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Integrated Test System for Large-aperture Telescopes Based on Astrophotonics Interconnections

Qichang An, Kun Wang, Xinyue Liu, Hongwen Li, Jiakang Zhu

2024(1):1-9, DOI: https://doi.org/10.15878/j.instr.202300161

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Abstract:
This study aims to improve the integrated testing of large-aperture telescopes to clarify the fundamental principles of an integrated testing system based on astrophotonics. Our demonstration and analyses focused on element-position sensing and modulation based on spatial near-geometric beams, high-throughput step-difference measurements based on channel spectroscopy, distributed broadband-transmittance testing, and standard spectral tests based on near-field energy regulation. Comprehensive analyses and experiments were conducted to confirm the feasibility of the proposed system in the integrated testing process of large-aperture telescopes. The results demonstrated that the angular resolution of the light rays exceeded 5 arcsec, which satisfies the requirements for component-position detection in future large-aperture telescopes. The measurement resolution of the wavefront tilt was better than 0.45 µrad. Based on the channel spectral method—which combined a high signal-to-noise ratio and high sensitivity, along with continuous-spectral digital segmentation and narrowband-spectral physical segmentation—a resolution of 0.050 μm and a range of 50 μm were obtained. After calibration, the measurement resolution of the pupil deviation improved to exceed 4% accuracy, and the transmission measurements achieved a consistency of over 2% accuracy. Regarding fringe-broadband interferometry measurements, the system maintained high stability, ensuring its operation within the coherence length, and robustly detected the energy without unwrapping the phase. The use of a projector for calibrating broadband-spectrum measurements led to a reduction in contrast from 0.8142 to 0.6038, which further validates the system's applicability in the integrated testing process of large-aperture telescopes. This study greatly enhanced the observational capabilities of large-aperture telescopes while reducing the integrated system's volume, weight, and power consumption.

Measurement Uncertainty Analysis of the Rotary-scan Method for the Measurable Dimension of Cylindrical Workpieces

Jiali Zhao, Liang Zhang, Dan Wu, Bobo Shen, Qiaolin Li

2024(1):10-17, DOI: https://doi.org/10.15878/j.instr.202300133

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Abstract:
The measurement uncertainty analysis is carried out to investigate the measurable dimensions of cylindrical workpieces by the rotary-scan method in this paper. Due to the difficult alignment of the workpiece with a diameter of less than 3 mm by the rotary scan method, the measurement uncertainty of the cylindrical workpiece with a diameter of 3 mm and length of 50 mm which is measured by a roundness measuring machine, is evaluated according to GUM (Guide to the Expression of Uncertainty in Measurement) as an example. Since the uncertainty caused by the eccentricity of the measured workpiece is different with the dimension changing, the measurement uncertainty of cylindrical workpieces with other dimensions can be evaluated the same as the diameter of 3 mm but with different eccentricity. Measurement uncertainty caused by different eccentricities concerning the dimension of the measured cylindrical workpiece is set to simulate the evaluations. Compared to the target value of the measurement uncertainty of 0.1μm, the measurable dimensions of the cylindrical workpiece can be obtained. Experiments and analysis are presented to quantitatively evaluate the reliability of the rotary-scan method for the roundness measurement of cylindrical workpieces.

Study on Sealing Characteristics of Sliding Seal Assembly of Aircraft Hydraulic Actuator

Weinan Li, Saixin Shi, Hongxia Tang1, Liang Chen, Jiawei Zhang, Hao Tang, Jianhua Zhao

2024(1):18-29, DOI: https://doi.org/10.15878/j.instr.202300146

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Abstract:
The hydraulic actuator, known as the "muscle" of military aircraft, is responsible for flight attitude adjustment, trajectory control, braking turn, landing gear retracting and other actions, which directly affect its flight efficiency and safety. However, the sealing assembly often has the situation of over-aberrant aperture fit clearance or critical over-aberrant clearance, which increases the failure probability and degree of movable seal failure, and directly affects the flight efficiency and safety of military aircraft. In this paper, the simulation model of hydraulic actuator seal combination is established by ANSYS software, and the sealing principle is described. The change curve of contact width and contact pressure of combination seal under the action of high-pressure fluid is drawn. The effects of different oil pressure, fit clearance and other parameters on the sealing performance are analyzed. Finally, the accelerated life test of sliding seal components is carried out on the hydraulic actuator accelerated life test rig, and the surface morphology is compared and analyzed. The research shows that the O-ring is the main sealing element and the role of the check ring is to protect and support the O-ring to prevent damage caused by squeezing into the fit clearance, so the check ring bears a large load and is prone to shear failure. Excessive fit clearance is the main factor affecting the damage of the check ring, and the damage parts are mainly concentrated at the edge of the sealing surface. This paper provides a theoretical basis for the design of hydraulic actuator and the improvement of sealing performance.

