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第36届中国控制会议

作者： Yuan Zhou Siyu Xia Junkang Zhang Dandi Chen Key Laboratory of Measurement and Control of CSE School of AutomationSoutheast University Computer Science and Engineering Department University of Connecticut

ISBN: (纸本)9781538629185

Personal photographs shared in social network websites show a wide range of variations in illumination,pose and *** a result,conventional face recognition methods show poor performance in such uncontrolled *** resolve this problem,we explore incorporating associated social network context into visual face recognition *** by collaborative filtering technique,a new method for photo tagging task is proposed in this ***,a large-scale social network image dataset（SNRR-collection） is collected in this *** evaluated on SNRR-collection dataset demonstrate the effectiveness of our proposed model.

关键词： Photo tagging Face recognition Social Networks Collaborative filtering Recommender system

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Adaptive Neural Dynamic Surface Control for Flexible Joint Manipulator with Prescribed Performance 29

Adaptive Neural Dynamic Surface Control for Flexible Joint M...

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第29届中国控制与决策会议

作者： Min Wang Huiping Ye School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

ISBN: (纸本)9781509046584

This paper focuses on adaptive neural control for a class of flexible joint manipulator with unknown dynamics under the output tracking error *** facilitate the design of control law,the constrained tracking error is transformed into unconstrained variable by introducing a performance function,the unknown dynamics of manipulator are accurately approximated by radial basis function(RBF) neural network(NN).Then,a novel adaptive neural control scheme is proposed using the derivative of the filter's output as the input of NN instead of traditional intermediate *** to structure features of the considered flexible joint manipulator,the proposed control scheme not only decreases the dimension of NN inputs,but also reduces the number of NN ***,it can be verified that all the signals in the closed-loop system are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero with the prescribed *** results on a single-link flexible joint manipulator are given to illustrate the effectiveness of the proposed scheme.

关键词： Adaptive Neural Control Dynamic Surface Control Prescribed Performance Flexible Joint Manipulator

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The design of network pedometer based on Bluetooth 4.0

The design of network pedometer based on Bluetooth 4.0

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作者： Tang Meiyu Gao Tianlei Zhou Shuwang Zhao Kai Shandong Computer Science Center(National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks School of Electrical Engineering and Automation Qilu University of Technology

The pedometer combined with Bluetooth 4.0 can effectively implement the communication with the laptops,PAD,and mobile *** can more accurately calculate the number of pace through the method of real-time digital filter smoothing data curve,the design not only can enhance people's enthusiasm of taking exercise but also can effectively save communication *** of the Bluetooth technology is in the 2.4 GHz band which is free spectrum [1],Bluetooth technology is bound to get more widely applications and more recognition,it can effectively put the data recorded by pedometer sent to the mobile phone and other modern terminal devices through Bluetooth technology,and then the data is uploaded to the cloud server to be saved through the *** can increase people's interest in the pedometer,because the relevant data can be visible saved for a long time,it tracks more convenient for people to exercise and reach the effect of physical *** it is convenient for people to track and compare the physical activity to reach the effect of physical fitness.

关键词： Bluetooth 4.0 pedometer Real-time digital filtering Cloud server

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Neural Control of Robotic Manipulator with Guaranteed Tracking Precision and Velocity Constraints 36

Neural Control of Robotic Manipulator with Guaranteed Tracki...

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第36届中国控制会议

作者： Min Wang Yanwen Zhang School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

ISBN: (纸本)9781538629185

This paper focuses on adaptive neural control design for n-link robot manipulator systems in the presence of tracking error and velocity state constraints.A performance function is utilized to characterize the prescribed performance of the tracking error.A new transformed function is used to guarantee that tracking errors satisfy the prescribed performance and the velocity states are not to violate their *** designed transformed function is not only of simple structure,but also overcomes the possible high-gain control problem caused by the existing transformed ***,a novel adaptive neural control scheme is proposed by combining independently Lyapunov functions *** proposed scheme guarantees all closed-loop signals are uniformly ultimately bounded,the output tracking errors satisfy the prescribed transient and steady-state tracking performances,and the velocity states are ensured to remain in the predefined bounded *** studies on a 2-link robot are performed and the effectiveness of the proposed scheme is illustrated by simulation results.

关键词： Adaptive Neural Control Tracking Error Constraints State Constraints Performance Transformation Robot Manipulators

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A Lane-Changing Trajectory Planning and Assistant Decision-Making Method for Autonomous Vehicle 18

A Lane-Changing Trajectory Planning and Assistant Decision-M...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Cui, Zheyu Hu, Jianming Guan, Hongfei School of Computer Science Beijing Univ. of Posts and Telecommunications Beijing100876 China Dept. of Automation Tsinghua Univ. TNList Beijing100084 China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Aiming at shortcomings of the traditional decision-making models for lane changing, such as only considering the nearest time gaps, and using static gap models, overlook the dynamic characteristic of vehicles' gap. A new lane-changing decision-making model is put forward based on dynamic gap. By adopting an approach of depth-first search and the designed satisfactory function, the gap that meets the most satisfaction is selected for lanes changing. The second model uses the constraint optimization model on the basis of the gap obtained by the previous model. It then uses the primal dual interior point method based on the second order cone programming (SOCP) to obtain the optimal trajectory, after considering the comfort and traffic flow. Finally, the correctness, robustness, and linkage of the two models are verified through the design of typical lane changing scenarios and simulation experiments. © 2018 American Society of Civil Engineers.

