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第三十九届中国控制会议

作者： Xiang Chen Shiqi Zheng Feng Liu Xiongliang Zhang Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

This paper concentrates on the control problem of a class of fractional order nonlinear systems. A new adaptive backstepping sliding mode control is proposed, which inherits both the advantages of backstepping and sliding mode ***, a fractional order sliding mode disturbance observer is proposed to estimate unknown external disturbances. Meanwhile, RBF neural network is adopted to approximate unknown nonlinear terms. Finally, an illustrative simulation result based on a practical fractional order permanent magnet synchronous motor model is presented to verify the validity of proposed controller.

关键词： Fractional order systems adaptive sliding mode control disturbance observer backstepping

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Adaptive control for Stochastic Nonlinear Systems With Quantized States

Adaptive Control for Stochastic Nonlinear Systems With Quant...

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第三十九届中国控制会议

作者： Mingyuan Zhao Shiqi Zheng Feng Liu Xiang Chen Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, adaptive control problem is investigated for stochastic nonlinear systems with quantized states. Compared with the existing works, the considered system is more general which contains a deterministic nonlinear term and a stochastic disturbance in each channel. In order to handle the high order nonlinearities, we present a new technique which makes the partial derivative of virtual control law become a constant, this makes the adaptive control scheme of state quantization feasible. Then, a new adaptive law is proposed to deal with some completely unknown parameters, which includes an interesting third-order term of the parameters. Finally, simulation results demonstrate the efficacy of the proposed schemes.

关键词： adaptive control backstepping state quantization stochastic system

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Noise Modeling and Data Augmentation Using Conditional Adversarial Autoencoder

Noise Modeling and Data Augmentation Using Conditional Adver...

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第三十九届中国控制会议

作者： Yunkai Jiang Yuan Zhu Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd.

Noising image recognition is an important but challenging task. The images collected by the camera are often accompanied by a variety of noise, which will cause the loss of image information to make recognition difficult. With powerful feature extraction, neural network models can be used to process these complex noising images. However, when the noise model to be learned is complex and the number of learning samples is limited, this may lead to overfitting of the network model. In order to increase the diversity of noising images in limited learning samples and improve the generalization ability of the recognition model, we propose a noise modeling method based on CVAE-GAN. First, the encoder maps a real noising image to hidden layer vectors, which contains noise distribution, image category, and pose information. Then the decoder receives these hidden layer vectors to generate an synthetic image of the specified category and noise level. Finally, during the training process, we combine adversarial loss and smoothl1 loss to make the generated noising image more realistic. Experiments show that compared with other current generative models, our method can more effectively learn the distribution rules of complex noise in real images and generate synthetic images more similar to real noise. These synthetic images generated by our method can can alleviate the overfitting problem caused by insufficient data volume and enhance the generalization ability of image recognition models. By this way, we obtain the state-of-the-art recognition performance on the challenging noising image dataset.

关键词： Noising Image Data Augmentation Recognition Noise Modeling Weak Supervision

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Improving the precision of weak-value-amplification with two cascaded Michelson interferometers based on Vernier-effect

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui He, Fei-Fan Duan, Xue-Ying Wang, Guang-Jun Hu, Xiang-Yun School of Institute of Geophysics and Geomatics China University of Geosciences Lumo Road 388 Wuhan430074 China School of Automation China University of Geosciences Lumo Road 388 Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

A modified-weak-value-amplification(MWVA) technique of measuring the mirror's velocity based on the Vernier-effect has been proposed. We have demonstrated with sensitivity-enhanced and the higher signal-to-noise ratio(SNR) by using two cascaded Michelson interferometers. These two interferometers are composed of similar optical structures. One interferometer with a fixed mirror acts as a fixed part of the Vernier-scale, while the other with a moving mirror acts as a sliding part of the Vernier-scale for velocity sensing. The envelope of the cascaded interferometers shifts much more than a single one with a certain enhancement factor, which is related to the free space range difference between these two interferometers. In addition, we calculate the SNR based on the Fisher information with both the MWVA technique and the traditional-weak-value-amplification(TMVA) technique. The results show that the SNR with our MWVA technique is larger than the the SNR with the TWVA technique within the range of our time measurement window. Our numerical analysis proved that our MWVA technique is more efficient than the TWVA technique. And by using the principles of the Vernier-effect, it is applicative and convenient to ulteriorly improving the sensitivity and SNR in measuring other quantities with the MWVA technique. © 2021, CC BY.

关键词： Signal to noise ratio

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A historical review and bibliometric analysis of research on weak measurement research over the past decades based on biblioshiny

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui Duan, Xue-Ying He, Fei-Fan Wang, Guang-Jun Hu, Xiang-Yun School of Institute of Geophysics and Geomatics China University of Geosciences Lumo Road 388 Wuhan430074 China School of Automation China University of Geosciences Lumo Road 388 Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

Weak measurement has enabled fundamental studies in both experiment and theory of quantum measurement. Numerous researches have indicated that weak measurements have a wide range of application and scientific research value. In our work, we used bibliometric methods to evaluate the global scientific output of research on Weak measurement and explore the current status and trends in this field from 2000 to 2020. The R bibliometric package was used for quantitative and qualitative analyses of publication outputs and author contributions. In total, 636 related articles and reviews were included in the final analysis. The main results were as follows: The number of publications has increased substantially with time. Physical Review A was the most active journal. The country and institution contributing the most to this field were The United States and University Rochester respectively. The analysis of the conceptual structure of keywords indicated that weak measurements were involved a variety of metrology, quantum communication, and nonclassical features of quantum mechanics. Our bibliometric analysis shows that weak measurement research continues to be a hot-spot. The focus has evolved to study quantum information and amplify weak signals. © 2021, CC BY.

