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

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Preface

Advanced Textbooks in Control and Signal Processing

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Advanced Textbooks in control and Signal Processing 2019年 ix-x页

作者： Keviczky, László Bars, Ruth Hetthéssy, Jenő Bányász, Csilla Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary

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Preface

Advanced Textbooks in Control and Signal Processing

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Advanced Textbooks in control and Signal Processing 2019年 xi-xiii页

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RF chain reduction for MIMO systems: A hardware prototype

arXiv

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arXiv 2019年

作者： Gong, Tierui Shlezinger, Nir Ioushua, Shahar Stein Namer, Moshe Yang, Zhijia Eldar, Yonina C. State Key Lab of Robotics Key Lab of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel School of Electrical Engineering Tel Aviv University Tel Aviv Israel Faculty of Electrical Engineering Technion Haifa Israel

RF chain circuits play a major role in digital receiver architectures, allowing passband communication signals to be processed in baseband. When operating at high frequencies, these circuits tend to be costly. This increased cost imposes a major limitation on future multiple-input multiple-output (MIMO) communication technologies. A common approach to mitigate the increased cost is to utilize hybrid architectures, in which the received signal is combined in analog into a lower dimension, thus reducing the number of RF chains. In this work we present a hardware prototype implementing analog combining for RF chain reduction. The prototype consists of a specially designed configurable combining board as well as a dedicated experimental setup. Our hardware prototype allows to evaluate the effect of analog combining in MIMO systems using actual communication signals. The experimental study, which focuses on channel estimation accuracy in MIMO channels, demonstrates that using the proposed prototype, the achievable channel estimation performance is within a small gap in a statistical sense from that obtained using a costly receiver in which each antenna is connected to a dedicated RF chain. Furthermore, in the considered scenarios, this gap becomes negligible when the reduction rate, i.e., the ratio of the number of RF chains to the number of antennas, is above 62.5%. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel estimation

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Residential Energy Management with Deep Reinforcement Learning

Residential Energy Management with Deep Reinforcement Learni...

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International Joint Conference on Neural Networks

作者： Zhiqiang Wan Hepeng Li Haibo He Department of Electrical Computer and Biomedical Engineering University of Rhode Island RI USA Lab. of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

A smart home with battery energy storage can take part in the demand response program. With proper energy management, consumers can purchase more energy at off-peak hours than at on-peak hours, which can reduce the electricity costs and help to balance the electricity demand and supply. However, it is hard to determine an optimal energy management strategy because of the uncertainty of the electricity consumption and the real-time electricity price. In this paper, a deep reinforcement learning based approach has been proposed to solve this residential energy management problem. The proposed approach does not require any knowledge about the uncertainty and can directly learn the optimal energy management strategy based on reinforcement learning. Simulation results demonstrate the effectiveness of the proposed approach.

关键词： Batteries computer architecture Energy management Load modeling Microprocessors Feature extraction

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LoRa on the Move: Performance Evaluation of LoRa in V2X Communications

LoRa on the Move: Performance Evaluation of LoRa in V2X Comm...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Li, Yuke Han, Shuangshuang Yang, Linyao Wang, Fei-Yue Zhang, Hui State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Qingdao Huituo Intelligent Machine Company Qingdao China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Computer Science Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Conviva Inc. Silicon ValleyCA94404 United States

ISBN: (纸本)9781538644522

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks. © 2018 IEEE.

关键词： Monte Carlo methods

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Neural Network Adaptive Critic control With Disturbance Rejection 29

Neural Network Adaptive Critic Control With Disturbance Reje...

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第29届中国控制与决策会议

作者： Ding Wang Chaoxu Mu Derong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Electrical and Information Engineering Tianjin University School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509046584

A neural-network-based adaptive critic control method is established for continuous-time input-affine uncertain nonlinear systems to achieve disturbance *** present problem can be formulated as a two-player zero-sum differential game and the adaptive critic mechanism is employed to solve the minimax optimization problem.A neural network identifier is developed to reconstruct the unknown dynamical *** optimal control law and the worst-case disturbance law are designed by introducing and training a critic neural *** effectiveness of the present self-learning control method is also illustrated by a simulation experiment.

关键词： Adaptive critic control Adaptive dynamic programming Approximate dynamic programming Disturbance rejection Learning systems Neural networks Nonlinear control

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Relaying Strategy Based on Estimated Information for Multi-Antenna Cooperative Networks

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China Communications 2017年第8期14卷 157-165页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijingChina and Qingdao Academy of Intelligent Industries School of Computer Engineering Weifang University Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and Research Center of Computational Experiments and Parallel SystemsThe National University of Defense Technology

A sphere-based list forwarding scheme for multiple-input multiple-output(MIMO) relay networks is proposed and analyzed. Firstly, an estimate forwarding(EF) method is proposed, which forwards the minimum mean squared error(MMSE) estimate of the source data to the destination. Since it performs like amplify-and-forward(AF) and decode-and-forward(DF) for the low and high signal-to-noise ratio(SNR) regions, respectively, the EF relay thus outperforms conventional AF and DF across all SNRs without the need for switching algorithms for different SNRs. Because computational complexity is however high for relays with a large number of antennas(large MIMO) and/or high order constellations, list EF for large MIMO relay networks is proposed. It computes a list sphere decoder based MMSE estimate and retains the advantages of the exact EF relay at a negligible performance loss. The proposed list EF could offer a flexible trade-off between the performance and computational complexity.

关键词： MIMO system cooperative network soft information

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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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Hybrid flow shop scheduling with finite buffers

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International Journal of Simulation and Process Modelling 2018年第2期13卷 156-166页

作者： Han, Zhonghua Sun, Yue Ma, Xiaofu Lv, Zhe Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China Department of Digital Factory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Virginia Tech Blacksburg United States

In this paper, the scheduling problem for hybrid flow shop is investigated with the consideration of the finite buffers. Different from the existing works which focus on the makespan minimisation under the constraints of given intermediate buffer sizes, this paper investigates how to decide the reasonable size of the intermediate buffer. Firstly, the problem is modelled with the design of the accurate and reasonable buffer space without affecting the flow shop production efficiency. Then, a hybrid heuristic method is proposed to reduce the algorithm complexity. Specifically, the buffer size is estimated based on the theory of probability distribution, and the searching for the global optimal solution is processed by using a novel and effective self-adaptive differential evolution algorithm. The proposed algorithm can adjust parameters intelligently for stopping unnecessary iterations as well as avoiding the stagnant situation of the local optimum. Lastly, a wide range of practical scenarios are considered for algorithm evaluation, and the numerical results show that the proposed approach is effective on: 1) reducing the buffer size;2) guaranteeing the hybrid flow shop efficiency. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Probability distributions

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