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An online measurement method for noise-source impedance of electrical equipment

Progress In Electromagnetics Research C 2020年 100卷 95-103页

作者： Du, Mingxing Li, Yang Wang, Hongbin Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

The paper presents a method of extracting noise source impedance of electrical equipment under working condition. Firstly, based on the theory of two-port network, the measurement method of noise impedance is analyzed theoretically, and the injection probe and receiving probe are calibrated by two known resistors. No special calibration fixture is needed to calibrate the injection probe and receiving probe. Secondly, the port structure of the noise impedance measurement method is analyzed, and the noise source impedance is calculated by using the theory of microwave transmission. Compared with the traditional method, this method does not require calibration fixture and simplifies the experimental process. Finally, passive devices and active systems are tested respectively, and the experimental results show that the method is effective and feasible. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Probes

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An improved calculation method for static capacitance in inductor windings

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Progress In Electromagnetics Research C 2020年 104卷 25-36页

作者： Du, Mingxing Zhang, Yuxiao Wang, Hongbin Tian, Ye Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

This paper proposes an improved method for calculating static capacitance between two conductors with circular cross-sections in inductor windings. It considers the effects of electric field coupling and energy distribution on static capacitance. In this paper, the capacitance between two conductors in inductor windings is calculated by the improved calculation method and finite-element method (FEM), respectively. The relative error of the improved calculation method is between 0.11% and 4.51% compared to the FEM. In order to verify the effectiveness of this method for inductor winding, the orthogonal stacking winding and staggered stacking winding are chosen as calculation examples to accurately predict the static capacitance of multi-layer circular-section induction coils. Finite element models for the two types of windings are built to determine the capacitances for our 3 × 3 array arrangement winding. The results show that the improved calculation method proposed in this paper highly conforms to FEM. Finally, we adopt an air-cored cylindrical inductor winding for experimental verification, and the improved calculation method is proved to be correct. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Capacitance

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A Multi-Feature Fusion Based Pedestrian Detection Method

A Multi-Feature Fusion Based Pedestrian Detection Method

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IEEE International Conference on Mechatronics and Automation

作者： Enzeng Dong Cunlei Jing Zufeng Zhang Tianjin Key Laboratory For Control Theory & Applications in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

This paper proposes an improved pedestrian detection algorithm, the Haar-like feature and Adaboost cascade classifier Histogram of Oriented Gradient (HOG) feature are utilizing to construct hybrid features library, then the weak classifiers in Adaboost are trained by using the combined hybrid features. And the strong classifiers are built based on the weak classifiers. In the proposed algorithm, For improving the detection speed, the HOG feature is rapidly calculated by integral histogram instead of gradient histogram features, and the experimental results showed that the method proposed improved the detection performance and detection accuracy of pedestrians.

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Deep fuzzy infrared handprint target extraction algorithm based on wave-particle duality mechanism

Deep fuzzy infrared handprint target extraction algorithm ba...

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Chinese Automation Congress (CAC)

作者： Xuejuan Bao Dahua Li Qiang Gao Xiao Yu Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In criminal cases, handprint information on-site is one of the most critical evidences in the detection of the case. When shooting with an infrared camera, the heat trace images collected are blurred and incomplete due to heat loss, which makes it difficult to detect. The wave-particle duality shows the particle property and volatility interaction between macroscopic objects and microscopic particles, a handprint target extraction algorithm based on the wave-particle duality mechanism is proposed. Through the wave-particle duality relation between adjacent pixels, the target area of the hand is obtained in the image of the deep- blurred infrared handprints through the matching of the hand skeleton template. The gray value is adjusted by the influence of the volatility between the pixels in the target area on the gray, at the same time according to the particle property relationship expands the pixels on the edge of the skeleton to obtain a clear target area in the deep blurred image. Finally, the K-means clustering segmentation algorithm is used to segment the handprint and the background in the blurred image, and to extract the handprint target. Experimental results show that the target positive extraction rate of the algorithm is 97%. Compared with other traditional algorithms, the algorithm can more effectively and accurately extract the handprint target in the deep blurred infrared image.

关键词： Image segmentation Infrared heating Image edge detection Microscopy Clustering algorithms Skeleton Data mining

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Design of a Capsule Robot system for Gastric Hemorrhage Detection using Luminol

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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IEEE International Conference on Mechatronics and Automation

作者： Yuchen Mao Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application Complicated Systems and Intelligent Robot Laboratory School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system.

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Model Reference Scheduling and Resilient control Co-design with Modulation

Model Reference Scheduling and Resilient Control Co-design w...

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第32届中国控制与决策会议

作者： Shunli Zhao Yuehui Ji Tianjin Key Laboratory for Control Theory & Applications in Complicated System Tianjin University of Technology School of Electrical and Electronic Engineering Tianjin University of Technology

The medium access constraint that only permits limit nodes to gain access to the network is a research focus in the field of networked control systems.A co-design of the model reference scheduling and the resilient control with the modulation is proposed in the *** co-design scheme can deal with several representative problems in networked control systems,such as time delays from modulation,the medium access constraint and the perturbation of the ***,the effectivity of the co-design scheme proposed is demonstrated through a numerical example.

