. The experimental results show that our method achieves 98.53%, 97.71% and 97.20% accuracy on three public fall detection data sets. Compared with the experimental results of other methods on the same data set, the model designed in this paper has a certain improvement in system accuracy. The sensitivity is also improved, which will reduce the error detection probability of the system. In addition, this paper also verifies the real-time performance of the model. Even if researchers are experimenting with low-level hardware, it can ensure a certain detection speed without too much delay.
Abstract:
SinceIn order to improve the detection accuracy of chaotic small signal prediction models under the background of sea clutter, a distributed sea clutter denoising algorithm is proposed, on the basis of variational modal decomposition (VMD). The sea clutter signal is decomposed into variational modal functions (VMF) with different center bandwidths by means of VMD. By analyzing the autocorrelation characteristics of the decomposed signal, we perform instantaneous half-period (IHP) and wavelet threshold denoising processing on the high-frequency and low-frequency components respectively, and regain the sea clutter signals. Based on LSSVM sea clutter prediction model, this research compares and analyzes the denoising effects of VMD. Experiment results show that, , the RMSE after denoising is reduced by two orders of magnitude, apporoximating 0.00034，with an apparently better denoising effect，compared with the root mean square error (RMSE) of the prediction before denoising.
SinceIn order to improve the detection accuracy of chaotic small signal prediction models under the background of sea clutter, a distributed sea clutter denoising algorithm is proposed, on the basis of variational modal decomposition (VMD). The sea clutter signal is decomposed into variational modal functions (VMF) with different center bandwidths by means of VMD. By analyzing the autocorrelation characteristics of the decomposed signal, we perform instantaneous half-period (IHP) and wavelet threshold denoising processing on the high-frequency and low-frequency components respectively, and regain the sea clutter signals. Based on LSSVM sea clutter prediction model, this research compares and analyzes the denoising effects of VMD. Experiment results show that, , the RMSE after denoising is reduced by two orders of magnitude, apporoximating 0.00034，with an apparently better denoising effect，compared with the root mean square error (RMSE) of the prediction before denoising.
Display Method:
2022,9(2):1-12, DOI: 10.15878/j.cnki.instrumentation.2022.02.001
Abstract:
The automatic generation of test data is a key step in realizing automated testing. Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements. This paper presents an improved Whale Genetic Algorithm for generating test data required for unit testing MC/DC coverage. The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation. At the same time, the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic algorithm. The threshold is dynamically adjusted according to the diversity and evolution stage of current population, which positively guides the evolution of the population. Finally, an improved crossover strategy is proposed to accelerate the convergence of the algorithm. The improved whale genetic algorithm is compared with genetic algorithm, whale algorithm and particle swarm algorithm on two benchmark programs. The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations, and has great advantages in generating test data.
The automatic generation of test data is a key step in realizing automated testing. Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements. This paper presents an improved Whale Genetic Algorithm for generating test data required for unit testing MC/DC coverage. The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation. At the same time, the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic algorithm. The threshold is dynamically adjusted according to the diversity and evolution stage of current population, which positively guides the evolution of the population. Finally, an improved crossover strategy is proposed to accelerate the convergence of the algorithm. The improved whale genetic algorithm is compared with genetic algorithm, whale algorithm and particle swarm algorithm on two benchmark programs. The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations, and has great advantages in generating test data.
2022,9(2):13-25, DOI: 10.15878/j.cnki.instrumentation.2022.02.006
Abstract:
Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications. Biometric security, medical rehabilitation, virtual reality, and autonomous driving cars are some of the fields of study that rely on accurate gait recognition. While majority of studies have been focused on achieving very high recognition performance on a specific dataset, different issues arise in the real-world applications of this technology. This research is one of the first to evaluate the effects of changing walking speeds and directions on gait recognition rates under various walking conditions. Dataset was collected using the KINECT sensor. To draw an overall conclusion about the effects of walking speed and direction to the sensor, we define distance features and angle features. Furthermore, we propose two feature fusion methods for person recognition. Results of the study provide insights into how walking speeds and walking directions to the KINECT sensor influence the accuracy of gait recognition.
Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications. Biometric security, medical rehabilitation, virtual reality, and autonomous driving cars are some of the fields of study that rely on accurate gait recognition. While majority of studies have been focused on achieving very high recognition performance on a specific dataset, different issues arise in the real-world applications of this technology. This research is one of the first to evaluate the effects of changing walking speeds and directions on gait recognition rates under various walking conditions. Dataset was collected using the KINECT sensor. To draw an overall conclusion about the effects of walking speed and direction to the sensor, we define distance features and angle features. Furthermore, we propose two feature fusion methods for person recognition. Results of the study provide insights into how walking speeds and walking directions to the KINECT sensor influence the accuracy of gait recognition.
2022,9(2):26-32, DOI: 10.15878/j.cnki.instrumentation.2022.02.002
Abstract:
The laser device is the core component of coherent Doppler wind lidar. The peak power and pulse width of laser transmitting pulse have important effects on SNR. Based on coherent Doppler wind pulse lidar, the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis, and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar. But when the peak power is too large, the laser pulse may appear nonlinear phenomenon, which cause the damage of the laser. So, the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy, but it will also make the spatial resolution lower, and the influence of turbulence on SNR will be greater. After a series of simulation analyses, it can be concluded that when the peak power is 650W and the pulse width is 340ns, the SNR of the system can be maximized. In addition, the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification. The experimental results are consistent with the theoretical analysis and simulation, which verifies the correctness of the theoretical analysis and simulation results. It provides theoretical basis and practical experience for the design of laser transmitting pulse in coherent Doppler wind lidar system.