Research on Detection of Food additives Based on Terahertz Spectroscopy and Analytic Hierarchy Process

Miaoyu Zhao, Fang Yan, Wenwen Li, Yangshuo Liu

2024(1):30-37, DOI: https://doi.org/10.15878/j.instr.202300148

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Abstract:
Terahertz time-domain spectroscopy is a kind of far-infrared spectroscopy technology, and its spectrum reflects the internal properties of substances with rich physical and chemical information, so the use of terahertz waves can be used to qualitatively identify food additives containing nitrogen elements. Analytic hierarchy process (AHP) was originally used to solve evaluation-type problems, and this paper introduces it into the field of terahertz spectral qualitative analysis, proposes a terahertz time-domain spectral qualitative identification method combined with analytic hierarchy process, and verifies the feasibility of the method by taking four common food additives (xylitol, L-alanine, sorbic acid, and benzoic acid) and two illegal additives (melamine, and Sudan Red No. I) as the objects of study. Firstly, the collected terahertz time-domain spectral data were pre-processed and transformed into a data set consisting of peaks, peak positions, peak numbers and overall trends; then, the data were divided into comparison and test sets, and a qualitative additive identification model incorporating analytic hierarchy process was constructed and parameter optimisation was performed. The results showed that the qualitative identification accuracies of additives based on single factors, i.e., overall trend, peak value, peak position, and peak number, were 80.23%, 70.93%, 67.44%, and 40.70%, respectively, whereas the identification accuracy of the analytic hierarchy process qualitative identification method based on multi-factors could be improved to 92.44%. In addition, the fuzzy characterisation of the absorption spectrum data was binarised in the data pre-processing stage and used as the base data for the overall trend, and the recognition accuracy was improved to 94.19% by combining the fuzzy characterisation method of such data with the hierarchical analysis qualitative recognition model. The results show that it is feasible to use terahertz technology to identify different varieties of additives, and this paper constructs a hierarchical analytical qualitative model with better effect, which provides a new means for food additives detection, and the method is simple in steps, with a small demand for samples, which is suitable for the rapid detection of small samples.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Haitao Wang, Xiang Liu

2024(1):38-50, DOI: https://doi.org/10.15878/j.instr.202300151

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Abstract:
Aiming at the problems of low efficiency, poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery, a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model (WDMACN) and Gram Angle Product field (GAPF) was proposed. Firstly, the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series. Secondly, the residual network is used to extract data features, and the features of the target domain without labels are pseudo-labeled, and the transferable features among the feature extractors are shared through the depth parameter, and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish. The model t through adversarial domain adaptation, thus achieving fault classification. Finally, a large number of validations were carried out on the bearing data set of Case Western Reserve University (CWRU) and the gear data. The results show that the proposed method can greatly improve the diagnostic efficiency of the model, and has good noise resistance and generalization.

Performance Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on CEEMDAN-KPCA and DA-GRU Networks

Tingwei Zhao, Juan Wang, Jiangxuan Che, Yingjie Bian, Tianyu Chen

2024(1):51-61, DOI: https://doi.org/10.15878/j.instr.202300155

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Abstract:
In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell (PEMFC), a fusion prediction method (CKDG) based on adaptive noise complete ensemble empirical mode decomposition (CEEMDAN), kernel principal component analysis (KPCA) and dual attention mechanism gated recurrent unit neural network (DA-GRU) was proposed. CEEMDAN and KPCA were used to extract the input feature data sequence, reduce the influence of random factors, and capture essential feature components to reduce the model complexity. The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately. The actual aging experimental data verify the performance of the CKDG method. The results show that under the steady-state condition of 20% training data prediction, the CKDA method can reduce the root mean square error (RMSE) by 52.7% and 34.6%, respectively, compared with the traditional LSTM and GRU neural networks. Compared with the simple DA-GRU network, RMSE is reduced by 15%, and the degree of over-fitting is reduced, which has higher accuracy. It also shows excellent prediction performance under the dynamic condition data set and has good universality.

