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arXiv 2024年

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua The Department of Electrical Engineering Anhui Polytechnic University Wuhu China The School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China The School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Taipa China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging non-convex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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Convergence Condition of Simplified Information Geometry Approach for Massive MIMO-OFDM Channel Estimation

Convergence Condition of Simplified Information Geometry App...

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IEEE Conference on Vehicular Technology (VTC)

作者： Mingrui Fan Jiyuan Yang Yan Chen An-An Lu Xiqi Gao Xiang-Gen Xia Dirk Slock National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Communication Systems EURECOM Biot France

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we prove the convergence of the simplified information geometry approach (SIGA), which was proposed for massive MIMO-OFDM channel estimation. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results.

关键词： Damping Sufficient conditions Information geometry Vehicular and wireless technologies Simulation Channel estimation Bayes methods

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DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices

arXiv

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arXiv 2024年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen School of Optical- Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System the National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential pricing system (DDPS), which determines the price per unit time according to the usage of computing resources to improve the edge server utilization. Firstly, we propose an offloading decision algorithm to decide whether to conduct the offloading operation and how much data to be offloaded if conducted, the algorithm determines offloading operation by balancing the energy harvested with the energy consumed. Secondly, for the offloading case, we formulate the game between the MUs and the server as a Stackelberg game, and propose a differential pricing algorithm to determine the optimal computing resources required by MUs. Furthermore, the proposed algorithm also reallocates computing resources for delay-sensitive devices while server resources are surplus after the initial allocation, aiming to make full use of the server computing resources. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Optimal Information Provision for Strategic Hybrid Workers

arXiv

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arXiv 2022年

作者： Shah, Sohil Amin, Saurabh Jaillet, Patrick The Laboratory for Information and Decision Systems The Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center CambridgeMA United States

We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated public belief over the parameter, and agents then make equilibrium choices on whether to work remotely or in-person. The planner maintains a set of desirable outcomes for each realization of the uncertain parameter and seeks to maximize the probability that agents choose an acceptable outcome for the true parameter. We distinguish between stateless and stateful objectives. In the former, the set of desirable outcomes does not change as a function of the risk parameter, whereas in the latter it does. For stateless objectives, we reduce the problem to maximizing the probability of inducing mean beliefs that lie in intervals computable from the set of desirable outcomes. We derive the optimal signalling mechanism and show that it partitions the parameter domain into at most two intervals with the signals generated according to an interval-specific distribution. For the stateful case, we consider a practically relevant situation in which the planner can enforce in-person work capacity limits that progressively get more stringent as the risk parameter increases. We show that the optimal signalling mechanism for this case can be obtained by solving a linear program. We numerically verify the improvement in achieving desirable outcomes using our information design relative to no information and full information benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring

arXiv

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arXiv 2024年

作者： Wang, Yinjian Li, Wei Gui, Yuanyuan Du, Qian Fowler, James E. The School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution, and many tensor-based approaches to this task have been recently proposed. Yet, it is assumed in such tensor-based methods that the spatial-blurring operation that creates the observed hyperspectral image from the desired super-resolved image is separable into independent horizontal and vertical blurring. Recent work has argued that such separable spatial degradation is ill-equipped to model the operation of real sensors which may exhibit, for example, anisotropic blurring. To accommodate this fact, a generalized tensor formulation based on a Kronecker decomposition is proposed to handle any general spatial-degradation matrix, including those that are not separable as previously assumed. Analysis of the generalized formulation reveals conditions under which exact recovery of the desired super-resolved image is guaranteed, and a practical algorithm for such recovery, driven by a blockwise-group-sparsity regularization, is proposed. Extensive experimental results demonstrate that the proposed generalized tensor approach outperforms not only traditional matrix-based techniques but also state-of-the-art tensor-based methods;the gains with respect to the latter are especially significant in cases of anisotropic spatial blurring. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Enhanced Grid-Forming Inverter Control Through Integral Sliding Mode Control in a Cascaded Framework

Enhanced Grid-Forming Inverter Control Through Integral Slid...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Khaled Rayane Abdelhamid Rabhi Brahim Khalil Oubbati Mohammed Benzoubir University of Laghouat LACoSERE Lab Laghouat Algeria Department of Electrical and Computer Engineering Texas A and M University at Qatar Qatar Foundation Doha Qatar Laboratory of Modelisation Information and Systems–MIS University of Picardie Jules Verne

The microgrid (MG) relies heavily on the grid-forming inverter (GFI), making it a crucial component. Therefore, precise and adaptable control of the GFI is essential for microgrid operations due to its pivotal role in shaping voltage and regulating frequency. In this context, this paper introduces a Integral Sliding Mode Control (ISMC) based cascaded approach for controlling the GFI system. The primary goal of this control strategy is to produce a three-phase sinusoidal voltage with a specified amplitude while minimizing harmonic distortion in the output voltage under varying load conditions. The key advantage of the proposed GFI controller lies in its ability to deliver superior performance, characterized by low Total Harmonic Distortion (THD), rapid dynamic tracking capabilities, and robustness in the face of load fluctuations. A comparative assessment is conducted between the conventional repetitive controller and the newly proposed method, considering linear and nonlinear load scenarios. The simulation results provide strong validation for the effectiveness of the proposed approach.

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On Radically Expanding the Landscape of Potential Applications for Automated-Proof Methods

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SN computer Science 2021年第4期2卷 1-9页

作者： Uhlmann, Jeffrey Wang, Jie Department of Electrical Engineering and Computer Science University of Missouri 201 Naka Hall Columbia United States Laboratory for Analysis and Architecture of Systems French National Centre for Scientific Research Toulouse France

In this paper, we examine the potential of optimization-based computer-assisted proof methods to be applied much more widely than commonly recognized by engineers and computer scientists. More specifically, we contend that there are vast opportunities to derive valuable mathematical results and properties that may be narrow in scope, such as in highly specialized engineering control applications, that are presently overlooked, because they have characteristics atypical of those that are conventionally studied in the areas of pure and applied mathematics. As a concrete example, we demonstrate use of sum-of-squares (SOS) optimization for certifying polynomial nonnegativity as a part of a proposed dimension-pinning strategy to prove that the inverse of the relative gain array (RGA) of a d-dimensional positive-definite matrix is doubly stochastic for d≤ 4. However, it is not specifically this result and solution method that are of principal interest in this paper but rather how they illustrate the relevance of optimization-based proof techniques to engineering system design more broadly. We believe that our paper is the first to explicitly emphasize the fundamental distinction between methods that can be applied to prove results/properties over a fixed number of dimensions versus those that hold generally. The latter class of problems is the conventional domain of mathematicians, but the former is what we propose to be a fertile and largely unrecognized class of problems that are amenable to automated-proof technologies, e.g., as we demonstrate using our novel dimension-pinning approach. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Automated proofs Automated software methods computer-aided system design computer-assisted proofs Dimension pinning Inverse relative gain array IRGA Relative gain array RGA SOS proofs Sum-of-squares optimization

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2021 Monthly Rice Production in Chinese Coastal Provinces 3

2021 Monthly Rice Production in Chinese Coastal Provinces

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Parmar, Ajeet Munasinghe, Thilanka Tubbs, Heidi Anyamba, Assaf Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Troy United States Rensselaer Polytechnic Institute Department of Information Technology and Web Science Troy United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak Ridge United States

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations. © 2022 IEEE.

关键词： Regression analysis

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