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IEEE Global Communications Conference

作者： Mingzhe Chen Omid Semiari Walid Saad Xuanlin Liu Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Colorado Colorado Springs CO USA Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

关键词： Wireless communication Wireless sensor networks Delays Uplink Downlink Prediction algorithms Machine learning

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Estimating position of bio electric potential dataset as a natural sensor using time series approach 1

Estimating position of bio electric potential dataset as a n...

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1st International Workshop on Cognition and Artificial Intelligence for Human-Centred Design, CAID 2017

作者： Tahyudin, Imam Nambo, Hidetaka Artificial Intelligence Laboratory Graduate School of Natural Science and Technology Division of Electrical Engineering and Computer Science Kanazawa University Japan Department of Information System STMIK AMIKOM Purwokerto Indonesia

The application of plants as natural sensors to detect human behavior is very interesting to investigate. One benefit is to know the position of elderly people living alone in a house in order to avoid accidents resulting in death because of not immediately helped. The previous authors already use some method to estimate the position such as classification and multilayer perceptron methods. However, to find the best estimation is so difficult. Therefore, this study tried to use time series approach to solve bioelectric potential dataset problem because the data type is numeric and data stored are based on time. The time series model which is used is Autoregressive (AR) model. The purpose of this study is to estimate the position based on the distance of training dataset to AR model. The result performed that the AR model selected is AR of order 3. In addition, the estimation accuracy is pretty good of 75% compared with the other methods, such as multi layer perceptron or decision tree. © 2018 CEUR-WS. All rights reserved.

关键词： Time series

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Enhancing sensitivity to ambient refractive index with tunable few-layer graphene/hBN nanoribbons

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Photonics Research 2019年第7期7卷 130-137页

作者： Huan Jiang Sajid Choudhury Zhaxylyk A.Kudyshev Di Wang Ludmila J.Prokopeva Peng Xiao Yongyuan Jiang Alexander V.Kildishev Institute of Modern Optics Department of PhysicsHarbin Institute of TechnologyHarbin 150001China School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue UniversityWest Lafayette Indiana 47907USA School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Key Laboratory of Micro-Optics and Photonic Technology of Heilongjiang Province Harbin 150001China Key Laboratory of Micro-Nano Optoelectronic Information System of Ministry of Industry and Information Technology Harbin 150001China

Refractive index (RI) sensing helps to identify biomolecules and chemicals in the mid-infrared range for drug discovery, bioengineering, and environmental monitoring. In this paper, we numerically demonstrate an electrically tunable RI sensor with ultrahigh sensitivity using a three-layer graphene nanoribbon array separated by hexagonal boron nitride (hBN). Unlike the weak resonance in single-layer graphene nanoribbons, a much stronger plasmon resonance featuring a higher-quality factor can be excited in the graphene/hBN few-layer ribbon array. Simultaneously, the high purity of graphene on hBN results in an outstanding charge mobility above 4 ×10^4 cm^2 · V^-1·s^-1 at 300 K, which allows a larger modulation depth. The interaction between the locally enhanced field around graphene ribbons and its surrounding analyte leads to ultrahigh sensitivity (4.207μm/RIU), with the figure of merit reaching approximately ***, this ultrasensitive detector could selectively work in different wavebands by controlling gate voltages applied to *** merits of ultrahigh sensitivity and electrical tunability are major advances compared to previous RI sensors, paving a way toward ultrasensitive detection using graphene/hBN few-layer devices.

关键词： Enhancing sensitivity refractive index nanoribbons

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Adaptive Fault-Tolerant Control of A Two-car High-speed Train Model with Inter-car Flexible Link and Traction Actuator Failures*

Adaptive Fault-Tolerant Control of A Two-car High-speed Trai...

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Asian Control Conference

作者： Zehui Mao Gang Tao Bin Jiang Xing-Gang Yan Yali Xue Ministry of Industry and Information Technology and Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA) Key Laboratory of Navigation Control and Health Management Technologies of Advanced Aircraft (NUAA) Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA School of Engineering and Digital Arts University of Kent Canterbury United Kingdom

ISBN: (纸本)9781728102634

This paper studies the adaptive fault-tolerant tracking control problem for the high-speed trains with intercar flexible link and traction actuator failures. This study is focused on a benchmark model which, as a main dynamic unit of the CRH (China Railway High-speed) train, is a two-car dynamic system with a flexible link between two cars, for which the input acts on the second car and the output is the speed of the first car. This model is under parameter uncertainties and subject to uncertain actuator failures. For such an underactuated system, to ensure the first car tracking a desired speed trajectory, a coordinate transformation method is employed to decompose the system model into a control dynamics subsystem and a zero dynamics subsystem. Stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is partially input-to-state stable. An adaptive fault-tolerant controller is developed which is able to ensure the closed-loop system signal boundedness and desired speed tracking, in the presence of the actuator failures and unknown system parameters. Simulation results from a realistic train dynamic model are presented to verify the effectiveness of the adaptive controller.

