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Yu Yan Zhang
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

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Journal article
Published: 31 March 2021 in Sensors and Actuators A: Physical
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Planar array capacitance imaging technology is a kind of nondestructive testing technology applied to defect detection of composite materials. In the imaging process, the image quality is often poor due to the environment noise and the ill-posed of inverse problem solving. In this paper, an image reconstruction optimization method is proposed. Based on the analysis of the sensitive region of a single pair of electrodes and the contribution of capacitance value to each solution unit, an optimization matrix which is calculated by adjusting the contribution coefficient of each capacitance value to different solution units is proposed to optimize the sensitive field. Finally, simulation and experiment results are presented to show the effectiveness of the proposed method. Through the average relative error of each pixel of the reconstructed image before and after optimization, it can be seen that the error of reconstructed images at two different positions are reduced by an average of 3.22 % and 1.62 % respectively, while verified this method can effectively improve the reconstructed image and improve the anti-noise ability of the image.

ACS Style

Zhao Pan; Shan Wang; Pengcheng Li; Yuyan Zhang; Yintang Wen. An optimization method of planar array capacitance imaging. Sensors and Actuators A: Physical 2021, 327, 112724 .

AMA Style

Zhao Pan, Shan Wang, Pengcheng Li, Yuyan Zhang, Yintang Wen. An optimization method of planar array capacitance imaging. Sensors and Actuators A: Physical. 2021; 327 ():112724.

Chicago/Turabian Style

Zhao Pan; Shan Wang; Pengcheng Li; Yuyan Zhang; Yintang Wen. 2021. "An optimization method of planar array capacitance imaging." Sensors and Actuators A: Physical 327, no. : 112724.

Journal article
Published: 01 January 2021 in Acta Physica Sinica
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ACS Style

Zhang Yu-Yan; Yin Dong -Zhe; Wen Yin-Tang; Luo Xiao-Yuan. Planar array capacitance imaging based on adaptive Kalman filter. Acta Physica Sinica 2021, 70, 118102 -118102.

AMA Style

Zhang Yu-Yan, Yin Dong -Zhe, Wen Yin-Tang, Luo Xiao-Yuan. Planar array capacitance imaging based on adaptive Kalman filter. Acta Physica Sinica. 2021; 70 (11):118102-118102.

Chicago/Turabian Style

Zhang Yu-Yan; Yin Dong -Zhe; Wen Yin-Tang; Luo Xiao-Yuan. 2021. "Planar array capacitance imaging based on adaptive Kalman filter." Acta Physica Sinica 70, no. 11: 118102-118102.

Journal article
Published: 16 September 2020 in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
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Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

ACS Style

Zhao Pan; Rui Hang Li; Yao Yao Cui; Xi Jun Wu; Yu Yan Zhang; Yu Tian Wang. A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2020, 245, 118948 .

AMA Style

Zhao Pan, Rui Hang Li, Yao Yao Cui, Xi Jun Wu, Yu Yan Zhang, Yu Tian Wang. A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2020; 245 ():118948.

Chicago/Turabian Style

Zhao Pan; Rui Hang Li; Yao Yao Cui; Xi Jun Wu; Yu Yan Zhang; Yu Tian Wang. 2020. "A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 245, no. : 118948.

Journal article
Published: 24 December 2017 in Sensors
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A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced.

ACS Style

Yintang Wen; Zhenda Zhang; Yuyan Zhang; Dongtao Sun. Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing. Sensors 2017, 18, 31 .

AMA Style

Yintang Wen, Zhenda Zhang, Yuyan Zhang, Dongtao Sun. Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing. Sensors. 2017; 18 (1):31.

Chicago/Turabian Style

Yintang Wen; Zhenda Zhang; Yuyan Zhang; Dongtao Sun. 2017. "Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing." Sensors 18, no. 1: 31.

Journal article
Published: 25 October 2017 in Sensors
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This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms.

ACS Style

Yintang Wen; Yao Jia; Yuyan Zhang; Xiaoyuan Luo; Hongrui Wang. Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT. Sensors 2017, 17, 2440 .

AMA Style

Yintang Wen, Yao Jia, Yuyan Zhang, Xiaoyuan Luo, Hongrui Wang. Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT. Sensors. 2017; 17 (11):2440.

Chicago/Turabian Style

Yintang Wen; Yao Jia; Yuyan Zhang; Xiaoyuan Luo; Hongrui Wang. 2017. "Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT." Sensors 17, no. 11: 2440.

Journal article
Published: 01 February 2014 in Chinese Physics B
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ACS Style

Xiao-Yuan Luo; Shi-Kai Shao; Yu-Yan Zhang; Shao-Bao Li; Xin-Ping Guan; Zhi-Xin Liu. Generation of minimally persistent circle formation for a multi-agent system. Chinese Physics B 2014, 23, 028901 .

AMA Style

Xiao-Yuan Luo, Shi-Kai Shao, Yu-Yan Zhang, Shao-Bao Li, Xin-Ping Guan, Zhi-Xin Liu. Generation of minimally persistent circle formation for a multi-agent system. Chinese Physics B. 2014; 23 (2):028901.

Chicago/Turabian Style

Xiao-Yuan Luo; Shi-Kai Shao; Yu-Yan Zhang; Shao-Bao Li; Xin-Ping Guan; Zhi-Xin Liu. 2014. "Generation of minimally persistent circle formation for a multi-agent system." Chinese Physics B 23, no. 2: 028901.

Journal article
Published: 11 November 2010 in International Journal of Automation and Computing
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In this paper, decentralized methods of optimally rigid graphs generation for formation control are researched. The notion of optimally rigid graph is first defined in this paper to describe a special kind of rigid graphs. The optimally rigid graphs can be used to decrease the topology complexity of graphs while maintaining their shapes. To minimize the communication complexity of formations, we study the theory of optimally rigid formation generation. First, four important propositions are presented to demonstrate the feasibility of using a decentralized method to generate optimally rigid graphs. Then, a formation algorithm for multi-agent systems based on these propositions is proposed. At last, some simulation examples are given to show the efficiency of the proposed algorithm.

ACS Style

Rui Ren; Yu-Yan Zhang; Xiao-Yuan Luo; Shao-Bao Li. Automatic generation of optimally rigid formations using decentralized methods. International Journal of Automation and Computing 2010, 7, 557 -564.

AMA Style

Rui Ren, Yu-Yan Zhang, Xiao-Yuan Luo, Shao-Bao Li. Automatic generation of optimally rigid formations using decentralized methods. International Journal of Automation and Computing. 2010; 7 (4):557-564.

Chicago/Turabian Style

Rui Ren; Yu-Yan Zhang; Xiao-Yuan Luo; Shao-Bao Li. 2010. "Automatic generation of optimally rigid formations using decentralized methods." International Journal of Automation and Computing 7, no. 4: 557-564.