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Chinese control Conference (CCC)

作者： Jinglan Bai Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Infrared and visible image fusion technology helps to improve the spatial resolution. It mainly preserves the features and details of the source images and generates a fusion image with visual enhancement. In this paper, based on the gradient features and intensity information of the source images, an optimization model for image fusion is built. Firstly, the pre-fused gradient of the source images is obtained by combining the structure tensor and the proposed local gradient similarity, where local gradient similarity is used to make the fused gradient direction more accurately. Secondly, the source images are reconstructed into salient and non-salient images according to the comparison of the pixel intensity. A weight map before the non-salient image in the optimization model makes the effective details preserved, so that the pre-fused images consist of the salient image and the non-salient image with a weight map. Finally, an optimization model is constructed to constrain the gradient and intensity of the final fused image close to the pre-fused gradient and the pre-fused images. The final fused image is obtained from solving the optimization model by use of the variational method. The experimental results are evaluated from subjective and objective assessments, which show the effectiveness of the proposed algorithm.

关键词： Optimization Tensile stress Image fusion Mathematical model Matrix decomposition Eigenvalues and eigenfunctions Visualization

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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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Chinese control and Decision Conference, CCDC

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

ISBN: (纸本)9781665478977

Recently, convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding background. Most of the visual object trackers extract deep features from a specific layer, generally from the last convolutional layer. However, 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 on. In 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 sequence. Hierarchical features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and robustness. As the deep convolutional layers capture important semantic information, they are more robust to the target appearance variations. As for the shallow convolutional layers, they encode significant spatial details, which are more accurate to precisely localize the desired target. Moreover, 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 manner. Quantitative 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： Visualization Target tracking Correlation Semantics Video sequences Feature extraction Information filters

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： complex networks

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Dynamic State Estimation Using Event-Trigger Master-Slave Nonlinear Filter for WAMS Applications

Dynamic State Estimation Using Event-Trigger Master-Slave No...

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Data Driven control and Learning systems (DDCLS)

作者： Qing Yuan Fengdi Zhang Hengheng Gong Luyu Li Sen Li Xiaozhong Liao Zhen Li Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing P. R. China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing P. R. China Beijing Institute of Radio Measurement Beijing P. R. China

ISBN: (纸本)9781538626191

The real-time state estimation becomes greatly important with the wide application of phasor measurement unit (PMU) in distributed generation (DG) for wide-area measurement systems (WAMS). In view of estimation, particle filter (PF) is capable of providing the best performance but at the cost of heavy computation burden. Besides, the growing grid size sustainably boosts the amount of data communication from PMU, causing the congestion. An event-trigger master-slave nonlinear filter (ET-MSNF) is proposed to guarantee the estimation accuracy and get the communication bandwidth relieved. The local slave filter at the generator node carries out the local estimation and event-trigger strategy using unscented transformation, which is identical to the center slave. The master filter at the center is designed using Monte Carlo method to improve the center's estimation accuracy by the cooperation with the center slave. Such master-slave filtering structure can fully utilize the computation capability both at the center and node. Simulation on the standard IEEE 39-bus system verify the performance of ET-MSNF.

关键词： Generators Monte Carlo methods Phasor measurement units Master-slave Wide area measurements State estimation

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Primal-dual Accelerated Mirror-Descent Method for Constrained Bilinear Saddle-Point Problems

arXiv

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arXiv 2024年

作者： Li, Weijian Zeng, Xianlin Pavel, Lacra The Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada The Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

We develop a first-order accelerated algorithm for a class of constrained bilinear saddle-point problems with applications to network systems. The algorithm is a modified time-varying primal-dual version of an accelerated mirror-descent dynamics. It deals with constraints such as simplices and convex set constraints effectively, and converges with a rate of O(1/t2). Furthermore, we employ the acceleration scheme to constrained distributed optimization and bilinear zero-sum games, and obtain two variants of distributed accelerated algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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A Preliminary Study on the Functional Coupling between Nerve and Blood Microcirculation for Applications in Rehabilitation Robots

A Preliminary Study on the Functional Coupling between Nerve...