关键词： Networked control systems Modulation Medium Access Constraint Resilient control

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Short-term Photovoltaic Power Prediction based on DE-GWO-LSTM

Short-term Photovoltaic Power Prediction based on DE-GWO-LST...

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IEEE International Conference on Mechatronics and Automation

作者： Hui Zhao Zhili Zhao Hongjun Wang Youjun Yue Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin University of Technology Tianjin China College of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In order to solve the uncertainty and randomness of photovoltaic output prediction caused by weather factors, and improve the accuracy of short-term photovoltaic power prediction, this study uses long short-term memory (LSTM) to establish a prediction model, and trains historical time series data to predict short-term photovoltaic power. First, a layer-by-layer training algorithm is used to construct a multi-hidden-layer network structure with excellent performance in regression prediction analysis. By analyzing the characteristics of the LSTM network function, a photovoltaic output prediction model is established. Then, the grey wolf optimization (GWO) algorithm of differential evolution algorithm (DE) is proposed to optimize the connection weight of LSTM to prevent the local minimum caused by random initialization, overcome the shortcomings of back-propagation algorithm used in traditional LSTM network, and improve the prediction accuracy of LSTM network. Finally, the simulation experiment of the improved LSTM model is carried out to test the prediction effect of the model under three kinds of weather conditions to prove the effectiveness of the model.

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Research on Abnormal Power Consumption Detection Technology Based on Decision Tree and Improved SVM

Research on Abnormal Power Consumption Detection Technology ...

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IEEE International Conference on Mechatronics and Automation

作者： Hongjun Wang Zhiqi Li Hui Zhao Youjun Yue Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin University of Technology Tianjin China College of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

The main purpose of abnormal power consumption detection for power users is to maintain the legitimate rights and interests of normal users, improve the economic benefits of power grid companies, and thereby reduce non-technical losses (NTL). In order to realize the rational use of user-side data and improve the efficiency of power audit, this article proposes an improved SVM power theft detection model based on decision tree. First of all, to address the problem of less electricity stealing data, this article combines convolutional neural networks (CNN) and generative adversarial networks (GAN) for data generation, and uses the powerful feature extraction function of CNN to extract the features of different stealing methods to guide GAN. Then the improved support vector machine (SVM) model based on decision tree (DT) detects user data. When classifying, it combines SVM and KNN for classification, which solves the point of SVM near the decision plane. For the problem of low classification accuracy, simulation experiments finally verified the effectiveness of the proposed model.

关键词： Support vector machines Power demand Support vector machine classification Generative adversarial networks Feature extraction Data models Decision trees

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Study on Freshwater Fish Image Recognition Integrating SPP and DenseNet Network

Study on Freshwater Fish Image Recognition Integrating SPP a...

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IEEE International Conference on Mechatronics and Automation

作者： Hongjun Wang Yangyang Shi Youjun Yue Hui Zhao Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin University of Technology Tianjin China College of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

With the development of image processing and machine vision, there are emerging fish species classification algorithms that use machine vision technology to identify specific scenes. In order to solve the problem of poor working environment and low efficiency of traditional artificial identification of fish species, this paper proposes a deep learning method combining SPP (spatial pyramid pooling) and DenseNet (dense neural network). The model is connects the spatial pyramid pooling layer in front of the full connection layer of DenseNet network. On the basis of maintaining good recognition accuracy of DenseNet, the integrated new network can increase scale invariance and reduce overfitting. Through the identification test of 6 kinds of freshwater fish, the classification accuracy rate reached 97%. Experimental results show that the new network SPP-Densenet can effectively identify species of freshwater fish images.

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UAV aerial image mosaic algorithm based on FAST-Tomasi feature and Delaunay triangulation

UAV aerial image mosaic algorithm based on FAST-Tomasi featu...

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IEEE International Conference on Mechatronics and Automation

作者： Hui Zhao YaXin Du Hongjun Wang Youjun Yue Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin University of Technology Tianjin China College of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In view of the current inability to meet the requirements of fast speed and strong stability in feature extraction, and the slow speed in feature point elimination, this paper proposes an UAV remote sensing image mosaic algorithm based on FAST-Tomasi features and Delaunay triangulation. Firstly, FAST feature detection algorithm is used to accelerate Shi-Tomasi algorithm to obtain fast and accurate feature points, the feature points are described by BRIEF operator, and the phase is conducted by hamming distance. Then, Delaunay triangulation network is constructed according to the initial matching points, and the similarity between the triangles in the two images to be stitched is calculated. The triangle pair with large similarity is the matching triangle pair, so the corresponding accurate matching points are obtained. Then, the improved SPHP algorithm is used for registration. Finally, the weighted algorithm is used to fuse the image. Experimental results indicate that the algorithm retains more correct matching points, the accuracy of eliminating false matching points is higher, and the matching speed is significantly improved.

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