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arXiv 2019年

作者： Čičić, Mladen Jin, Li Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Malvinas väg 10 Stockholm10044 Sweden Department of Civil and Urban Engineering C2SMART University Transportation Center New York University Tandon School of Engineering 15 MetroTech Center BrooklynNY11201 USA United States

Truck platooning is a technology that is expected to become widespread in the coming years. Apart from the numerous benefits that it brings, its potential effects on the overall traffic situation needs to be studied further, especially at bottlenecks and ramps. However, assuming we can control the truck platoons from the infrastructure, they can be used as controlled moving bottlenecks, actuating control actions on the rest of the traffic. Thus properly controlled platoons can possibly improve the efficiency and throughput of the whole system. In this paper, we use a multi-class cell transmission model to capture the interaction between truck platoons and background traffic, and propose a corresponding queuing model, which we use for control design. As control inputs, we use platoon speeds, and the number of lanes platoons occupy, and we devise a control strategy for throughput improvement. This control law is applied on a highway section upstream of a bottleneck, with one on- ramp and one off-ramp. By postponing and shaping the inflow to the bottleneck, we are able to keep it in free flow, avoiding traffic breakdown and capacity drop, leading to significant reduction of total time spent of all vehicles. We tested the proposed control in a simulation study and found that by applying, we reduce the median delay of all vehicles by 75:6%. Notably, although they are slowed down while actuating control actions, platoons experience less delay compared to the case without control, since they avoid going through congestion at the bottleneck. Copyright © 2019, The Authors. All rights reserved.

关键词： Queueing theory

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White paper on broadband connectivity in 6G

arXiv

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Björnson, Emil Bourdoux, André Buzzi, Stefano Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, MohammadJavad Sarieddeen, Hadi Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland Linköping University Sweden IMEC Belgium Department of Electrical and Information Engineering University of Cassino and Southern Latium Italy CEA-Leti France Department of Electrical and Electronics Engineering Kadir Has University Turkey Institute of Telecommunications and Multimedia Applications Universitat Politecnica de Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Department of Electrical and Computer Engineering Institute for the Wireless Internet of Things Northeastern University United States Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering Chalmers University of Technology Sweden Department of Electrical and Computer Engineering National University of Singapore Singapore Huawei Technologies Germany

This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goals, not only the terrestrial networks will be evolved but they will also be integrated with satellite networks, all facilitating autonomous systems and various interconnected structures. We believe that several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric and scalable cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. The latter path will lead to THz communications complemented by visible light communications in specific scenarios. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latencies, higher reliability, and reduced complexity. Different options will be needed to optimally support different use cases. The resource efficiency can be further improved by using various combinations of full-duplex radios, interference management based on rate-splitting, machine-learning-based optimization, coded caching, and broadcasting. Finally, the three levels of enablers must be utilized not only to deliver better broadband services in urban areas, but also to provide full-coverage broadband connectivity must be one of the k

关键词： Visible light communication

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A winners-take-all incentive mechanism for crowd-powered systems 13

A winners-take-all incentive mechanism for crowd-powered sys...

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13th Workshop on the Economics of Networks, systems, and Computation, NetEcon 2018 - In conjunction with the ACM SIGMETRICS International Conference on Measurement and Modeling of computer systems, ACM SIGMETRICS 2018

作者： Jiang, Pengfei Wang, Weina Ying, Lei Zhou, Yao He, Jingrui School of Electrical Computer and Energy Engineering Arizona State University United States Coordinated Science Laboratory University of Illinois United States School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781450359160

This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment? Let ∈i denote the quality of the data/answer reported by individual i: In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i: Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution;and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case). © 2018 Copyright held by the owner/author(s).