Improved Algorithm for Efficient Extraction of Relaxation Parameter Values from Wideband Permittivity of Baijiu

Haoyan Yu, Qi Jin, Zhaozong Meng, Zhen Li

2024(1):62-69, DOI: https://doi.org/10.15878/j.instr.202300156

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Abstract:
The complex permittivity of baijiu varies with frequency, and dielectric spectroscopy has been used to evaluate the quality. To simplify the analysis and reduce the number of the parameters, a dielectric relaxation model is often used to fit the permittivity data. However, existing fitting methods such as the least squares and particle swarm optimization methods are often computationally complex and require preset initial values. Therefore, a simpler calculation method of the relaxation parameters considering the geometric characteristics of the permittivity spectrum is proposed. It is based on the relationship between the Cole-Cole relaxation parameters and the Cole-Cole diagram, which is fitted by a geometric method. First, the concepts of the Cole-Cole parameters and the diagram are introduced, and then the process of obtaining the parameters from the complex permittivity measurement data is explained. Taking baijiu with 56% alcohol by volume (ABV) as an example, the fitting is better than the least squares method and similar to the particle swarm optimization. This method is then used for the parameter fitting of baijiu with ABV of 42-52%, and the average error is less than 1%, demonstrating its wider applicability. Finally, a prediction model is used for baijiu with 53% ABV, and the error is only 1.51%. Hence, the method can be applied to the measurement of ABV of baijiu.

Effect of Marine Planktonic Algal Particles on the Communication Performance of Underwater Quantum Link

Xiuzai Zhang, Yujie Ge, AMITAVE Saha

2024(1):70-78, DOI: https://doi.org/10.15878/j.instr.202300158

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Abstract:
As one of the main application directions of quantum technology, underwater quantum communication is of great research significance. In order to study the influence of marine planktonic algal particles on the communication performance of underwater quantum links, based on the extinction characteristics of marine planktonic algal particles, the influence of changes in the chlorophyll concentration and particle number density of planktonic algal particles on the attenuation of underwater links is explored respectively, the influence of marine planktonic algal particles on the fidelity of underwater quantum links, the generation rate of the security key, and the utilization rate of the channel is analyzed, and simulation experiments are carried out. The results show that with the increase in chlorophyll concentration and particle density of aquatic planktonic algal particles, quantum communication channel link attenuation shows a gradually increasing trend. In addition, the security key generation rate, channel fidelity and utilization rate are gradually decreasing. Therefore, the performance of underwater quantum communication channel will be interfered by marine planktonic algal particles, and it is necessary to adjust the relevant parameter values in the quantum communication system according to different marine planktonic algal particle number density and chlorophyll concentration to improve the performance of quantum communication.

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Fangyi Xing, Cheng Xu, Yanming Wu, Hongwei Gao1

2024(1):79-87, DOI: https://doi.org/10.15878/j.instr.202300160

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Abstract:
Reconfigurable modular robots feature high mobility due to their unconstrained connection manners. Inspired by the snake multi-joint crawling principle, a chain-type reconfigurable modular robot (CRMR) is designed, which could reassemble into various configurations through the compound joint motion. Moreover, an illumination adaptive modular robot identification (IAMRI) algorithm is proposed for CRMR. At first, an adaptive threshold is applied to detect oriented FAST features in the robot image. Then, the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method. After matching features, an improved random sample consensus algorithm is employed to eliminate the mismatched features. Finally, the reconfigurable robot module is identified effectively through the perspective transformation. Compared with ORB, MA, Y-ORB, and S-ORB algorithms, the IAMRI algorithm has an improvement of over 11.6% in feature uniformity, and 13.7% in the comprehensive indicator, respectively. The IAMRI algorithm limits the relative error within 2.5 pixels, efficiently completing the CRMR identification under complex environmental changes.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

Anjiang Cai, Yangfan Yu, Manman Zhao

2024(1):88-100, DOI: https://doi.org/10.15878/j.instr.202300165

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Abstract:
To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning, a deep reinforcement learning framework considering sparse reward problem is proposed. The job shop scheduling problem is transformed into Markov decision process, and six state features are designed to improve the state feature representation by using two-way scheduling method, including four state features that distinguish the optimal action and two state features that are related to the learning goal. An extended variant of graph isomorphic network GIN++ is used to encode disjunction graphs to improve the performance and generalization ability of the model. Through iterative greedy algorithm, random strategy is generated as the initial strategy, and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm. Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules, the proposed method reduces the minimum average gap by 3.49%, 5.31% and 4.16%, respectively, compared with the priority rule-based method, and 5.34% compared with the learning-based method. 11.97% and 5.02%, effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

Model Prediction and Optimal Control of Gas Oxygen Content for A Municipal Solid Waste Incineration Process