The laser device is the core component of coherent Doppler wind lidar. The peak power and pulse width of laser transmitting pulse have important effects on SNR. Based on coherent Doppler wind pulse lidar, the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis, and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar. But when the peak power is too large, the laser pulse may appear nonlinear phenomenon, which cause the damage of the laser. So, the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy, but it will also make the spatial resolution lower, and the influence of turbulence on SNR will be greater. After a series of simulation analyses, it can be concluded that when the peak power is 650W and the pulse width is 340ns, the SNR of the system can be maximized. In addition, the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification. The experimental results are consistent with the theoretical analysis and simulation, which verifies the correctness of the theoretical analysis and simulation results. It provides theoretical basis and practical experience for the design of laser transmitting pulse in coherent Doppler wind lidar system.
2022,9(2):33-46, DOI: 10.15878/j.cnki.instrumentation.2022.02.005
Abstract:
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics, low contrast and complex background texture. Firstly, by analyzing the spectral com-ponent distribution and spatial contour feature of the image, a salient feature model is established in spatial-frequency domain. Then, the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain. Finally, the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target, and the target is segmented by seeded region growing. Experiments have been performed on Berkeley Segmentation Data Set, as well as sample images of online detection, to verify the effectiveness of the algorithm. The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%, which indicates that the proposed algorithm can availably extract the target feature information, suppress the background texture and resist noise interference. Besides, the Hausdorff Distance of the segmentation is less than 10, which infers that the proposed algorithm obtains a high evaluation on the target contour preservation. The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics, low contrast and complex background texture. Firstly, by analyzing the spectral com-ponent distribution and spatial contour feature of the image, a salient feature model is established in spatial-frequency domain. Then, the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain. Finally, the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target, and the target is segmented by seeded region growing. Experiments have been performed on Berkeley Segmentation Data Set, as well as sample images of online detection, to verify the effectiveness of the algorithm. The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%, which indicates that the proposed algorithm can availably extract the target feature information, suppress the background texture and resist noise interference. Besides, the Hausdorff Distance of the segmentation is less than 10, which infers that the proposed algorithm obtains a high evaluation on the target contour preservation. The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
2022,9(2):47-55, DOI: 10.15878/j.cnki.instrumentation.2022.02.003
Abstract:
The country strongly supports the development of new energy industries, with the clean energy wind power industry developing rapidly and the market maturing, the scale of wind farms and installed capacity expanding, and the blade length increasing to 60-70m. The increased blade length and weight increase the probability of damage. the manual inspection method is time-consuming and laborious, with a high economic cost, low inspection efficiency, and high safety risks, and cannot meet the current wind turbine fast and efficient inspection requirements. This paper intro-duces the characteristics of the type of UAV, its working status, and mode, and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field, to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection. It is believed that with the development of UAV technology, UAVs will play a more important role in the field of in-spection.
The country strongly supports the development of new energy industries, with the clean energy wind power industry developing rapidly and the market maturing, the scale of wind farms and installed capacity expanding, and the blade length increasing to 60-70m. The increased blade length and weight increase the probability of damage. the manual inspection method is time-consuming and laborious, with a high economic cost, low inspection efficiency, and high safety risks, and cannot meet the current wind turbine fast and efficient inspection requirements. This paper intro-duces the characteristics of the type of UAV, its working status, and mode, and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field, to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection. It is believed that with the development of UAV technology, UAVs will play a more important role in the field of in-spection.
2022,9(2):56-63, DOI: 10.15878/j.cnki.instrumentation.2022.02.004
Abstract:
With the development of human–computer interaction technology, brain–computer interface (BCI) has been widely used in medical, entertainment, military, and other fields. Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements. Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination. Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features. This study proposes a classification method of imagined speech Electroencephalogram (EEG) signals with discrete wavelet transform (DWT) and support vector machine (SVM). An open dataset that consists of 15 subjects imagining speaking six different words, namely, up, down, left, right, backward, and forward, is used. The objective is to improve the classification accuracy of imagined speech BCI system. The features of EEG signals are first extracted by DWT, and the imagined words are classified by SVM with the above features. Experimental results show that the proposed method achieves an average accuracy of 61.69%, which is better than those of existing methods for classifying imagined speech tasks.
With the development of human–computer interaction technology, brain–computer interface (BCI) has been widely used in medical, entertainment, military, and other fields. Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements. Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination. Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features. This study proposes a classification method of imagined speech Electroencephalogram (EEG) signals with discrete wavelet transform (DWT) and support vector machine (SVM). An open dataset that consists of 15 subjects imagining speaking six different words, namely, up, down, left, right, backward, and forward, is used. The objective is to improve the classification accuracy of imagined speech BCI system. The features of EEG signals are first extracted by DWT, and the imagined words are classified by SVM with the above features. Experimental results show that the proposed method achieves an average accuracy of 61.69%, which is better than those of existing methods for classifying imagined speech tasks.
