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15th International Workshop on Variable Structure Systems, VSS 2018

作者： Nateghi, Shamila Shtessel, Y. Barbot, J.-P. Zheng, Gang Yu, Lei University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States EA7393 ENSEA Non-A INRIA QUARTZ Laboratory Cergy-Pontoise95014 France QUARTZ Laboratory EA7393 ENSEA 6 Avenue du Ponceau Cergy-Pontoise and Non-A95014 France INRIA-Lille Nord Europe Villeneuve d'Ascq Cedex 59650 France School of Electronic Information Wuhan University Signal Processing Laboratory Wuhan430072 China

ISBN: (纸本)9781538664391

In this work, the unknown cyber-attacks on cyber-physical systems are reconstructed using sliding mode differentiation techniques in concert with the sparse recovery algorithm, when only several unknown attacks out of a long list of possible attacks are considered non-zero. The approach is applied to a model of the electric power system, and finally, the efficacy of the proposed techniques is illustrated via simulations of a real electric power system. © 2018 IEEE.

关键词： Electric power transmission networks

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NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

arXiv

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arXiv 2020年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Ahn, Namhyuk Bai, Dongwoon Cai, Jie Cao, Yun Chen, Junyang Cheng, Kaihua Chun, SeYoung Deng, Wei El-Khamy, Mostafa Man Ho, Chiu Ji, Xiaozhong Kheradmand, Amin Kim, Gwantae Ko, Hanseok Lee, Kanghyu Lee, Jungwon Li, Hao Liu, Ziluan Liu, Zhi-Song Liu, Shuai Lu, Yunhua Meng, Zibo Navarrete Michelini, Pablo Micheloni, Christian Prajapati, Kalpesh Ren, Haoyu Hyeok Seo, Yong Siu, Wan-Chi Sohn, Kyung-Ah Tai, Ying Muhammad Umer, Rao Wang, Shuangquan Wang, Huibing Haoning Wu, Timothy Wu, Haoning Yang, Biao Yang, Fuzhi Yoo, Jaejun Zhao, Tongtong Zhou, Yuanbo Zhuo, Haijie Zong, Ziyao Zou, Xueyi Wang, Li-Wen Cani, Marie-Paule Siu, Wan-Chi Yang, Huan Fu, Jianlong Shi, Yukai Chen, Junyang Lee, Kanghyu Park, Jaihyun Lee, Junyeop Min, Jeongki Lee, Bokyeung Ko, Hanseok Yoo, Jaejun Sohn, Kyung-Ah Micheloni, Christian Hu, Fengshuo Wang, Yanhong Lu, Yunhua Peng, Jinjia Wang, Huibing Zhuo, Haijie Lee, Junyeop Min, Jeongki Lee, Bokyeung Park, Jaihyun Ko, Hanseok Tai, Lab Ying Jilin Li, Youtu Lab Liu, Shuai Yang, Biao Liu, Xing Chen, Shuaijun Zhao, Lei Wang, Zhan Lin, Yuxuan Jia, Xu Gao, Qinquan Deng, Wei Kheradmand, Amin El-Khamy, Mostafa Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Patel, Heena Chudasama, Vishal Upla, Kishor Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Meng, Zibo Ho, Chiu Man Computer Vision Lab ETH Zurich Switzerland LIX - Computer science laboratory at the cole polytechnique Palaiseau France Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong Shanghai Jiao Tong University China Microsoft Research Beijing China Guangdong University of Technology China Intelligent Signal Processing Laboratory Korea University Korea Republic of Ajou University Korea Republic of EPFL University of Udine Italy BOE Technology Group Co. Ltd Dalian Maritime University China Guangdong OPPO Mobile Telecommunications Corp. Ltd Department of Video Information Processing Korea University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Tencent Youtu Lab Yun Cao Tencent Youtu Tencent Youtu Lab Chengjie Wang Tencent Tencent Youtu Lab Feiyue Huang Tencent Youtu Lab Peking University China North China University of Technology China Huawei Technologies Co. Ltd China Fuzhou University Tong Tong Fuzhou University Imperial Vision Technology China Fuzhou University Imperial Vision Technology China Imperial Vision Technology SOC R&D Samsung Semiconductor Inc. United States Ulsan national institute of science and technology Korea Republic of Sardar Vallabhbhai National Institute Of Technology Surat India Norwegian University of Science and Technology Gjøvik Norway InnoPeak Technology

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r.t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Preventive Soot Blowing Strategy based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy based on State of Health Pr...

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第37届中国控制会议

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering North University of China School of Computer Science and Technology North University of ChinaSchool of Computer Science and Technology Shanghai Jiao Tong University Department of Automationand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal-fired power plant boiler.A preventive soot blowing strategy based on state of health prediction for heat transfer surface was *** average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing *** to the theory of renewal process,an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was *** using Particle Swarm Optimization（PSO）,the optimal preventive cycle and soot-blowing threshold were obtained,and the long-run average cost rate was the *** model is validated with experiment data of a 300 MW coal-fired power plant boiler and the parameters of prediction model are *** results can verify the feasibility of proposed *** can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Coal-fired Boiler Heat Transfer Surface Soot-blowing State of Health（SOH） Prediction

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Author Correction: Frontotemporal coordination predicts working memory performance and its local neural signatures

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Nature communications 2021年第1期12卷 3811页

作者： Ehsan Rezayat Mohammad-Reza A Dehaqani Kelsey Clark Zahra Bahmani Tirin Moore Behrad Noudoost School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. dehaqani@ut.ac.ir. Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran. dehaqani@ut.ac.ir. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. Department of Biomedical Engineering Tarbiat Modares University Tehran Iran. Department of Neurobiology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. behrad.noudoost@utah.edu.

