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International Conference on Information Fusion

作者： Qi Wang Zhansheng Duan X. Rong Li Uwe D. Hanebeck Center for Information Engineering Science Research Xi'an Jiaotong University Xi'an Shaanxi China Department of Electrical Engineering University of New Orleans New Orleans LA U.S.A Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

Source localization is of great importance for wireless sensor network applications. Locating emission sources using received signal strength (RSS) measurements is investigated in this paper. As RSS localization is a non-convex optimization problem, it is difficult to achieve global optima. Many optimization methods have been proposed to relax it to a convex optimization problem. Unlike these methods, we propose a convex combination scheme. By introducing a highly accurate linear approximation of a logarithmic function, the source location is represented by a convex combination of a set of virtual anchors. Then the original problem is relaxed to be a convex optimization problem of finding the optimal combination coefficients, which can be solved efficiently using constrained least squares. To obtain the virtual nodes, we construct parallel lines and use their intersections to form a convex polygon, which covers the source location with certain probability. The vertices of the polygon are taken as the virtual nodes. Numerical examples verify the performance of the proposed method in both localization accuracy and computational efficiency.

关键词： Optimization Position measurement Wireless communication Maximum likelihood estimation Robot sensing systems Wireless sensor networks Convex functions

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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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Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure

arXiv

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arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

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Spectral analysis of backbone networks against targeted attacks 13

Spectral analysis of backbone networks against targeted atta...

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13th International Conference on Design of Reliable Communication Networks, DRCN 2017

作者： Shatto, Tristan A. Çetinkaya, Egemen K. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States

ISBN: (纸本)9783800743834

Network science has been a central focus to correctly model and study resilience characteristics of communication networks. There have been many metrics used to represent connectivity of graphs;however, they do not suffice to compare networks with different numbers of nodes and links. The normalized Laplacian spectra enables network scientists to analyze network structures beyond what traditional graph metrics lacks. In this paper, we study the normalized Laplacian spectra of five backbone networks against targeted attacks. The physical and logical level of four commercial and one research backbone provider networks is studied. The intelligent attacks are modeled based on important graph centrality metrics of betweenness, closeness, and degree. Our results indicate that spectra of eigenvalues converge to zero after attacks. Moreover, we also identify that while in some scenarios different centrality-based attack strategies yield identical eigenvalue distribution, in other scenarios different attacks yield different eigenvalue distributions. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Spectrum analysis

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Barrier-certified adaptive reinforcement learning with applications to brushbot navigation

arXiv

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arXiv 2018年

作者： Ohnishi, Motoya Wang, Li Notomista, Gennaro Egerstedt, Magnus School of Electrical Engineering Royal Institute of Technology Stockholm11428 Sweden Georgia Robotics and Intelligent Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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An intelligent Gripper Design for Autonomous Aerial Transport with Passive Magnetic Grasping and Dual-Impulsive Release

An Intelligent Gripper Design for Autonomous Aerial Transpor...

引用

IEEE/ASME International Conference on Advanced intelligent Mechatronics

作者： Usman A. Fiaz M. Abdelkader Jeff S. Shamma Department of Electrical & Computer Engineering University of Maryland(UMD) College Park. MD USA KAUST The Robotics Intelligent Systems & Control (RISC) Laboratory Thuwal KSA

We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multi-rotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments in both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.

关键词： Grasping Force Servomotors Grippers Payloads Torque Magnetic separation

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Investigation of the Factors Influencing Wide Area Control

Investigation of the Factors Influencing Wide Area Control

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IEEE Power and Energy Society General Meeting

作者： Lazaros Zacharia Markos Asprou Elias Kyriakides Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks University of Cyprus

ISBN: (纸本)9781538622131;9781538622124

Various factors which can occur in the power system can potentially have a negative impact on the damping performance of a wide area controller, in case they are not properly considered. Such factors might be the measurement uncertainty of Phasor Measurement Units (PMUs), line parameters error, topology changes, and measurement delays of PMUs due to the communication network. Therefore, it is important to identify which factors are capable of affecting the wide area control (WAC) damping capabilities, in order to find ways to compensate their effect. In this paper, a case study is performed, where each factor is separately considered when a line is tripped and reclosed. The results indicate that data delays and topology changes are the factors which deteriorate the performance of the WAC. The simulations are based on the IEEE 39-bus dynamic test system.

关键词： Power systems Indexes Phasor measurement units Measurement uncertainty Topology Delays Economic indicators

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Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

arXiv

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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John S. Thompson: Technology Challenges in the Development of the First Single-Chip DSP

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IEEE Solid-State Circuits Magazine 2017年第2期9卷 21-24页

作者： Victor B. Lawrence Bryan Ackland Stevens Institute of Technology Center for Intelligent Networked Systems New Jersey Department of Electrical and Computer Engineering Stevens Institute of Technology

As we reflect on John Thompson's career, we realize how significant his achievements have been and recognize the impact he has had on our industry. His achievements span several decades.

关键词： Digital signal processing Laboratories Patents Speech processing Program processors computer architecture

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