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Synchronized Cell-Balancing Charging of Supercapacitors

IFAC-PapersOnLine 2017年第1期50卷 3338-3343页

作者： Li H. Peng J. He J. Huang Z. Pan J. School of Information Science and Engineering Central South University Changsha 410087 China Department of Computer Science University of Victoria Victoria V8P5C2 Canada Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

As a classical cell balancing solution in low-power supercapacitor applications, the switched resistor circuit is vulnerable to resistance deviation effects with existing cell balancing methods. In this paper, we propose a synchronized cell balancing method for switched resistor circuits based on the consensus theory and a cyber-physical approach, with the aim of synchronizing cell voltages before they are fully charged. It is shown that the proposed cell-balancing algorithm is robust to resistance deviation effects. Extensive simulations are provided to show the effectiveness of the proposed method. © 2017

关键词： Cell balancing Charging Consensus Cyber-physical systems Supercapacitors

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A Location Strategy for Traffic Emission Remote Sensing system Based on Bus Routes 36

A Location Strategy for Traffic Emission Remote Sensing Syst...

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第36届中国控制会议

作者： Yuxiao Yang Zerui Li Yu Kang Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a novel location strategy for traffic emission remote sensing system（TERSS） based on bus *** the purpose of reducing cost,the corresponding Hypergraph Model is established based on graph theory and the topological structure of urban road ***,the location problem of traffic emission remote sensing detectors is defined and transformed into finding the minimum transversal of the Hypergraph which is used to obtain the location scheme for TERSS based on bus routes according to Boolean algebra ***,the proposed location strategy helps to obtain a location scheme for a city bus system to monitor buses as many as possible.

关键词： Location strategy TERSS bus routes Boolean algebra

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An optimal density peak algorithm based on data field and information entropy 17

An optimal density peak algorithm based on data field and in...

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2017 International Conference on Data Mining, Communications and information Technology, DMCIT 2017

作者： Tao, Ling Li, Wenjie Jin, Yu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan China

ISBN: (纸本)9781450352185

The manual selection of threshold dc and cluster centers are the big limitations of the clustering by fast search and find of density peaks algorithm (DPC). In this paper, the data field theory was introduced to adaptively select the threshold dc. The selection of cluster centers was regarded as the segmentation of the dataset that contains positive and negative samples. The information entropy was calculated to measure the purity of the dataset after each segmentation. Segmentation with the maximum entropy reduction divided all the cluster centers into the positive sample set, thus avoiding the errors cause by operating the Decision Graph manually. Comparison of clustering results on the artificial datasets shows that the proposed method (F-DPC) can choose the threshold dc reasonably according to the distribution of datasets and is robust to noise. Evaluating indicators in the real-life experiments exhibit that the proposed method can accurately determine the number of clusters and gain higher clustering accuracy. © 2017 Association for Computing Machinery.

关键词： Clustering algorithms

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A high-accuracy interferometric SAR images registration strategy combined with cross correlation and spectral diversity

A high-accuracy interferometric SAR images registration stra...

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2017 International Conference on Radar systems, Radar 2017

作者： Wang, Zhen Yang, Yanjiao Ding, Zegang Lin, Sheng Zhang, Tianyi Lv, Zheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Spacecraft System Engineering CAST Beijing100094 China

ISBN: (纸本)9781785615153

For the image registration of interferometric SAR, there is a contradiction between the registration accuracy and the robustness based on the traditional cross correlation and spectral diversity methods. To eliminate the contradiction, this paper proposes an image registration strategy combined with the respective advantages of above two methods. Its improvements are summarized as follows. Firstly, it divides the image registration process into two main steps. Step one is oriented to the image registration in the range direction based on cross correlation to enhance the robustness. Step two is oriented to the image registration in the azimuth direction based on spectral diversity to ensure the registration accuracy. Secondly, a method for detecting and correcting the singular values in the range pixels offsets is proposed in step one. It can reduce the impact of low-correlation regions. Finally, a hybrid filtering method is introduced for the azimuth pixels offsets in step two to reduce the impact of phase noise. Airborne dual-antenna data are employed to validate the effectiveness of the proposed strategy. © 2017 Institution of Engineering and Technology. All rights reserved.

关键词： Interferometry

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Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

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Science China(information Sciences) 2016年第2期59卷 217-231页

作者： Zhi ZHANG Jianxun LI Liu LIU Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications

Low energy consumption and limited power supply are significant factors for wireless sensor networks(WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore,quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.