Aijun Yan, Tingting Gu

2024(1):101-111, DOI: https://doi.org/10.15878/j.instr.202400025

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Abstract:
In the municipal solid waste incineration process, it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience. To address this problem, this paper proposes an optimization control method of gas oxygen content based on model predictive control. First, a stochastic configuration network is utilized to establish a prediction model of gas oxygen content. Second, an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow. Finally, model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions. The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value, which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Jianjun Zhuang, Xiaohui Wu, Dongdong Meng, Shenghua Jing

2024(1):112-120, DOI: https://doi.org/10.15878/j.instr.202400058

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Abstract:
Breast cancer has become a killer of women's health nowadays. In order to exploit the potential representational capabilities of the models more comprehensively, we propose a multi-model fusion strategy. Specifically, we combine two differently structured deep learning models, ResNet101 and Swin Transformer (SwinT), with the addition of the Convolutional Block Attention Module (CBAM) attention mechanism, which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability, and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets. The multi-classification recognition accuracies of the proposed fusion model under 40X, 100X, 200X and 400X BreakHis datasets are 97.50%, 96.60%, 96.30 and 96.10%, respectively. Compared with a single SwinT model and ResNet101 model, the fusion model has higher accuracy and better generalization ability, which provides a more effective method for screening, diagnosis and pathological classification of female breast cancer.

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Fault Detection and Diagnosis of Pneumatic Control Valve Based on a Hybrid Deep Learning Model

HAO Hongtao, WANG Kai

2023(4):12-26, DOI: 10.15878/j.cnki.instrumentation.20231127.001

[Abstract] (90) [HTML] (0) [PDF 11.21 M] (8716)

Abstract:
As the growing requirements for the stability and safety of process industries, the fault detection and diagnosis of pneumatic control valves have crucial practical significance. Many of the approaches were presented in the literature to diagnose faults through the comparison of residual sequences with thresholds. In this study, a novel hybrid neural network model has been developed to address the issue of pneumatic control valve fault diag-nosis. First, the feature extractor automatically extracts in-depth features of the signals through multi-scale convolutional neural networks with different kernel sizes, which not only adequately explores the local dis-tinguishable features, but also takes into account the global features. The extracted features are then fused by the feature fusion layer to reduce redundant features. Finally, the long short-term memory for fault identification and the dense layer for fault classification. Experimental results demonstrate that the average test accuracy is above 94% and 16 out of the 19 conditions can be successfully detected in the simulated actual industrial en-vironment. The effectiveness and practicability of the proposed method have been verified through a com-parative analysis with existing intelligent fault diagnosis methods, and the results suggest that the developed model has better robustness.

Mach Number Prediction for a Wind Tunnel Based on the CNN-LSTM-Attention Method

ZHAO Luping, WU Kunyang

2023(4):64-82, DOI: 10.15878/j.cnki.instrumentation.2023.04.002

[Abstract] (130) [HTML] (0) [PDF 12.87 M] (6428)

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The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance. The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions. The process flow in wind tunnel testing is inherently complex, resulting in a system characterized by nonlinearity, time lag, and multiple working conditions. To implement the predictive control algorithm, a precise Mach number prediction model must be created. Therefore, this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions. Firstly, this paper introduces a continuous transonic wind tunnel. The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process. Secondly, considering the nonlinear and time-lag characteristics of the wind tunnel system, a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model, which has long and short-term memory functions. Then, the attention mechanism is incorporated into the CNN-LSTM predic-tion model to enable the model to focus more on data with greater information importance, thereby enhancing the model's training effectiveness. The application results ultimately demonstrate the efficacy of the proposed approach.

Vibration Measurement of Pedestrian Bridge Using Double Magnetic Suspension Vibrator Based on Wavelet Analysis

JIANG Dong, KONG Deshan, ZHANG Zhengnan, WANG Deyu

2017,4(3):14-23, DOI:

[Abstract] (2024) [HTML] (0) [PDF 13.79 M] (5146)

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.

Research on the Crack Detection of Conductive Components Using Pulsed Eddy Current Thermography

ZHOU Deqiang, CHANG Xiang, DU Yang, CAO Piyu, WANG Hua, ZHANG Hong

2017,4(3):59-68, DOI:

[Abstract] (1440) [HTML] (0) [PDF 10.00 M] (4325)

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.

A Practical Pll-Based Drive Circuit with Ultra-Low-Noise Tia for Mems Gyroscope

CHEN Hua, ZHONG Yanqing, MENG Zhen

2017,4(3):7-13, DOI:

[Abstract] (2353) [HTML] (0) [PDF 8.76 M] (4195)

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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.