关键词： Automobiles Actuators Fault tolerance Fault tolerant systems Adaptation models Aerodynamics Adaptive systems

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Correction: After the pandemic: the global seafood trade market forecasts in 2030

Humanities and Social Sciences Communications

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Humanities and Social Sciences Communications 2023年第1期10卷 1页

作者： Chunzhu Wei Wei Chen Desheng Xue Yong Ge Mo Zhang Die Zhang Daoping Wang Qiuming Cheng Changxiu Cheng Wenguang Zhang School of Geography and Planning Sun Yat-sen University 510006 Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) 519080 Zhuhai China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences 100101 Beijing China College of Resources and Environment University of Chinese Academy of Sciences 100049 Beijing China Department of Computer Science and Technology University of Cambridge Cambridge CB3 0FD UK Department of Geography King’s College London London WC2R 2LS UK School of Earth Science and Engineering Sun Yat-Sen University 519000 Zhuhai China Key Laboratory of Environmental Change and Natural Disaster Beijing Normal University 100875 Beijing China Faculty of Geographical Science Beijing Normal University 100875 Beijing China School of Government Beijing Normal University 100875 Beijing China

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Greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhao, Shengjie Shi, Qingjiang Arce, Gonzalo R. College of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

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Pocket diagnosis: Secure federated learning against poisoning attack in the cloud

arXiv

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arXiv 2020年

作者： Ma, Zhuoran Ma, Jianfeng Miao, Yinbin Liu, Ximeng Choo, Kim-Kwang Raymond Deng, Robert H. School of Cyber Engineering Xidian University Xi’an710071 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States School of Information Systems Singapore Management University 188065 Singapore

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this paper, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called SFPA. Concretely, SFPA provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to defense DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of SFPA as well as its capability of resisting DI-level poisoning attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Power system state estimation via feasible point pursuit: Algorithms and Cramér-rao bound

Power system state estimation via feasible point pursuit: Al...

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作者： Wang, Gang Zamzam, Ahmed S. Giannakis, Georgios B. Sidiropoulos, Nicholas D. State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Accurately monitoring the system's operating point is central to the reliable and economic operation of an autonomous energy grid. Power system state estimation (PSSE) aims to obtain complete voltage magnitude and angle information at each bus given a number of system variables at selected buses and lines. Power flow analysis amounts to solving a set of noise-free power flow equations, and is cast as a special case of PSSE. Physical laws dictate quadratic relationships between available quantities and unknown voltages, rendering general instances of power flow and PSSE nonconvex and NP-hard. Past approaches are largely based on gradient-type iterative procedures or semidefinite relaxation (SDR). Due to nonconvexity, the solution obtained via gradient-type schemes depends on initialization, while SDR methods do not perform as desired in challenging scenarios. This paper puts forth novel feasible point pursuit (FPP)-based solvers for power flow analysis and PSSE, which iteratively seek feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS). Relative to the prior art, the developed solvers offer superior numerical performance at the cost of higher computational complexity. Furthermore, they converge to a stationary point of the WLS problem. As a baseline for comparing different estimators, the Cramér-Rao lower bound is derived for the fundamental PSSE problem in this paper. Judicious numerical tests on several IEEE benchmark systems showcase markedly improved performance of our FPP-based solvers for both power flow and PSSE tasks over popular WLS-based Gauss-Newton iterations and SDR approaches. © 1991-2012 IEEE.

关键词： State estimation

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Robust Semi-Global Leaderless Consensus and Containment Control of Identical Linear systems with Imperfect Actuators

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Journal of systems Science & Complexity 2018年第1期31卷 69-86页

作者： SHI Liangren ZHAO Zhiyun LIN Zongli Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education Shanghai 200237 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 USA

This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and there input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed to solve these problems. More specifically, it is shown that robust semi-global leaderless consensus can be achieved over a connected undirected graph and robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.

关键词： Consensus containment control dead zone leaderless consensus multi-agent systems saturation

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Real-time multi-target path prediction and planning for autonomous driving aided by FCN

arXiv

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arXiv 2019年

作者： Zhou, Hongtu Yang, Xinneng Zhang, Enwei Zhao, Junqiao Cai, Lewen Ye, Chen Wu, Yan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai Institute of Intelligent Vehicle Tongji University Shanghai Department of Computer Science Nanjing University Nanjing China

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms). Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous vehicles

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