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Chinese control Conference

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： Couplings Tuning Transmission line matrix methods Integrated circuit modeling Optimization methods Cats

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Fog Removal Method of Slope Monitoring Image Based On Vision Detection

Fog Removal Method of Slope Monitoring Image Based On Vision...

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Chinese control Conference

作者： Xianzhen Sang Yue Yang Xianhe Hou Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

In the slope monitoring based on image detection, the main work is to process the acquired slope image. The landslide occurred mostly in the rain, fog and other complex weather conditions. If we can process effectively and fast fog images according to the fog horizon slope vision images. It would be helpful for subsequent image segmentation, object extraction, positioning, and improving the accuracy and efficiency of detection of slope deformation. Based on the visual technology in slope monitoring, we compared two kinds of defogging algorithm of slope images. One is a kind of image enhancement method of non-physical model, mainly including: equalization algorithm and homomorphic filtering algorithm, McCann Retinex algorithm and multi-scale Retinex algorithm and a global histogram. The other is the image restoration method based on physical model, including the dark channel prior bilateral filtering algorithm, and combined with the theory of dark channel prior to fog algorithm. The experimental results show that the histogram equalization method has the advantage of fast imaging quality in slope visual image processing, and is more suitable for slope monitoring.

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Bifurcation Analysis and control of Fractional-Order Quorum Sensing Network 37

Bifurcation Analysis and Control of Fractional-Order Quorum ...

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37th Chinese control Conference, CCC 2018

作者： Feng, Liu Xiangjuan, Dai Qicheng, Mei Guang, Ling Xinmei, Wang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789881563941

In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by sRNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Stability criteria

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RBF Neural Network Based-PID control for Weight on Bit During Drilling Operation

RBF Neural Network Based-PID Control for Weight on Bit Durin...

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Chinese control Conference

作者： Sike Ma Min Wu Luefeng Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China

Deep drilling is a costly project and efficiency is of paramount importance. The weight on bit is one of the main operating parameters that influences the drilling efficiency and it was controlled by manual before. But after people saw the giant potential of an auto-drilling system in increasing the drilling efficiency, more and more studies on the feed back control of weight on bit have emerged. This paper mainly studied weight on bit dynamic under the variational formation based on a lumped parameter model and a self-tuning PID controller for weight on bit control. The parameters of the PID controller are tuned by using gradient descent method and RBF neural network identification.

关键词： Neural networks DC motors Feeds Suspensions Lifting equipment Torque Angular velocity

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Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

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Energy Procedia 2019年 158卷 1798-1803页

作者： Tao Xue Xiaolong Wu Yuanwu Xu Suwen Jing Zehua Li Jianhua Jiang Zhonghua Deng Xiaowei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

关键词： SOFC fault diagnosis neural network

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Real-time optical music recognition for dulcimer robot 8

Real-time optical music recognition for dulcimer robot

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Xiao, Zhe Chen, Xin Zhou, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Arts and Communication China University of Geosciences Wuhan430074 China

Optical music recognition (OMR) is an important technology to recognize paper music sheet automatically, which has been applied to preserve music scores. In this paper, we propose a real-time OMR system to recognize scores, and the recognized information is converted into a standard electronic music file to dulcimer robot, achieving real-time performance. During the recognition steps, we treat note group and isolated notes separately. The note group is decomposed into three fundamental elements: note stem, note head and note beams. And the isolated music symbol is recognized based on shape model descriptor. We conduct tests on five pages of music sheets, which are real images taken live by the camera. The tests show that the proposed method constitutes an interesting contribution to OMR. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Computer music

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Collaborative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge automation

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IFAC-PapersOnLine 2020年第5期53卷 572-577页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

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EEG emotion recognition based on ensemble learning 8

EEG emotion recognition based on ensemble learning

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作者： Ding, Xue-Wen Liu, Zhen-Tao Feng, Zhi-Yong Xie, Qiao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Automotive Engineering Wuhan University of Technology Wuhan430070 China

EEG emotion recognition based on ensemble learning is proposed, in which three classification models including k-Nearest Neighbor, Decision Tree and Logistic Regression are integrated. In feature extraction, four kinds of features including Mean, Skewness, Sample Entropy and Lempel-Ziv complexity (LZC) are extracted from eight channels’ EEG signals, and Kernel Principal Component Analysis (KPCA) is employed to reduce the dimension of the feature set. The proposal is tested on the DEAP dataset in which the EEG emotional states are accessed in Arousal-Valence emotion space. The result shows that the average emotion recognition accuracies of 77.73% in Arousal dimension and 75.34% in Valence dimension are obtained, which demonstrates its feasibility. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Emotion Recognition

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Cyber-Physical-Social systems for Smart City: An Implementation Based on intelligent Loop

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IFAC-PapersOnLine 2020年第5期53卷 501-506页

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-system (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS intelligent Loop Human-Cyber-Physical Ternary Space

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An Improved Version of DMOEA-εC for Many-objective Optimization Problems: IDMOEA-εC

An Improved Version of DMOEA-εC for Many-objective Optimiza...

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第三十八届中国控制会议

作者： Juan Li Bin Xin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

The decomposition strategy and the ε-constraint method are two important strategies in the field of multi-objective optimization. DMOEA-εC first attempts to incorporate the ε-constraint method into the decomposition strategy and solve a multi-objective optimization problem(MOP) via optimizing a series of scalar constrained subproblems collaboratively with the help of information from neighboring subproblems. However, given the inefficiency of applying DMOEA-εC to deal with many-objective optimization problems(MaOPs), a two-stage upper bound vectors generation procedure is proposed to generate widely spread upper bound vectors in a high-dimensional space. Besides, a boundary points maintenance mechanism is put forward to remedy the diversity loss of a population in DMOEA-εC. Based on the above, DMOEA-εC with the two-stage upper bound vectors generation procedure and the boundary points maintenance mechanism, named as IDMOEA-εC, is presented for MaOPs. IDMOEA-εC is compared with four state-of-the-art many-objective evolutionary algorithms, including HypE, NSGA-Ⅲ, MOEADD, and Two Arch2. Experimental studies demonstrate that IDMOEA-εC outperforms or performs competitively against these algorithms on the majority of sixteen DTLZ test instances with up to 10 objectives.

关键词： Many-objective optimization decomposition ε-constraint method two-stage upper bound vectors generation procedure boundary points maintenance mechanism

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