Detection of Contamination Defect on Ice Cream Bar Based on Fuzzy Rule and Absolute Neighborhood

LI Shaoli, YUAN Weiqi

2017,4(3):24-34, DOI:

[Abstract] (2149) [HTML] (0) [PDF 8.44 M] (3527)

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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.

Iris Segmentation Based on Matting

CHEN Qiru, WANG Qi, SUN Ting, WANG Ziyuan

2022,9(1):12-22, DOI: 10.15878/j.cnki.instrumentation.2022.01.003

[Abstract] (792) [HTML] (0) [PDF 3.00 M] (3302)

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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.

Research on Hand-eye Calibration Technology of Visual Service Robot Grasping Based on ROS

LI Shengqian, ZHANG Xiaofan

2022,9(1):23-30, DOI: 10.15878/j.cnki.instrumentation.2022.01.002

[Abstract] (704) [HTML] (0) [PDF 1.51 M] (3250)

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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.

Research on Surface Defect Detection Technology of Wind Turbine Blade Based on UAV Image

TAN Xingguo, ZHANG Gaoming

2022,9(1):41-48, DOI: 10.15878/j.cnki.instrumentation.2022.01.004

[Abstract] (494) [HTML] (0) [PDF 2.27 M] (3148)

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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.

Capacitive micromachined ultrasonic transducer as a resonant temperature sensor

LI Zhikang, Rahman.hebibul, ZHAO Libo, YE Zhiying, LI Ping, ZHAO Yulong , JIANG Zhuangde

2014,1(3):67-74, DOI:

[Abstract] (1252) [HTML] (0) [PDF 1.48 M] (3137)

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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.

Non-destructive Testing Method for Crack Based on Diamond Nitrogen-vacancy Color Center

ZHAO Long, WANG Xin, ZHAO Bowen, WU Guiyuan, LUO Dacheng, ZHANG Shaochun

2022,9(1):56-64, DOI: 10.15878/j.cnki.instrumentation.2022.01.006

[Abstract] (920) [HTML] (0) [PDF 6.19 M] (3117)

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.

Target Path Tracking Method of Intelligent Vehicle Based on Competitive Cooperative Game

SONG Chongzhi, NIU Limin

2022,9(1):31-40, DOI: 10.15878/j.cnki.instrumentation.2022.01.007

[Abstract] (550) [HTML] (0) [PDF 1.62 M] (3004)

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.

Structural Design of High-precision Positioning System in Weak Signal Environment Based on UWB and IMU Fusion

ZHAO Yang, WANG Tianhu, LI Wenjie, MIAO Qiannian, SHEN Yunzhe, HUANG Tao

2023(2):30-39, DOI: 10.15878/j.cnki.instrumentation.2023.02.001

[Abstract] (73) [HTML] (0) [PDF 3.80 M] (2931)

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.

BP-PID Control Applied in Evaporator of Organic Rankine Cycle System

WANG Zhigang, GUO Shuang

2017,4(3):54-58, DOI:

[Abstract] (2187) [HTML] (0) [PDF 2.44 M] (2878)

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.

Development and Application of Working Standard Pyranometer

MO Yueqin, LV Wenhua, QUAN Jimei

2017,4(3):35-39, DOI:

[Abstract] (1882) [HTML] (0) [PDF 3.21 M] (2867)

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.

Modeling of a wireless saw temperature sensor and associated antenna

Laurent ALLIES, Eloi BLAMPAIN, Hamid M’JAHED, Gérard PRIEUR, , Omar ELMAZRIA

2014,1(1), DOI:

[Abstract] (839) [HTML] (0) [PDF 8.46 M] (2865)

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.

Research on dynamic performance of star tracker

SUN Ting, XING Fei, YOU Zheng

2015,2(1):17-26, DOI:

[Abstract] (934) [HTML] (0) [PDF 3.70 M] (2779)

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.

Contrastive Observation of Solar Thermal and Photovoltaic Resource

BIAN Zeqiang, LV Wenhua, CHONG Wei

2017,4(3):1-6, DOI:

[Abstract] (2306) [HTML] (0) [PDF 1.84 M] (2490)

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.

Overview of one transistor type of hybrid organic ferroelectric non-volatile memory

Young Tea Chun, Daping Chu

2015,2(1):65-74, DOI:

[Abstract] (820) [HTML] (0) [PDF 12.96 M] (2281)

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.

Control of a 6DOF Mobile Manipulator with Object Detection and Tracking Using Stereo Vision

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] (1071) [HTML] (0) [PDF 77.55 M] (2251)

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.

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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

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