关键词： data fusion distributed state estimation target tracking Kalman filtering quantization wireless sensor networks

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A Novel Experience-Based Exploration Method for Q-Learning

A Novel Experience-Based Exploration Method for Q-Learning

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Bohong Yang Hong Lu Baogen Li Zheng Zhang Wenqiang Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Shanghai Engineering Research Center for Video Technology and System School of Computer ScienceFudan University School of Computer Science New York University Shanghai

Reinforcement learning algorithms are used to deal with a lot of sequential problems, such as playing games, mechanical control,and so on. Q-Learning is a model-free reinforcement learning *** traditional Q-learning algorithms, the agent stops immediately after it has reached the goal. We propose in this paper a new method—Experience-based Exploration method—in order to sample more efficient state-action pairs for Q-learning updating. In the Experience-based Exploration method, the agent does not stop and continues to search the states with high bellman-error inversely. In this setting, the agent will set the terminal state as a new start point, and generate pairs of action and state which could be useful. The efficacy of the method is proved analytically. And the experimental results verify the hypothesis on Gridworld.

关键词： Inverse exploration Reinforcement learning Q-learning Deep learning Deep Q-learning Network

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Task-space cooperative tracking control of multi-robot systems with unknown parameters and time delays

Task-space cooperative tracking control of multi-robot syste...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Xinwu Liang Hesheng Wang Yun-Hui Liu Weidong Chen Le Xie The School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China National Digital Manufacturing Technology Center Shanghai Jiao Tong University Shanghai China

In this paper, the task-space cooperative tracking control problem of multi-robot systems on strongly connected digraphs is studied. To solve such synchronization problem, a novel adaptive control approach is proposed by introducing task-space synchronization errors between neighboring robotic systems into the task-space reference velocity/acceleration. To cope with unknown kinematic and dynamic parameters, adaptive laws are developed without explicitly incorporating synchronization terms. Lyapunov stability theory is used to rigorously prove asymptotical convergence of the task-space synchronization and tracking errors by fully considering the time delays. Finally, simulation results are given to illustrate the performance of the proposed task-space cooperative tracking control approach.

关键词： Task analysis Robot kinematics Kinematics Multi-robot systems Synchronization Aerospace electronics

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Alternative metrics correlations: Do academic reviews correlate with library holdings? 16

Alternative metrics correlations: Do academic reviews correl...

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16th International Conference on Scientometrics and Informetrics, ISSI 2017

作者： Qingqing, Zhou Shenghui, Wang Chengzhi, Zhang Nanjing University of Science and Technology Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China OCLC Research Leiden Netherlands

Existing researches have proved that both academic reviews and library holdings can be alternative sources to assess impacts of academic books. This paper endeavors to identify correlations between the two sources, which may be useful for book ordering of libraries. Specifically, 69,263 academic reviews in Choice {Choice: Current Reviews for Academic Libraries) are collected with four metrics: recommendation levels, readership levels, numbers of interdisciplinary subjects and review contents. Then, a topic model is used to extract topics from review contents. Meanwhile, library holdings of each book are identified, including the total number of library holdings, holding regions and holding distributions based on an entropy method. Finally, correlation analysis between Choice reviews and library holdings are conducted. Experimental results reveal that books with higher recommendation levels or extensive readerships tend to be ordered more by academic libraries. Meanwhile, books with extensive readerships will be collected more uniformly by academic libraries. In conclusion, metrics derived from Choice academic book reviews can be used as indicators to recommend book ordering of academic libraries.

关键词： Libraries

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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Eigen Entropy Based Optimal Neighborhood Selection in Point CloudFeature Extraction and Description

Eigen Entropy Based Optimal Neighborhood Selection in Point ...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Jiajie Tan Guanhao Wang Ning Li Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

Feature extraction of 3 D space is of great significance in the field of computer vision and surface reconstruction,and the traditional method is to estimate and extract feature based on local 3 D *** to enhance the adaptability and accuracy of the method to various 3 D scenes,we propose an enhanced feature extraction method relies on eigen entropy within an adaptive local *** method will self-adaptively and efficiently select the optimal neighborhood to extract feature by calculating and analyzing eigen entropy among several 3 D *** validate our new method,we choose some point clouds model and extract their features descriptors using our enhanced *** results indicate that our method is efficient and accurate.

关键词： Point Cloud Registration self-adaption eigen entropy local neighborhood

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