Display Method：
Display Method:
Abstract:
Aiming at the problem of pedestrian bridge vibration measurement, a vibration measurement system of pedestrian bridge with dual magnetic suspension vibrator structure was designed according to absolute vibration measurement principle. The relationship between the magnetic repulsion force of vibrator and its displacement was obtained by the experimental method and the least square fitting method. The vibration equations of two magnetic suspension vibrators were deduced respectively, and the measurement sensitivity of the system was deduced. The amplitude-frequency characteristic of the system was studied. A simulation model of vibrator measurement system with double magnetic suspension vibrator was established. The analysis shows that the sensitivity of the vibration measurement system with double magnetic suspension vibrator is higher than that with single magnetic suspension vibrator. The four vibration waveforms were measured, that is, no one passes through a pedestrian bridge, there are cars running under the pedestrian bridge, single pedestrian passes through the pedestrian bridge and multiple pedestrians pass through the pedestrian bridge. The multi-scale one-dimensional wavelet decomposition function was used to analyze the vibration signals. The vibration characteristics were obtained using one dimension wavelet decomposition function under four different conditions. Finally, the vibration waveforms of four cases were reconstructed. The measured results show that the vibration measurement system of pedestrian bridge with double magnetic suspension vibrator structure has high measurement sensitivity. The design has a certain value to monitor a pedestrian bridge.
Aiming at the problem of pedestrian bridge vibration measurement, a vibration measurement system of pedestrian bridge with dual magnetic suspension vibrator structure was designed according to absolute vibration measurement principle. The relationship between the magnetic repulsion force of vibrator and its displacement was obtained by the experimental method and the least square fitting method. The vibration equations of two magnetic suspension vibrators were deduced respectively, and the measurement sensitivity of the system was deduced. The amplitude-frequency characteristic of the system was studied. A simulation model of vibrator measurement system with double magnetic suspension vibrator was established. The analysis shows that the sensitivity of the vibration measurement system with double magnetic suspension vibrator is higher than that with single magnetic suspension vibrator. The four vibration waveforms were measured, that is, no one passes through a pedestrian bridge, there are cars running under the pedestrian bridge, single pedestrian passes through the pedestrian bridge and multiple pedestrians pass through the pedestrian bridge. The multi-scale one-dimensional wavelet decomposition function was used to analyze the vibration signals. The vibration characteristics were obtained using one dimension wavelet decomposition function under four different conditions. Finally, the vibration waveforms of four cases were reconstructed. The measured results show that the vibration measurement system of pedestrian bridge with double magnetic suspension vibrator structure has high measurement sensitivity. The design has a certain value to monitor a pedestrian bridge.
Abstract:
Crack of conductive component is one of the biggest threats to daily production. In order to detect the crack on conductive component, the pulsed eddy current thermography models were built according to different materials with the cracks based on finite element method (FEM) simulation. The influence of the induction heating temperature distribution with the different defect depths were simulated for the carbon fiber reinforced plastic (CFRP) materials and general metal materials. The grey value of image sequence was extracted to analyze its relationship with the depth of crack. Simulative and experimental results show that in the carbon fiber reinforced composite materials, the bigger depth of the crack is, the larger temperature rise of the crack during the heating phase is; and the bigger depth of the crack is, the faster the cooling rate of the crack during the cooling phase is. In general metal materials, the smaller depth of the crack is, the lager temperature rise of the crack during the heating phase is; and the smaller depth of the crack is, the faster the cooling rate of crack during the cooling phase is.
Crack of conductive component is one of the biggest threats to daily production. In order to detect the crack on conductive component, the pulsed eddy current thermography models were built according to different materials with the cracks based on finite element method (FEM) simulation. The influence of the induction heating temperature distribution with the different defect depths were simulated for the carbon fiber reinforced plastic (CFRP) materials and general metal materials. The grey value of image sequence was extracted to analyze its relationship with the depth of crack. Simulative and experimental results show that in the carbon fiber reinforced composite materials, the bigger depth of the crack is, the larger temperature rise of the crack during the heating phase is; and the bigger depth of the crack is, the faster the cooling rate of the crack during the cooling phase is. In general metal materials, the smaller depth of the crack is, the lager temperature rise of the crack during the heating phase is; and the smaller depth of the crack is, the faster the cooling rate of crack during the cooling phase is.
Abstract:
A novel phase-locked loop (PLL) -based closed-loop driving circuit with ultra-low-noise trans-impedance amplifier (TIA) is proposed. The TIA is optimized to achieve ultra-low input-referred current noise. To track drive-mode resonant frequency and reduce frequency jitter of actuation voltage, a PLL-based driving technique is adopted. Implemented on printed circuit board (PCB), the proposed driving loop has successfully excited MEMS element into resonance, with a settling time of 3s. The stable frequency and amplitude of TIA output voltage are 10.14KHz and 800mVPP, respectively. With sense-channel electronics, the gyroscope exhibits a scale factor of 0.04mV/°/s and a bias instability of 57.6°/h, which demonstrates the feasibility of the proposed driving circuit.
A novel phase-locked loop (PLL) -based closed-loop driving circuit with ultra-low-noise trans-impedance amplifier (TIA) is proposed. The TIA is optimized to achieve ultra-low input-referred current noise. To track drive-mode resonant frequency and reduce frequency jitter of actuation voltage, a PLL-based driving technique is adopted. Implemented on printed circuit board (PCB), the proposed driving loop has successfully excited MEMS element into resonance, with a settling time of 3s. The stable frequency and amplitude of TIA output voltage are 10.14KHz and 800mVPP, respectively. With sense-channel electronics, the gyroscope exhibits a scale factor of 0.04mV/°/s and a bias instability of 57.6°/h, which demonstrates the feasibility of the proposed driving circuit.
