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Chinese automation Congress (CAC)

作者： Xuefu Yu Jinsheng Li Fei Xiao Wei Liu Dapeng Luo Longsheng Wei School of Automation China University of Geosciences Wuhan China Key Laboratory of Geological Survey and Evaluation of Ministry of Education Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Intelligent Technology co. Ltd of Chinese Construction Third Engineering Bureau Wuhan China School of Electronic Information and Mechanics China University of Geosciences Wuhan China

ISBN: (纸本)9781665426480

Deep Convolutional Neural Networks (DNNs) have made significant progress on human pose estimation task in recent years. However, the question of whether we are making the most use of the information we get from DNNs has not been well studied. Top-down approach which first detects person body box then utilizes single person pose estimation by heatmap is a widely used method in multi-person pose estimation. However, information that predicted by DNNs contains more information than we consider. In person box prediction process, when two persons are closed to each other, the Non-Maximum Suppression (NMS) we often use filters out the body boxes with smaller scores. for tackling this problem, we apply NMS based on Shape Similarity to person body boxes detection for tackling the problem that deleting the overlapped body boxes with smaller scores, and then extract keypoint coordinates and tag information for filter noise candidates from heatmap by Trasformer to improve the prediction accuracy. At last we develop an algorithm for refining keypoint coordinate by tag information. In experiment, we got mean Average Precision(mAP) of 76.0% in COCO2017 validation dataset, better than 75.6% with TransPose.

关键词： Heating systems Shape Pose estimation Refining Filtering algorithms Information filters Prediction algorithms

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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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No-Reference Image Quality Assessment using Meta-SGD

No-Reference Image Quality Assessment using Meta-SGD

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Chinese automation Congress (CAC)

作者： Qingqing Yan Bei Dong Xin Li Yiwen Xiang Dapeng Luo Longsheng Wei School of Automation China University of Geosciences Wuhan China Key Laboratory of Geological Survey and Evaluation of Ministry of Education Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Intelligent Technology co. Ltd of Chinese Construction Third Engineering Bureau Wuhan China School of Electronic Information and Mechanics China University of Geosciences Wuhan China

ISBN: (纸本)9781665426480

Deep learning-based no-reference image quality assessment (NR-IQA) algorithm requires massive training data and labels, but data scarcity usually exists in practical applications. Most of the previous metrics used pre-trained networks to solve this problem by fine-tuning on the target tasks. Unfortunately, the network trained on other tasks cannot be directly applied to IQA tasks and failed when encountering complex distortions. To reduce dependence on labeled data and improve generalization ability of unknown distortion types, a NR-IQA metric based on Meta-SGD is proposed, which can learn the quick adaptability of humans and obtain general meta-knowledge for distortion evaluation. Specifically, we collected a great many NR-IQA tasks with different distortion types to pre-train the model; then a meta-learning metric based on optimization, Meta-SGD, is proposed to acquire the meta-knowledge when evaluating images quality with various known distortions; finally, the obtained pre-trained meta-model can be directly applied to the new NR-IQA tasks only by fine-tuning a few images. Experiments on the TID2013 database demonstrate that the model after meta-learning obviously outperforms than some existing methods, and the average SRCC value for all types of distortion performance tests can reach 0.91.

关键词： Measurement Image quality Adaptation models automation Databases Training data Distortion

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An Universal control Strategy for Planar 2-DoF Underactuated Manipulator with One Passive Joint

An Universal Control Strategy for Planar 2-DoF Underactuated...

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第40届中国控制会议

作者： Zixin Huang Yaosheng Zhou Zhen Chen Wei Wang Lejun Wang School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University 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 Innovation and Entrepreneurship Wuhan Institute of Technology School of Information and Safety Engineering Zhongnan University of Economics and Law

This paper presents an unified control strategy based on the trajectory planning and tracking control for the planar 2-Do F underactuated manipulator. The trajectory of the active link is composed of two parts. The first part trajectories are designed according to the initial and target angles of the active link. The second part trajectories are designed based on the constraints between the underactuated link and the active link. Meanwhile, the parameters of the trajectories are optimized by the differential evolution algorithm(DEA) to ensure all links eventually reach to their target values by tracking the designed trajectories. Then,the sliding mode variable structure controller are designed to make the active link track their trajectories. The effectiveness of such strategy is demonstrated through simulation results.

关键词： Planar 2-DoF underactuated manipulator Trajectory planning Tracking control Differential evolution algorithm

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Analysis and circuit implementation of a new fourth-order multi-wing chaotic system based on memristors

Analysis and circuit implementation of a new fourth-order mu...

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Chinese automation Congress (CAC)

作者： Lei Jia Xiaowei Jiang Xianhe Zhang Ming Chi Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education School of Automation China University of Geosciences Wuhan P. R. China School of Electrical Engineering and Automation Hubei Normal University Huangshi P. R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781665426480

In this paper, a new fourth-orders memristor chaotic system is obtained by introducing a smooth memristor model as the feedback term based on the modified three-orders chaotic system. The system can generate double-wing, four-wing chaos or hyperchaos for different parameters and initial conditions. Through the analysis of Lyapunov exponent and other dynamics, we can see that the system has complex chaotic, hyperchaotic, and continuous chaotic. Other dynamical behaviors such as multi-stability and extreme multi-stability are found by comparing phase and bifurcation diagrams, and equilibrium point calculations. Finally, the chaotic behavior is successfully verified by circuit simulation.

关键词： Chaos automation Circuit simulation Buildings Memristors Bifurcation Encryption

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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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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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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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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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