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Person Re-identification by Integrating Static Texture and Shape Cues 1

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12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Madongo, Canaan Tinotenda Huang, Di Chen, Jiaxin Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (数字)9783319699233

ISBN: (纸本)9783319699226

Person Re-Identification (Re-ID) is a challenging task with wide ranging applications in various fields. This paper presents a novel hand-crafted method for this issue, enhancing the state of the art ones in literature in two aspects. In contrast to most current studies that analyze texture features, we propose a discriminative and compact shape feature by applying Procrustes shape analysis. It not only retains shape distinctiveness of an individual sample, but also alleviates cross-view impacts. Furthermore, we combine the shape feature with some current popular texture features, namely LOMO and mid-level filters, so that the advantages of multiple clues can be jointly used. A score level fusion strategy is finally adopted to optimally integrate their credits. Evaluated on two public benchmarks, i.e. VIPeR and CUHK03, the proposed method achieves very competitive results, indicating its effectiveness in person Re-ID. © 2017, Springer International Publishing AG.

关键词： computers

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Preventive Soot Blowing Strategy Based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy Based on State of Health Pr...

引用

Chinese Control Conference (CCC)

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering Taiyuan China North University of China School of Computer Science and Technology Taiyuan China Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal- fired power plant boiler. A preventive soot blowing strategy based on state of health prediction for heat transfer surface was proposed. The average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing strategy. According to the theory of renewal process, an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was established. By using Particle Swarm Optimization (PSO), the optimal preventive cycle and soot-blowing threshold were obtained, and the long-run average cost rate was the lowest. The model is validated with experiment data of a 300MW coal-fired power plant boiler and the parameters of prediction model are obtained. The results can verify the feasibility of proposed strategy. It can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Thermal engineering Radio frequency Coal Combustion Heat engines Heat transfer

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A Geometric Approach to Second-Order Consensus of Heterogeneous Networked Systems

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IEEE Transactions on Cybernetics 2018年 2018 Jan 9页

作者： Su, Housheng Ye, Yanyan Chen, Xia He, Haibo School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China. China China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA. United States

This paper investigates second-order consensus of networked systems with heterogeneous intrinsic nonlinear dynamics via a geometrical method, in which the nonlinear dynamics are governed by both velocity and position. First, two necessary conditions are deduced for the existence of consensus solutions by analyzing the inherent nonlinear dynamics of isolated nodes. Then a closed invariant set is constructed via geometrical methods. The nonempty of the set implies the existence of consensus solution. Assuming that the primary and the second dimension about the above invariant set are the same linear subspace, the system can be divided into two simple subsystems through a linear transformation. In addition, some sufficient conditions are proposed for reaching the global consensus based on matrix theory and Lyapunov method. Finally, numerical simulation results are provided to illustrate the validity of theoretical analysis. IEEE

关键词： Multi agent systems

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Social cognitive optimization with tent map for combined heat and power economic dispatch

arXiv

引用

arXiv 2018年

作者： Sun, Jiaze Li, Yang School of Computer Science and Technology Xi 'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi 'an University of Posts and Telecommunications Xi 'an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Combined Heat and Power Economic Dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel Social Cognitive Optimization algorithm with Tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, Adaptive Constraints Relaxing (ACR) rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by carrying out two typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Functions

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Automatic generation of fast BLAS3-GEMM: A portable compiler approach 17

Automatic generation of fast BLAS3-GEMM: A portable compiler...

引用

International Symposium on Code Generation and Optimization (CGO)

作者： Xing Su Xiangke Liao Jingling Xue College of Computer National Laboratory for Parallel and Distributed Processing Changsha China UNSW School of Computer Science and Engineering Sydney NSW Australia

ISBN: (纸本)9781509049318

GEMM is the main computational kernel in BLAS3. Its micro-kernel is either hand-crafted in assembly code or generated from C code by general-purpose compilers (guided by architecture-specific directives or auto-tuning). Therefore, either performance or portability suffers. We present a POrtable Compiler Approach, Poca, implemented in LLVM, to automatically generate and optimize this micro-kernel in an architecture-independent manner, without involving domain experts. The key insight is to leverage a wide range of architecture-specific abstractions already available in LLVM, by first generating a vectorized micro-kernel in the architecture-independent LLVM IR and then improving its performance by applying a series of domain-specific yet architecture-independent optimizations. The optimized micro-kernel drops easily in existing GEMM frameworks such as BLIS and OpenBLAS. Validation focuses on optimizing GEMM in double precision on two architectures. On Intel Sandybridge and AArch64 Cortex-A57, Poca's micro-kernels outperform expert-crafted assembly code by 2.35% and 7.54%, respectively, and both BLIS and OpenBLAS achieve competitive or better performance once their micro-kernels are replaced by Poca's.

关键词： Kernel Optimization computer architecture Linear algebra Libraries Programming Program processors

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Deep network for simultaneous decomposition and classification in UWB-SAR imagery

arXiv

引用

arXiv 2018年

作者： Vu, Tiep H. Nguyen, Lam Guo, Tiantong Monga, Vishal School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States RF Signal Processing and Modeling Branch U.S. Army Research Laboratory 2800 Powder Mill Rd AdelphiMD20783

Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issue regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Ultra-wideband (UWB)

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