Abstract:
The contamination proposed in this paper is a defect on the surface of ice cream bar, which is a serious security threat. So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed, which is based on fuzzy rule and absolute neighborhood feature. Firstly, the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last, the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent, which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper, which is of actual use value.
The contamination proposed in this paper is a defect on the surface of ice cream bar, which is a serious security threat. So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed, which is based on fuzzy rule and absolute neighborhood feature. Firstly, the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last, the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent, which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper, which is of actual use value.
2022,9(1):12-22, DOI: 10.15878/j.cnki.instrumentation.2022.01.003
Abstract:
In this paper, we aim to propose a novel and effective iris segmentation method that is robust to uneven light intensity and different kinds of noises such as occlusion by light spots, eyelashes, eyelids, spectacle-frame, etc. Unlike previous methods, the proposed method makes full use of gray intensities of the iris image. Inspired by the matting algorithm, a premier assumption is made that the foreground and background images of the iris image are both locally smooth. According to the RST algorithm, trimaps are built to provide priori information. Under the assumption and priori, the optimal alpha matte can be obtained by least square loss function. A series of effective post processing methods are applied to the alpha image to obtain a more precise iris segmentation. The experiment on CASIA-iris-thousand database shows that the proposed method achieves a much better performance than conventional methods. Our experimental results achieve 20.5% and 26.4%, more than the well-known integro-differential operator and edge detection combined with Hough transform on iris segmentation rate respectively. The stability and validity of the proposed method is further demonstrated through the complementary experiments on the challenging iris images.
In this paper, we aim to propose a novel and effective iris segmentation method that is robust to uneven light intensity and different kinds of noises such as occlusion by light spots, eyelashes, eyelids, spectacle-frame, etc. Unlike previous methods, the proposed method makes full use of gray intensities of the iris image. Inspired by the matting algorithm, a premier assumption is made that the foreground and background images of the iris image are both locally smooth. According to the RST algorithm, trimaps are built to provide priori information. Under the assumption and priori, the optimal alpha matte can be obtained by least square loss function. A series of effective post processing methods are applied to the alpha image to obtain a more precise iris segmentation. The experiment on CASIA-iris-thousand database shows that the proposed method achieves a much better performance than conventional methods. Our experimental results achieve 20.5% and 26.4%, more than the well-known integro-differential operator and edge detection combined with Hough transform on iris segmentation rate respectively. The stability and validity of the proposed method is further demonstrated through the complementary experiments on the challenging iris images.
Abstract:
Resonant temperature sensors have drawn considerable attention for their advantages such as high sensitivity, digitized signal output and high precision. This paper presents a new type of resonant temperature sensor, which uses capacitive micromachined ultrasonic transducer (CMUT) as the sensing element. A lumped electro-mechanical-thermal model was established to show its working principle for temperature measurement. The theoretical model explicitly explains the thermally induced changes in the resonant frequency of the CMUT. Then, the finite element method was used to further investigate the sensing performance. The numerical results agree well with the established analytical model qualitatively. The numerical results show that the resonant frequency varies linearly with the temperature over the range of 20 ℃ to 140℃ at the first four vibrating modes. However, the first order vibrating mode shows a higher sensitivity than the other three higher modes. When working at the first order vibrating mode, the temperature coefficient of the resonance frequency (TCf) can reach as high as -1114.3 ppm/℃ at a bias voltage equal to 90% of the collapse voltage of the MCUT. The corresponding nonlinear error was as low as 1.18%. It is discovered that the sensing sensitivity is dependent on the applied bias voltages. A higher sensitivity can be achieved by increasing the bias voltages.
Resonant temperature sensors have drawn considerable attention for their advantages such as high sensitivity, digitized signal output and high precision. This paper presents a new type of resonant temperature sensor, which uses capacitive micromachined ultrasonic transducer (CMUT) as the sensing element. A lumped electro-mechanical-thermal model was established to show its working principle for temperature measurement. The theoretical model explicitly explains the thermally induced changes in the resonant frequency of the CMUT. Then, the finite element method was used to further investigate the sensing performance. The numerical results agree well with the established analytical model qualitatively. The numerical results show that the resonant frequency varies linearly with the temperature over the range of 20 ℃ to 140℃ at the first four vibrating modes. However, the first order vibrating mode shows a higher sensitivity than the other three higher modes. When working at the first order vibrating mode, the temperature coefficient of the resonance frequency (TCf) can reach as high as -1114.3 ppm/℃ at a bias voltage equal to 90% of the collapse voltage of the MCUT. The corresponding nonlinear error was as low as 1.18%. It is discovered that the sensing sensitivity is dependent on the applied bias voltages. A higher sensitivity can be achieved by increasing the bias voltages.
2022,9(1):23-30, DOI: 10.15878/j.cnki.instrumentation.2022.01.002
Abstract:
For the hand-eye calibration of the vision service robot, the traditional hand-eye calibration technology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose in-formation of the service robot in real time. In this paper, a hand-eye calibration method based on ROS (Robot Operating System) is proposed. In this method, ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times. Meanwhile, the head camera of the service robot takes images of a fixed point in the scene. Then, the nonlinear equations were established according to the homography matrix of the two images and the position and pose information of the ROS system, and the accurate hand-eye relationship was optimized by the least square method. Finally, an experimental platform is built and the proposed hand-eye calibration method is verified. The experiment results show that the method is easy to operate, simple algorithm and correct result, which verifies the ef-fectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.