关键词： Quantum optics

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Fault Diagnosis of Micro Grid Inverter Based on Wavelet Transform and Probabilistic Neural Network

Fault Diagnosis of Micro Grid Inverter Based on Wavelet Tran...

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第三十九届中国控制会议

作者： Xin Gong Nan Wang Yiqiong Zhang Shuai Yin Mingyang Wang Genping Wu School of Automation China University of Geosciences (Wuhan) Hubei Provincial Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Second Ship Design and Research Institute

This paper focuses on the fault diagnosis of IGBT switch for microgrid three-phase inverters. Based on the analysis of all kinds of fault mechanisms, firstly, the fault simulation model of microgrid inverter is built on MATLAB/Simulink to simulate the fault state of inverter switch under various faults. The load-side three-phase current fault data is collected. Then, wavelet transform is used to extract feature parameters, and normalize the process in the form of energy to construct the fault feature vector. Finally, the genetic algorithm-based probability neural network(PNN) is used for classification. The experimental results show that the method that combining wavelet transform and PNN can diagnose the faults of microgrid inverter accurately and effectively.

关键词： Inverter Fault diagnosis Wavelet transform Genetic algorithm Probabilistic neural network

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Model-free Output Consensus control for Nonlinear Multi-agent Systems with Random Disturbance

Model-free Output Consensus Control for Nonlinear Multi-agen...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yipu Sun Xin Chen Hao Fu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728163895

ISBN: (纸本)9781728163901

The model-free output consensus control problem for the nonlinear multi-agent systems (MASs) with random disturbance is considered. In order to avoid the difficulty of designing a consensus protocol when the dynamic model is unknown, this paper proposes an online learning method for a consensus protocol by Gaussian regression based two-phase iteration. First, output of the leader is estimated by an adaptive distributed observer. On this basis, measurable input/output data is used to solve the consensus control problem of model-free systems. A two-phase iteration for Gaussian-kernel based adaptive critic design (GK-ACD) is proposed to approximate optimal Q-function and optimal control policies, which can simultaneously approximate and update value functions and hyper-parameters. This avoids the problem of artificial selection of improper hyper-parameters may mislead the kernel-based regression. Finally, the simulation results show that, without knowledge of dynamics, the distributed policies achieves the output consensus control of nonlinear MASs, and proves the effectiveness of the method.

关键词： Observers Adaptation models Vehicle dynamics System dynamics Synchronization Nonlinear dynamical systems Multi-agent systems

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Prediction of Gas Utilization Ratio Under Different Working Conditions of Blast Furnace Based on Extreme Learning Machine

Prediction of Gas Utilization Ratio Under Different Working ...

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International Conference on control and Robots (ICCR)

作者： Tianen Ma Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education of Engineering Research Centre for Earth Exploration Intelligent Technology Wuhan China

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Since the working condition affects the chemical reactions in a blast furnace (BF), the relationship between the blast-supply parameters and the gas utilization ratio (GUR) varies with different working conditions. This paper presents a GUR prediction model based on working condition classification. First, this paper analyzes the influence of working conditions on the state parameters of a BF. Then, a fuzzy clustering method is used to identify the working condition by using some state parameters. Next, the GUR prediction model was designed based on an extreme learning machine under different working conditions. Finally, the simulation results verified the effectiveness of this method.

关键词： Employee welfare Extreme learning machines Simulation Clustering methods Predictive models Blast furnaces Robots

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Learning Multiple Network Embeddings for Social Influence Prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2868-2873页

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Tokyo 192-0982 Japan

How to effectively predict social influence is an essential issue in social network analysis. Almost all reported methods for social influence prediction are mainly concerned with estimating influence probabilities for each linking edge. However, all of this past work cannot accurately predict influence probabilities for all edges due to the problem of data sparsity. Unlike conventional approaches, this work focuses on exploring a cross problem for multiple network embeddings and social influence prediction. This study developed a new end-to-end approach, Multi-Influor, that learns multiple influence vectors for each user in social networks, instead of estimating influence probabilities for each edge. The multiple network embeddings consider multi-dimensional influence factors that incorporate pairwise node interactions, network structures, and global similarity comparisons. Moreover, this study solves the problem of influence evaluation caused by sparse observations. Extensive comparisons based on large-scale datasets indicate that the Multi-Influor approach outperforms several state-of-the-art baselines, and the experimental results demonstrate that the Multi-Influor approach is more practical on real-world social networks.

关键词： Multiple Network Embeddings Representation Learning Online Social Networks Neural Network Social Influence Prediction

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Tolerance and detection for the edge disconnection in consensus problems of multi-agent systems with the time-delay

Tolerance and detection for the edge disconnection in consen...

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Chinese automation Congress (CAC)

作者： Junhao Ren Xiaofeng Zong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, tolerance and detection problems for the edge disconnection in consensus issues of discrete-time multi-agent systems with the time-delay are investigated. We first discuss the tolerance of the edge disconnection in consensus problems. For the case with the strongly connected communication topology, the faulty system can still achieve consensus if the connectedness of the multi-agent system is not changed as the edge disconnection, but the consensus value of the faulty system might be different from the original one. Especially, the consensus value of the system with the edge disconnection will be consistent with the original one if the communication topology of the faulty system is undirected and connected. In addition, the detection problem of the edge disconnection in multi-agent systems is considered by transforming the detection problem into the discernibility problem between the faulty system and the original system. By the state augmentation method and observability properties of the system, some equivalent conditions for the discernibility of the edge disconnection are established.

关键词： Topology Image edge detection Delays Multi-agent systems Laplace equations Matrix decomposition Directed graphs

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