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IEEE International Conference on Robotics and Biomimetics

作者： Qingge Li Yuanzhe Dong Yuxiang Zhang Xin Wang Naifu Jiang Jianping Huang Han Cui Lan Tian Yue Zheng Xiangxin Li Lin Wang Guanglin Li Wenyuan Liang Liang Peng Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China National Research Center for Rehabilitation Technical Aids Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Rehabilitation robots play an important role in the motor function rehabilitation for stroke survivors with hemiplegia. However, the rehabilitation effect of current robots is still limited partly because a single training of motor function can be strongly affected by the decreased blood supply function of the bedridden patients. This work proposed an approach to study the coupling relationship between the motor and blood supply functions by combining the synchronously recorded EEG and cerebral blood oxygen information, where the cerebrations in different movement paradigms were analyzed from an aspect of "functional coupling". The results show that the information of oxyhemoglobin concentration change (ΔHbO) can effectively indicate the cortex activation, and a stronger blood supply is needed in cortexes to perform body movements. The correlations within motor cortexes are significantly stronger than the ones between motor and prefrontal cortexes, and a higher resistance level of extremity training will cause stronger correlations. Calculation of transfer entropy (TE) shows that there exists a bidirectional information transmission between the electrophysiological and blood supply signals, and more information is transmitted always in the direction from ΔHbO to EEG than in the opposite direction. The information transmission or the coupling relationship can be significantly enhanced by extremity movements, and large TE values are achieved in the Theta, Beta and Gamma frequency bands of EEG that correspond to motor functions. This work has demonstrated the functional coupling between nerve and blood microcirculation, which would provide a technical guidance to improve the rehabilitation effect for current robot systems and have great application potentials.

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Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing automation

arXiv

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arXiv 2024年

作者： Wu, Wansen Yang, Weiyi Li, Juanjuan Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Peng, Yong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China Hunan Institute of Advanced Technology Hunan Province Changsha410073 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

—The precise characterization and modeling of Cyber-Physical-Social systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the "6A-goal" of ACS and propose potential avenues for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

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Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

Batch Normalization Masked Sparse Autoencoder for Robotic Gr...

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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Zhenzhou Shao Ying Qu Guangli Ren Guohui Wang Yong Guan Zhiping Shi Jindong Tan Beijing Advanced Innovation Center for Imaging Technology and Beijing Key Laboratory of Light Industrial Robot and Safety Verification Capital Normal University Beijing China The Engineering College The University of Tennessee Knoxville TN USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95.51% for robotic grasping detection.

关键词： Training Redundancy Grasping Detectors Feature extraction intelligent robots Load modeling

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A Novel Remaining Useful Life Prediction Model Based on Adaptive Feature Fusion and its Application to Rolling Bearings

SSRN

引用

SSRN 2024年

作者： Guo, Kai Ma, Jun Wu, Jiande Xiong, Xin Faculty of Information Engineering & Automation Kunming University of Science and Technology Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming650500 China

As a key component in the transmission system of high-speed trains, once a failure occurs, it will directly affect the safety of train operation. Therefore, it is of great significance to construct a degradation index that can comprehensively characterize the running state of bearings and establish a reliable remaining useful life model to ensure the safety of train operation. Aiming at the problems that the redundant features in the existing multi-feature fusion process affect the diagnosis performance and the spurious fluctuations phenomenon of the fusion features causes the inaccurate determination of the start fault time and leads to the low prediction accuracy of remaining useful life (RUL), A method based on adaptive feature fusion and autoregressive integrated moving average model is proposed for rolling bearings prediction. The research in this study demonstrates that by comparing and verifying with two sets of public data, the proposed method is effective in accurately identifying the features that are most sensitive to degradation trends, accurately determine the initial fault moment of bearings, achieve effective prediction of the RUL of bearings, and the related research results can provide reference basis for predictive maintenance of train bearings. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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