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IEEE International Conference on Robotics and Biomimetics

作者： Liang Ren Zhiqiang Cao Min Tan Peng Zhao Xuechao Chen State Key Laboratory of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China The 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems BIT Beijing China

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance.

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A Hybrid of Hard and Soft Attention for Person Re-Identification

A Hybrid of Hard and Soft Attention for Person Re-Identifica...

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Chinese Automation Congress (CAC)

作者： Xuesong Li Yating Liu Kunfeng Wang Yong Yan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China State Grid Zhejiang Electric Power Company LTD Hangzhou China

Existing pedestrian re-identification methods based on deep learning have achieved good results under constrained conditions. However, there exist some challenges including large human pose variations, viewpoint changes, severe occlusions and imprecise detection of persons. So we present a Hard/Soft hybrid Attention Network (HSAN) that combines pose information and attention mechanism to deal with the challenges. Our model includes two main parts: Pose-guided Hard Attention (PHA) and Regional Soft Attention (RSA). PHA uses the keypoints generated by pose estimation to enhance the foreground information, and RSA is learned to eliminate the background clutter. We extract reliable features and locate discriminative regions by using these two modules to handle occlusions, pose changes and background noises. We conduct a lot of experiments on public datasets including DukeMTMC-ReID, Market-1501, and CUHK03, and the results show that our method achieves stateof-the-art performance.

关键词： Pose estimation Task analysis Noise measurement Feature extraction Reliability Machine learning

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Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation

arXiv

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arXiv 2019年

作者： Tian, Ning Fang, Huazhen Chen, Jian Wang, Yebin The Department of Mechanical Engineering University of Kansas LawrenceKS66045 United States The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Ningbo Research Institute Zhejiang University Ningbo315100 China The Mitsubishi Electric Research Laboratories CambridgeMA02139 United States

This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in the literature. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a battery’s nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two offline parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on the maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications. Copyright © 2019, The Authors. All rights reserved.

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Dynamically Optimized Sensor Deployment Based on Game Theory

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Journal of systems Science and Complexity 2018年第1期31卷 276-286页

作者： Zhang Fan Zheng Zhi Jiao Lei School of Automation Beijing Institute of Technology Beijing 100081 Decision of Complex Systems State Key Laboratory of Intelligent Control Beijing 100081

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

关键词： Connectivity dynamic deployment game theory target allocation

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A Facile Recovery Process for Cathodes from Spent Lithium Iron Phosphate Batteries by Using Oxalic Acid

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CSEE Journal of Power and Energy systems 2018年第2期4卷 219-225页

作者： Li Li Jun Lu Longyu Zhai Xiaoxiao Zhang Larry Curtiss Yi Jin Feng Wu Renjie Chen Khalil Amine School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Chemical Sciences and Engineering Division Argonne National LaboratoryIL 60439USA Material Science Division Argonne National LaboratoryIL 60439USA State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute.Beijing 100192China

A simplistic and novel leaching process is developed to dispose spent lithium iron phosphate(LiFePO_(4))*** this paper,oxalic acid is selected as a leaching reagent to recover lithium as a resource and remove phosphorus from LiFePO_(4) batteries,benefiting from its low natural *** physical properties of spent cathode materials(before leaching)and residues(after leaching)are identified by X-ray diffraction(XRD)and scanning electronic microscopy(SEM)with energy dispersive X-ray spectroscopy(EDS).The total amounts of Li and Fe are analyzed by inductively coupled plasma-atomic emission spectroscopy(ICP-AES).The process occurs under conditions of a 0.3 mol·L^(-1) oxalic acid concentration,a temperature of 80℃,a reaction time of 60 min and a solid/liquid ratio of 60 g·L^(-1).It can effectively precipitate92%ferrum in terms of FeC_(2)O_(4)·2H_(2)O from LiFePO4,with the leaching efficiency of Li achieving up to 98%.This method demonstrates a new strategy for dealing with spent lithium iron phosphate batteries with a low cost and low environmental impact.

关键词： Leaching oxalic acid precipitation spent lithium-ion batteries

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Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

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Chinese Physics B 2017年第3期26卷 268-275页

作者：宋睿卓魏庆来 School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration （PI） is introduced to solve the rain-max optimization problem. The off-policy adaptive dynamic programming （ADP） algorithm is then proposed to find the solution of the tracking Hamilton-Jacobi- Isaacs （HJI） equation online only using measured data and without any knowledge about the system dynamics. Critic neural network （CNN）, action neural network （ANN）, and disturbance neural network （DNN） are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded （UUB） of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem.

关键词： adaptive dynamic programming approximate dynamic programming chaotic system zero-sum

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From Parallel Plants to Smart Plants:Intelligent control and Management for Plant Growth

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 161-166页

作者： Mengzhen Kang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences (SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology IEEE State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

关键词： Agriculture Artificial intelligence Decision support systems Expert systems Sustainable development

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Blockchain technology Based Decentralized Energy Trading for Multiple-Microgrid systems

Blockchain Technology Based Decentralized Energy Trading for...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ting Li Wei Zhang Ning Chen Minhui Qian Ying Xu School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China EPRI Nanjing China Department of Electrical Engineering & Computer Science University of Central Florida Orlando USA

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach.

关键词： Peer-to-peer computing Microgrids Blockchain Distributed power generation Electronic commerce Power distribution economics Pricing

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RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network

arXiv

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arXiv 2019年

作者： Ni, Zhen-Liang Bian, Gui-Bin Xie, Xiao-Liang Hou, Zeng-Guang Zhou, Xiao-Hu Zhou, Yan-Jie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU. Copyright © 2019, The Authors. All rights reserved.

关键词： Surgical equipment

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Acoustic Source Tracking in Reverberant Environment Using A Novel PSO Particle Filter Framework

Acoustic Source Tracking in Reverberant Environment Using A ...

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Chinese Automation Congress (CAC)

作者： Jing Yang Xiuqin Shang Bin Hu Zhen Shen Gang Xiong Hui Wang College of Information Sciences and Technology Beijing University of Chemical Technology Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China

In this paper, several methods are combined together and applied to a Particle Filter based on Particle Swarm Optimization acoustic source tracking framework in order to solve some problems of sound source tracking. The Time Difference of Arrival (TDOA), which is extracted from the audio signal received by the microphone pair, makes observations. The Langevin Model is used to describe the speaker's motion characteristics. The pseudo-likelihood function is constructed by using the Global Coherence Field (GCF) function to update the particle weight, in order to solve problem of the phenomenon of spurious source due to noise and reverberation. According to the iterative process and particle flight conditions, the inertial weights of the particles are adjusted nonlinearly and dynamically. The concepts of superior flight speed and inferior flight speed are introduced to Particle Swarm Optimization, which helps jump out of local optimum and solves the problem of lack of particle diversity. Thus, the faster tracking of sound source is realized. In the framework, with fewer particles, the robustness of tracking is enhanced and accuracy of tracking is improved. Finally, the effectiveness of this framework for acoustic source tracking is verified through several Monte Carlo simulation experiments.

关键词： Microphones Particle filters Particle swarm optimization Tracking Coherence Reverberation

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