关键词： Second-order cone programming

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CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer

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Genome biology 2025年第1期26卷 178页

作者： Qifei Wang He Zhu Yiwen Hu Yanjie Chen Yuwei Wang Guochao Li Yun Li Jinfeng Chen Xuegong Zhang James Zou Manolis Kellis Yue Li Dianbo Liu Lan Jiang China National Center for Bioinformation Beijing 100101 China. Beijing Institute of Genomics Chinese Academy of Sciences Beijing 100101 China. University of Chinese Academy of Sciences Beijing 100049 China. School of Computer Science McGill University Montreal QC H3A 0C6 Canada. Mila - Quebec AI Institute Montreal QC H2S 3H1 Canada. Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China. CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing 100101 China. Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China. International Big-Data Center for Depression Research Institute of Psychology Chinese Academy of Sciences Beijing 100101 China. Department of Automation MOE Key Laboratory of Bioinformatics and Bioinformatics Division BNRIST Tsinghua University Beijing 100084 China. School of Life Sciences and School of Medicine Center for Synthetic and Systems Biology Tsinghua University Beijing 100084 China. Department of Computer Science Stanford University Stanford CA 94305 USA. Department of Biomedical Data Science Stanford University School of Medicine Stanford CA 94305 USA. The Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. School of Computer Science McGill University Montreal QC H3A 0C6 Canada. yueli@cs.mcgill.ca. Mila - Quebec AI Institute Montreal QC H2S 3H1 Canada. yueli@cs.mcgill.ca. Quantitative Life Science McGill University Montreal QC H3A 1E3 Canada. yueli@cs.mcgill.ca. School of Medicine and College of Design and Engineering National University of Singapore Singapore Singapore. dianbo@nus.edu.sg. China National Center for Bioinformation Beijing 100101 China. jiangl@***. Beijing Institute of Genomics Chinese Academy of Sciences Beijing 100101 China. jiangl@***. University of Chinese Academy of Sciences Beijing 100049 C

Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalizability designed for the hierarchical interpretation of OOD cells. Without pre-training, CellMemory outperforms existing single-cell foundation models and accurately deciphers spatial transcriptomics at high resolution. Leveraging its robust representations, we further elucidate malignant cells and their founder cells across patients, providing reliable characterizations of the cellular changes caused by the disease.

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Co-design of event generator and synchronous controller for chaotic neural networks with quantization

Co-design of event generator and synchronous controller for ...

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Chinese Control Conference

作者： Liuwen Li Wenlin Zou Shumin Fei Yushun Tan Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing School of Automation Southeast University Nanjing

This paper studies co-design of event generator and synchronous controller for chaotic neural networks with quantization. A quantized event-triggered scheme is presented to reduce the load of network transmission. Sufficient stabilization conditions for synchronization error system are derived on the basis of Lyapunov-Krasovskii functional. Based on the above stability condition, a co-designed parameters of controller and the corresponding event-triggered parameters are obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control method.

关键词： Quantization (signal) Biological neural networks Synchronization Generators Neurons Electronic mail Chaotic communication

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IEEE Transactions on Evolutionary Computation Special Issue on Parallel Evolution for Large Scale Optimization

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IEEE Transactions on Evolutionary Computation 2018年第4期22卷 633-633页

作者： Wang, Fei-Yue Wei, Qinglai Tang, Ke Coello Coello, Carlos A. State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science CINVESTAV-IPN Mexico

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Hardware-efficient guided image filtering for multi-label problem

arXiv

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arXiv 2018年

作者： Dai, Longquan Yuan, Mengke Li, Zechao Zhang, Xiaopeng Tang, Jinhui School of Computer Science and Engineering Nanjing University of Science and Technology National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

The Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an n×n matrix for each pixel. To the best of our knowledge existing matrix inverse algorithms are inefficient on current hardwares. This shortcoming limits applications of multichannel guidance in computation intensive system such as multi-label system. We need a new GF-like filter that can perform fast multichannel image guided filtering. Since the optimal linear complexity of GF cannot be minimized further, the only way thus is to bring all potentialities of current parallel computing hardwares into full play. In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance. Specifically, in order to boost the filtering performance, HGF takes a new matrix inverse algorithm which only involves two hardware-efficient operations: element-wise arithmetic calculations and box filtering. In order to break the linear model restriction, HGF synthesizes a polynomial multichannel guidance to introduce nonlinearity. Benefiting from our polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not only is more sensitive to the underlying structure of guidance but also achieves the fastest computing speed. Due to these merits, HGF obtains state-of-the-art results in terms of accuracy and efficiency in the computation intensive multi-label systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Inverse problems

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