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Deep Convolutional Correlation Filter Learning Toward Robust Visual Object Tracking

Deep Convolutional Correlation Filter Learning Toward Robust...

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第34届中国控制与决策会议

作者： Tayssir Bouraffa Zihang Feng Yuxuan Wang Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

Recently,convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding *** of the visual object trackers extract deep features from a specific layer,generally from the last convolutional ***,these trackers are less effective,especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations,such as occlusion,illumination change,background clutter and so *** this research paper,a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video *** features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and *** the deep convolutional layers capture important semantic information,they are more robust to the target appearance *** for the shallow convolutional layers,they encode significant spatial details,which are more accurate to precisely localize the desired ***,Peak-Strength Context-Aware correlation filters are embedded to each convolutional layer output that produce multi-level convolutional response maps to collaboratively identify the estimated position of the target in a coarse-to-fine *** and qualitative experiments are performed on the widely used benchmark,the OTB-2015 dataset that shows impressive results compared to the state-of-the-art trackers.

关键词： Visual tracking convolutional neural network correlation filters hierarchical features

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No-Reference Image Quality Assessment using Meta-SGD

No-Reference Image Quality Assessment using Meta-SGD

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2021 China automation Congress, CAC 2021

作者： Van, Qmgqmg Dong, Bei Li, Xin Xiang, Yiwen Luo, Dapeng Wei, Longsheng 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)9781665426473

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. © 2021 IEEE

关键词： Deep learning

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Oscillation Suppression Method based on Power control Optimization of the Wind Farms 3

Oscillation Suppression Method based on Power Control Optimi...

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3rd IEEE International Power Electronics and Application Conference and Exposition, PEAC 2022

作者： Yang, Jiaqi Wang, Shaojun Li, Zhen Tian, Xu Liu, Bin Liu, Xiangdong Beijing Institute of Technology School of Automation Beijing China Co.Ltd Bayannur Power Supply Bureau Inner Mongolia China State Key Laboratory for Intelligent Control and Decision of Complex Systems Beijing China Beijing University of Mining and Technology Beijing Electric Engineering and Automation Beijing China Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (数字)9781665491419

ISBN: (纸本)9781665491419

Oscillation issues in the grid-connected wind farm system can severely deteriorate the power system security and lead to serious damage. Based on the impedance analysis method, many oscillation suppression methods have been proposed for reshaping the impedance characteristics of system to avoid the risk of oscillations. However, modifying converters' controllers to reshape the wind turbines (WTs) impedance and usage of additional damping devices for reshaping the grid impedance both are time-consuming and costly. Therefore, based on the sequence impedance model of the grid-connected wind farm system, this paper proposes a novel oscillation suppression method by optimizing the power control of wind farm. The system stability margin can be improved by modifying the number of online WTs and the output power of each WT to reshape the impedance of wind farm, under the precondition that the output power demand of wind farm is satisfied. Then, the intelligent particle swarm optimization (PSO) algorithm is used to solve the optimization approach to obtain the number of online WTs with optimal stability margin. Finally, the simulation confirms the effectiveness of the proposed oscillation suppression method. © 2022 IEEE.

关键词： System stability

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Overlapped Human Pose Estimation using Non-Maximum Suppression based on Shape Similarity

Overlapped Human Pose Estimation using Non-Maximum Suppressi...

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2021 China automation Congress, CAC 2021

作者： Yu, Xuefu Li, Jinsheng Xiao, Fei Liu, Wei Luo, Dapeng Wei, Longsheng 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 Chinese Construction Third Engineering Bureau Wuhan China School of Electronic Information and Mechanics China University of Geosciences Wuhan China

ISBN: (纸本)9781665426473

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. © 2021 IEEE

关键词： Convolutional neural networks

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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2022 Chinese automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available. © 2022 IEEE.

关键词： Reinforcement learning

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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Distributed Resilient State Estimation for Time-Varying Fractional-Order Discrete complex Networks with Encoding-Decoding Scheme

Distributed Resilient State Estimation for Time-Varying Frac...

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第42届中国控制会议

作者： Yu Chen Jun Hu Jiaxing Li Aozhan Zhou Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

In this paper,the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding *** encoding-decoding-induced dynamic errors for distinct nodes are characterized by the truncated Gaussian *** order to compensate the effects induced by encodingdecoding scheme,the variances of encoding-decoding-induced dynamic errors are considered in process of designing the resilient distributed estimation *** particular,the upper bounds of updated estimation error covariances are derived ***,the upper bounds are minimized by constructing the gain matrices at each sampling ***,a sufficient condition is provided to guarantee the boundedness of estimation error dynamics in the mean-square ***,the validity of distributed resilient state estimation scheme is demonstrated by a simulation example.

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Recursive Filtering for Time-Varying Nonlinear Delayed systems with Stochastic Parameter Matrices and Censored Measurements

Recursive Filtering for Time-Varying Nonlinear Delayed Syste...

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第42届中国控制会议

作者： Shuo Yang Jun Hu Peixia Gao Ruijie Dong Zhihui Wu Department of Applied Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems(NDSs) with stochastic parameter matrices(SPMs) and censored *** particular,the Tobit Type Ⅰ model provides a description of the censored *** main objective of this paper is to construct a recursive filter for NDSs with both SPMs and censored *** upper bound of the filtering error covariance is first calculated via mathematical induction,and the upper bound is then minimized by choosing proper filter ***,a sufficient condition is provided to guarantee that the filtering error is uniformly bounded in the mean-square ***,the viability and applicability of the proposed filterin g method are demonstrated using a numerical simulation.

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Outlier-Resistant Distributed Filtering for Multi-Rate Nonlinear systems under Amplify-and-Forward Relays

Outlier-Resistant Distributed Filtering for Multi-Rate Nonli...

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第42届中国控制会议

作者： Shuting Fan Jun Hu Chaoqing Jia Hairui Zhao Department of Applied Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

In this paper, the outlier-resistant distributed filtering problem based on amplify-and-forward relays is studied for discrete time-varying nonlinear multi-rate systems with multiple measurement delays over sensor networks, where the augmenting method is utilized to transform the multi-rate system into a single rate system. An amplify-and-forward(AF) relay is set between the sensor and the filter to extend the transmission distance of the signal and ensure the communication transmission *** outlier-resistant distributed filter is constructed by introducing a saturation function to limit the innovations, then the upper bound on the filtering error covariance is obtained and the filter gain is designed to minimize such obtained upper bound. Finally,a numerical example is used to show the effectiveness of the outlier-resistant distributed filtering algorithm based on AF relays.

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Sound Field control by Using Equivalent Input Disturbance method

Sound Field Control by Using Equivalent Input Disturbance me...

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Industrial Cyber-Physical systems (ICPS)

作者： Toshiki Watanabe Jinhua She Zewen Wang Department of Sustainable Engineering Tokyo university of technology Japan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan

Low-frequency noise, such as road noise from automobiles and interior noise from bullet train cars, is detrimental to passenger comfort. Active noise control provides us with a way of suppressing low-frequency noise. It generates a sound of the same amplitude and opposite phase as noise and uses it to cancel the noise. Conventional active noise control is complicated to apply. Thus, the objective of this study is to establish a sound field control system by using the equivalent-input-disturbance method. System identification was performed to obtain an ideal plant of a duct. The control system is constructed based on the equivalent-input-disturbance approach with a low-order controller. The experimental results showed a suppression effect of about 50% of the sound field.

关键词： Roads Noise reduction Ducts Channel estimation Auditory system Cyber-physical systems control systems

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