For the hand-eye calibration of the vision service robot, the traditional hand-eye calibration technology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose in-formation of the service robot in real time. In this paper, a hand-eye calibration method based on ROS (Robot Operating System) is proposed. In this method, ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times. Meanwhile, the head camera of the service robot takes images of a fixed point in the scene. Then, the nonlinear equations were established according to the homography matrix of the two images and the position and pose information of the ROS system, and the accurate hand-eye relationship was optimized by the least square method. Finally, an experimental platform is built and the proposed hand-eye calibration method is verified. The experiment results show that the method is easy to operate, simple algorithm and correct result, which verifies the ef-fectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.
2022,9(1):41-48, DOI: 10.15878/j.cnki.instrumentation.2022.01.004
Abstract:
In the background of “double carbon,” vigorously developing new energy is particularly important. Wind power is an important clean energy source. In the field of new energy, wind power scale is also expanding. With the wind turbine, the probability of large-scale blade damage is also increasing. Because the large wind turbine blade crack detection cost is high and because of the poor working environment, this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image processing. The application of weighted averages to achieve grayscale processing, followed by median filtering to achieve image noise reduction, and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images, which enhances the image by limiting the contrast adaptive histogram equalization algorithm to make the details at the target area and defects more clear and complete, and improves the detection efficiency. The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation, threshold processing, and framing by the connected domain. The validity and accuracy of the proposed method in leaf detection were verified by experiments.
In the background of “double carbon,” vigorously developing new energy is particularly important. Wind power is an important clean energy source. In the field of new energy, wind power scale is also expanding. With the wind turbine, the probability of large-scale blade damage is also increasing. Because the large wind turbine blade crack detection cost is high and because of the poor working environment, this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image processing. The application of weighted averages to achieve grayscale processing, followed by median filtering to achieve image noise reduction, and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images, which enhances the image by limiting the contrast adaptive histogram equalization algorithm to make the details at the target area and defects more clear and complete, and improves the detection efficiency. The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation, threshold processing, and framing by the connected domain. The validity and accuracy of the proposed method in leaf detection were verified by experiments.
2022,9(1):31-40, DOI: 10.15878/j.cnki.instrumentation.2022.01.007
Abstract:
To improve intelligent vehicle drive performance and avoid vehicle side-slip during target path tracking, a linearized four-wheel vehicle model is adopted as a predictive control model, and an intelligent vehicle target path tracking method based on a competitive cooperative game is proposed. The design variables are divided into different strategic spaces owned by each player by calculating the affecting factors of the design variables with objective functions and fuzzy clustering. Based on the competitive cooperative game model, each game player takes its payoff as a mono-objective to optimize its own strategic space and obtain the best strategy to deal with others. The best strategies were combined into the game strategy set. Considering the front wheel angle and side slip angle increment constraint, tire side-slip angle, and tire side slip deflection dynamics, it took the path tracking state model was used as the objective, function and the calculation was validated by competitive cooperative game theory. The results demonstrated the effectiveness of the proposed algorithm. The experimental results show that this method can track an intelligent vehicle quickly and steadily and has good real-time performance.
To improve intelligent vehicle drive performance and avoid vehicle side-slip during target path tracking, a linearized four-wheel vehicle model is adopted as a predictive control model, and an intelligent vehicle target path tracking method based on a competitive cooperative game is proposed. The design variables are divided into different strategic spaces owned by each player by calculating the affecting factors of the design variables with objective functions and fuzzy clustering. Based on the competitive cooperative game model, each game player takes its payoff as a mono-objective to optimize its own strategic space and obtain the best strategy to deal with others. The best strategies were combined into the game strategy set. Considering the front wheel angle and side slip angle increment constraint, tire side-slip angle, and tire side slip deflection dynamics, it took the path tracking state model was used as the objective, function and the calculation was validated by competitive cooperative game theory. The results demonstrated the effectiveness of the proposed algorithm. The experimental results show that this method can track an intelligent vehicle quickly and steadily and has good real-time performance.
2022,9(1):56-64, DOI: 10.15878/j.cnki.instrumentation.2022.01.006
Abstract:
Magnetic field measurement plays an extremely important role in material science, electronic engineering, power system and even industrial fields. In particular, magnetic field measurement provides a safe and reliable tool for in-dustrial non-destructive testing. The sensitivity of magnetic field measurement determines the highest level of detec-tion. The diamond nitrogen-vacancy (NV) color center is a new type of quantum sensor developed in recent years. The external magnetic field will cause Zeeman splitting of the ground state energy level of the diamond NV color center. Optical detection magnetic resonance (ODMR), using a microwave source and a lock-in amplifier to detect the resonant frequency of the NV color center, and finally the change of the resonant frequency can accurately calcu-late the size of the external magnetic field and the sensitivity of the external magnetic field change. In the experiment, a diamond containing a high concentration of NV color centers is coupled with an optical fiber to realize the prepara-tion of a magnetic field scanning probe. Then, the surface cracks of the magnetized iron plate weld are scanned, and the scanning results are drawn into a two-dimensional magnetic force distribution map, according to the magnetic field gradient change of the magnetic force distribution map, the position and size of the crack can be judged very accurately, which provides a very effective diagnostic tool for industrial safety.