关键词： Crowdsourcing

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An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： Complex network prisoner’s dilemma reinforcement learning temporal differences learning

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Collision-free trajectory design for 2D persistent monitoring using second-order agents

arXiv

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arXiv 2019年

作者： Wang, Yan-Wu Zhao, Ming-Jie Yang, Wu Zhou, Nan Cassandras, Christos G. School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Division of Systems Engineering Boston University BostonMA02446 United States Division of Systems Engineering Department of Electrical and Computer Engineering Boston University BostonMA02446 United States

—This paper considers a two-dimensional persistent monitoring problem by controlling movements of second-order agents to minimize some uncertainty metric associated with targets in a dynamic environment. In contrast to common sensing models depending only on the distance from a target, we introduce an active sensing model which considers the distance between an agent and a target, as well as the agent’s velocity. We propose an objective function which can achieve a collision-free agent trajectory by penalizing all possible collisions. Applying structural properties of the optimal control derived from the Hamiltonian analysis, we limit agent trajectories to a simpler parametric form under a family of 2D curves depending on the problem setting, e.g. ellipses and Fourier trajectories. Our collision-free trajectories are optimized through an event-driven Infinitesimal Perturbation Analysis (IPA) and gradient descent method. Although the solution is generally locally optimal, this method is computationally efficient and offers an alternative to other traditional time-driven methods. Finally, simulation examples are provided to demonstrate our proposed results. Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

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Actuator security indices based on perfect undetectability: Computation, robustness, and sensor placement

arXiv

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arXiv 2018年

作者： Milošević, Jezdimir Teixeira, André Johansson, Karl H. Sandberg, Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Signals and Systems Department of Engineering Sciences Uppsala University Uppsala Sweden

This paper proposes an actuator security index based on the definition of perfect undetectability. This index can help a control system operator to localize the most vulnerable actuators in a networked control system, which can then be secured. Particularly, the security index of an actuator equals the minimum number of sensors and actuators that needs to be compromised, such that a perfectly undetectable attack against that actuator can be conducted. A method for computing the index for small scale networked control systems is derived, and it is shown that the index can potentially be increased by placing additional sensors. The difficulties that appear once the system is of a large scale are then outlined: the problem of calculating the index is NP–hard, the index is vulnerable to system variations, and it is based on the assumption that the attacker knows the entire model of the system. To overcome these difficulties, a robust security index is introduced. The robust index can be calculated in polynomial time, it is unaffected by the system variations, and it can be related to both limited and full model knowledge attackers. Additionally, we analyze two sensor placement problems with the objective to increase the robust indices. We show that both of these problems have submodular structures, so their suboptimal solutions with performance guarantees can be obtained in polynomial time. Finally, the theoretical developments are illustrated through numerical examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Actuators

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Scoring the terabit/s goal: Broadband connectivity in 6G

arXiv

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Bicaïs, Simon Björnson, Emil Bourdoux, André Buzzi, Stefano D'Andrea, Carmen Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Makki, Behrooz Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, Mohammadjavad Sarieddeen, Hadi Shahabuddin, Shahriar Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland CEA-Leti France KTH Royal Institute of Technology Sweden Linköping University Sweden IMEC Belgium University of Cassino and Southern Latium Italy Italy Department of Electrical and Electronics Engineering Kadir Has University Turkey Fivecomm Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University United States Ericsson Research Gothenburg Sweden Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Nokia Finland Department of Electrical Engineering Chalmers University of Technology Sweden State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Huawei Technologies Germany

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. © 2020, CC BY.

关键词： MIMO systems

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Solving Lur'e equations through zeroing neural networks

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Information sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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An Edge Computing Platform of Guide-dog Robot for Visually Impaired

An Edge Computing Platform of Guide-dog Robot for Visually I...

引用

International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Jinhui Zhu Yinong Chen Mei Zhang Qiang Chen Ying Guo Huaqing Min Zhilie Chen School of Software Engineering South China University of Technology Guangzhou China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe USA School of Automation Science and Control Engineering South China University of Technology Guangzhou China College of computer & information science Southwest University Chongqing China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China EVOC Intelligent Technology Company Limited Shen zhen China

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

Information processing of guide dog robot requires expensive computing resources to meet real-time performance. We propose an edge computing framework based on Intel Up-Squared board and neural compute stick. Image processing and real-time control are performed on the framework. In addition, the voice recognition and commanding are also implemented, which are processed on Amazon cloud service. Vision, speech, and control services are integrated by ASU VIPLE (Visual IoT/Robotics Programming Language Environment). A prototype is developed, which implements the guide dog's obstacle avoidance, traffic sign recognition and following, and voice interaction with human.

关键词： Dogs Mobile robots Robot sensing systems Wheelchairs Edge computing Cameras

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