Magnetic field measurement plays an extremely important role in material science, electronic engineering, power system and even industrial fields. In particular, magnetic field measurement provides a safe and reliable tool for in-dustrial non-destructive testing. The sensitivity of magnetic field measurement determines the highest level of detec-tion. The diamond nitrogen-vacancy (NV) color center is a new type of quantum sensor developed in recent years. The external magnetic field will cause Zeeman splitting of the ground state energy level of the diamond NV color center. Optical detection magnetic resonance (ODMR), using a microwave source and a lock-in amplifier to detect the resonant frequency of the NV color center, and finally the change of the resonant frequency can accurately calcu-late the size of the external magnetic field and the sensitivity of the external magnetic field change. In the experiment, a diamond containing a high concentration of NV color centers is coupled with an optical fiber to realize the prepara-tion of a magnetic field scanning probe. Then, the surface cracks of the magnetized iron plate weld are scanned, and the scanning results are drawn into a two-dimensional magnetic force distribution map, according to the magnetic field gradient change of the magnetic force distribution map, the position and size of the crack can be judged very accurately, which provides a very effective diagnostic tool for industrial safety.
Abstract:
Surface acoustic wave (SAW) resonator used as wireless sensor was characterized and the parameters of its MBVD ((Modified Butterworth-Van Dyke) model were extracted versus temperature. The extracted parameters lead to evaluate the resonator performances in terms of Temperature coefficient of frequency (TCF) and quality factor (Q). An antenna was then associated with the SAW resonator and the entire system has been characterized and modeled. The good agreement experiment-simulation allows to define the optimum operating conditions of the wireless sensor.
Surface acoustic wave (SAW) resonator used as wireless sensor was characterized and the parameters of its MBVD ((Modified Butterworth-Van Dyke) model were extracted versus temperature. The extracted parameters lead to evaluate the resonator performances in terms of Temperature coefficient of frequency (TCF) and quality factor (Q). An antenna was then associated with the SAW resonator and the entire system has been characterized and modeled. The good agreement experiment-simulation allows to define the optimum operating conditions of the wireless sensor.
Abstract:
According to the problem that the selection of traditional PID control parameters is too complicated in evaporator of Organic Rankine Cycle system (ORC), an evaporator PID controller based on BP neural network optimization is designed. Based on the control theory, the model of ORC evaporator is set up. The BP algorithm is used to control the , and parameters of the evaporator PID controller, so that the evaporator temperature can reach the optimal state quickly and steadily. The MATLAB software is used to simulate the traditional PID controller and the BP neural network PID controller. The experimental results show that the , and parameters of the BP neural network PID controller are 0.5677, 0.2970, and 0.1353, respectively. Therefore, the evaporator PID controller based on BP neural network optimization not only satisfies the requirements of the system performance, but also has better control parameters than the traditional PID controller.
According to the problem that the selection of traditional PID control parameters is too complicated in evaporator of Organic Rankine Cycle system (ORC), an evaporator PID controller based on BP neural network optimization is designed. Based on the control theory, the model of ORC evaporator is set up. The BP algorithm is used to control the , and parameters of the evaporator PID controller, so that the evaporator temperature can reach the optimal state quickly and steadily. The MATLAB software is used to simulate the traditional PID controller and the BP neural network PID controller. The experimental results show that the , and parameters of the BP neural network PID controller are 0.5677, 0.2970, and 0.1353, respectively. Therefore, the evaporator PID controller based on BP neural network optimization not only satisfies the requirements of the system performance, but also has better control parameters than the traditional PID controller.
Abstract:
Using the temperature compensation and structure optimization design technology, developed the TBQ-2-B type standard pyranometer on the original pyranometer basis, its stability is better than 2%, reached the international standard ISO 9060 and the World Meteorological Organization (WMO) instruments and methods of observation Committee (CIMO) on the first level pyranometer request. Over the years, comparing with our national solar radiation standard (absolute cavity radiometer), its performance is very stable. As a working standard pyranometer, it has been used for more than twenty years in the field of metrological calibration of meteorological radiation instruments.
Using the temperature compensation and structure optimization design technology, developed the TBQ-2-B type standard pyranometer on the original pyranometer basis, its stability is better than 2%, reached the international standard ISO 9060 and the World Meteorological Organization (WMO) instruments and methods of observation Committee (CIMO) on the first level pyranometer request. Over the years, comparing with our national solar radiation standard (absolute cavity radiometer), its performance is very stable. As a working standard pyranometer, it has been used for more than twenty years in the field of metrological calibration of meteorological radiation instruments.
Abstract:
Abstract: This manuscript briefly summarizes the development trends and recent research focus of the star tracker. And the relevant technologies about dynamic performance of the star tracker are analyzed and discussed. These can provide reference for the star tracker and attitude measurement device researchers.
Abstract: This manuscript briefly summarizes the development trends and recent research focus of the star tracker. And the relevant technologies about dynamic performance of the star tracker are analyzed and discussed. These can provide reference for the star tracker and attitude measurement device researchers.
Abstract:
Solar thermal and photovoltaic applications are the most widely used and the most successful way of commercial development in solar energy applications. Observation and assessment of solar thermal and photovoltaic resources are the basis and key of their large-scale development and utilization. Using the observational data carried out from Beijing southern suburbs observation station of China Meteorological Administration in summer of 2009, preliminary solar thermal and photovoltaic resources characteristics for different weather conditions, different angle and different directions are analyzed. The results show that: (1) In sunny, cloudy or rainy weather conditions, both of solar thermal and photovoltaic sensors daily irradiance have consistent change in trend. Solar thermal irradiance is larger than photovoltaic. Under sunny conditions, solar thermal global radiation has about 2.7% higher than the photovoltaic global radiation. Under cloudy weather conditions, solar thermal global radiation has about 3.9% higher than the photovoltaic. Under rainy weather conditions, solar thermal global radiation has about 20% higher than the photovoltaic. (2) For different inclined plane daily global radiation, southern latitude -15 °incline is the maximum and southern vertical surface is the minimum. The order from large to small is southern latitude-15 ° incline, southern latitude incline, southern latitude+15 °incline, horizontal surface and southern vertical surface. Southern latitude -15 °incline global radiation has about 41% higher than the southern vertical surface. (3) For different orientation vertical surface daily global radiation, southern vertical surface is the maximum and western vertical surface is the minimum, which eastern vertical surface is in the middle. Southern vertical surface global radiation has about 20% higher than the western vertical surface.
Solar thermal and photovoltaic applications are the most widely used and the most successful way of commercial development in solar energy applications. Observation and assessment of solar thermal and photovoltaic resources are the basis and key of their large-scale development and utilization. Using the observational data carried out from Beijing southern suburbs observation station of China Meteorological Administration in summer of 2009, preliminary solar thermal and photovoltaic resources characteristics for different weather conditions, different angle and different directions are analyzed. The results show that: (1) In sunny, cloudy or rainy weather conditions, both of solar thermal and photovoltaic sensors daily irradiance have consistent change in trend. Solar thermal irradiance is larger than photovoltaic. Under sunny conditions, solar thermal global radiation has about 2.7% higher than the photovoltaic global radiation. Under cloudy weather conditions, solar thermal global radiation has about 3.9% higher than the photovoltaic. Under rainy weather conditions, solar thermal global radiation has about 20% higher than the photovoltaic. (2) For different inclined plane daily global radiation, southern latitude -15 °incline is the maximum and southern vertical surface is the minimum. The order from large to small is southern latitude-15 ° incline, southern latitude incline, southern latitude+15 °incline, horizontal surface and southern vertical surface. Southern latitude -15 °incline global radiation has about 41% higher than the southern vertical surface. (3) For different orientation vertical surface daily global radiation, southern vertical surface is the maximum and western vertical surface is the minimum, which eastern vertical surface is in the middle. Southern vertical surface global radiation has about 20% higher than the western vertical surface.
2021,8(1):61-73, DOI: 10.15878/j.cnki.instrumentation.2021.01.006
Abstract:
To keep coal workers away from the hazardous area with frequent accidents such as the roof fall and rib spalling in an underground coalmine, we put forward the solution with robotized self-moving anchor-supporting unit. The existing research shows that the surrounding rock of the roadway has self-stability, and the early or late support is not conducive to the safe and reliable support of the roadway, so there is a problem of support opportunity. In order to study the supporting effect and the optimal supporting time of the above solution, we established the mechanical coupling model of surrounding rock and advance support, and investigated the surrounding rock deformation and advance support pressure distribution under different reserved roof subsidence by using the numerical simulation software FLAC3D. The results show that the deformation of surrounding rock increases and finally tends to a stable level with the increase of pre settlement of roadway roof, and when the pre settlement of roof is between 8-15mm, the vertical pressure of the top beam of advance support reaches the minimum value, about 0.58MPa. Based on the above research, we put forward the optimum supporting time in roadway excavation, and summarized the evaluation method based on the mechanical coupling model of surrounding rock-advance support.
To keep coal workers away from the hazardous area with frequent accidents such as the roof fall and rib spalling in an underground coalmine, we put forward the solution with robotized self-moving anchor-supporting unit. The existing research shows that the surrounding rock of the roadway has self-stability, and the early or late support is not conducive to the safe and reliable support of the roadway, so there is a problem of support opportunity. In order to study the supporting effect and the optimal supporting time of the above solution, we established the mechanical coupling model of surrounding rock and advance support, and investigated the surrounding rock deformation and advance support pressure distribution under different reserved roof subsidence by using the numerical simulation software FLAC3D. The results show that the deformation of surrounding rock increases and finally tends to a stable level with the increase of pre settlement of roadway roof, and when the pre settlement of roof is between 8-15mm, the vertical pressure of the top beam of advance support reaches the minimum value, about 0.58MPa. Based on the above research, we put forward the optimum supporting time in roadway excavation, and summarized the evaluation method based on the mechanical coupling model of surrounding rock-advance support.
2021,8(1):29-42, DOI: 10.15878/j.cnki.instrumentation.2021.01.003
Abstract:
In the process of crowd movement, pedestrians are often affected by their neighbors. In order to describe the consistency of adjacent individuals and collectivity of a group, this paper learns from the rules of the flocking behavior, such as segregation, alignment and cohesion, and proposes a method for crowd motion simulation based on the Boids model and social force model. Firstly, the perception area of individuals is divided into zone of segregation, alignment and cohesion. Secondly, the interactive force among individuals is calculated based upon the zone information, velocity vector and the group information. The interactive force among individuals is the synthesis of three forces: the repulsion force to avoid collisions, the alignment force to keep consistent with the velocity direction, and the attractive force to get close to the members of group. In segregation and alignment areas, the repulsion force and alignment force among pedestrians are limited by visual field factors. Finally, the interactive force among individuals, the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion. The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.
In the process of crowd movement, pedestrians are often affected by their neighbors. In order to describe the consistency of adjacent individuals and collectivity of a group, this paper learns from the rules of the flocking behavior, such as segregation, alignment and cohesion, and proposes a method for crowd motion simulation based on the Boids model and social force model. Firstly, the perception area of individuals is divided into zone of segregation, alignment and cohesion. Secondly, the interactive force among individuals is calculated based upon the zone information, velocity vector and the group information. The interactive force among individuals is the synthesis of three forces: the repulsion force to avoid collisions, the alignment force to keep consistent with the velocity direction, and the attractive force to get close to the members of group. In segregation and alignment areas, the repulsion force and alignment force among pedestrians are limited by visual field factors. Finally, the interactive force among individuals, the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion. The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.
P.W.S.I. Wijethunga, I.A. Chandrawansa, B.M.D.T. Rathnayake, W.A.N.I.Harischandra, W.M.M.T.S. Weerakoon, , B.G.L.T. Samaranayake
2021,8(1):1-13, DOI: 10.15878/j.cnki.instrumentation.2021.01.001
Abstract:
A supportive mobile robot for assisting the elderly is an emerging requirement mainly in countries like Japan where population ageing become relevant in near future. Falls related injuries are considered as a critical issue when taking into account the physical health of older people. A personal assistive robot with the capability of picking up and carrying objects for long/short distances can be used to overcome or lessen this problem. Here, we design and introduce a 3D dynamic simulation of such an assistive robot to perform pick and place of objects through visual recognition. The robot consists of two major components; a robotic arm or manipulator to do the pick and place, and an omnidirectional wheeled robotic platform to support mobility. Both components are designed and operated according to their kinematics and dynamics and the controllers are integrated for the combined performance. The objective was to improve the ac-curacy of the robot at a considerably high speed. Designed mobile manipulator has been successfully tested and sim-ulated with a stereo vision system to perform object recognition and tracking in a virtual environment resembling aroom of an elderly care. The tracking accuracy of the mobile manipulator at an average speed of 0.5m/s is 90% and is well suited for the proposed application.
A supportive mobile robot for assisting the elderly is an emerging requirement mainly in countries like Japan where population ageing become relevant in near future. Falls related injuries are considered as a critical issue when taking into account the physical health of older people. A personal assistive robot with the capability of picking up and carrying objects for long/short distances can be used to overcome or lessen this problem. Here, we design and introduce a 3D dynamic simulation of such an assistive robot to perform pick and place of objects through visual recognition. The robot consists of two major components; a robotic arm or manipulator to do the pick and place, and an omnidirectional wheeled robotic platform to support mobility. Both components are designed and operated according to their kinematics and dynamics and the controllers are integrated for the combined performance. The objective was to improve the ac-curacy of the robot at a considerably high speed. Designed mobile manipulator has been successfully tested and sim-ulated with a stereo vision system to perform object recognition and tracking in a virtual environment resembling aroom of an elderly care. The tracking accuracy of the mobile manipulator at an average speed of 0.5m/s is 90% and is well suited for the proposed application.
Abstract:
Abstract: Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years. This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process, simple structure and freedom in substrate choices. This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials. Recent progresses in this ferroelectric memory field, hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems. In this paper, a general review of this type of ferroelectric non-volatile memory is provided, which include the device structure, organic ferroelectric materials, electrical characteristics and working principles. We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.
Abstract: Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years. This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process, simple structure and freedom in substrate choices. This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials. Recent progresses in this ferroelectric memory field, hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems. In this paper, a general review of this type of ferroelectric non-volatile memory is provided, which include the device structure, organic ferroelectric materials, electrical characteristics and working principles. We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.
Abstract:
As an important and necessary part in the intelligent battery management systems (BMS), the prognostics and remaining useful life (RUL) estimation for lithium-ion batteries attach more and more attractions. Especially, the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status, that makes these methods flexible and applicable in actual lithium-ion battery applications. At first, the related concepts and definitions are introduced. And the degradation parameters identification and extraction is presented, as the health indicator and the foundation of RUL prediction for the lithium-ion batteries. Then, data-driven methods used for lithium-ion battery RUL estimation are summarized, in which several statistical and machine learning algorithms are involved. Finally, the future trend for battery prognostics and RUL estimation are forecasted.
As an important and necessary part in the intelligent battery management systems (BMS), the prognostics and remaining useful life (RUL) estimation for lithium-ion batteries attach more and more attractions. Especially, the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status, that makes these methods flexible and applicable in actual lithium-ion battery applications. At first, the related concepts and definitions are introduced. And the degradation parameters identification and extraction is presented, as the health indicator and the foundation of RUL prediction for the lithium-ion batteries. Then, data-driven methods used for lithium-ion battery RUL estimation are summarized, in which several statistical and machine learning algorithms are involved. Finally, the future trend for battery prognostics and RUL estimation are forecasted.
Journal information
- Competent Authority：
China Association for Science and Technology (CAST)
- Sponsor：
China Instrument and Control Society
Popular Science Press - Editorin-Chief：
YOU Zheng
- CN：
10-1206/TH
- ISSN：
2095-7521
- Index：
CNKI