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IEEE Conference on Decision and Control

作者： Brendon G. Anderson Samuel Pfrommer Somayeh Sojoudi Department of Mechanical Engineering California Polytechnic State University Department of Electrical Engineering and Computer Sciences University of California Berkeley

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This work leverages tools from evolutionary game theory to solve unconstrained nonconvex optimization problems. Specifically, we lift such a problem to an optimization over probability measures, whose minimizers exactly correspond to the Nash equilibria of a particular population game. To algorithmically solve for such Nash equilibria, we introduce approximately Gaussian replicator flows (AGRFs) as a tractable alternative to simulating the corresponding infinite-dimensional replicator dynamics. Our proposed AGRF dynamics can be integrated using off-the-shelf ODE solvers when considering objectives with closed-form integrals against a Gaussian measure. We theoretically analyze AGRF dynamics by explicitly characterizing their trajectories and stability on quadratic objective functions, in addition to analyzing their descent properties. Our methods are supported by illustrative experiments on a range of canonical nonconvex optimization benchmark functions.

关键词： Visualization Heuristic algorithms Games Benchmark testing approximation algorithms Linear programming Stability analysis Polynomials Trajectory Optimization

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Quantum-Inspired Resilient Restoration Approach Considering UAV-Assisted Fault Location and Network Reconfiguration

Quantum-Inspired Resilient Restoration Approach Considering ...

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IEEE General Meeting Power& Energy Society

作者： Wei Fu Haipeng Xie Hefeng Wang Chen Chen School of Electrical Engineering Xi’an Jiaotong University Xi’an China School of Physics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Post-disaster fault identification and network reconfiguration are particularly important for enhancing the resilience of power distribution system (PDS). In this paper, a two-step quantum-inspired approach is devised to resolve resilient restoration problem, in which the UAV-assisted fault location and network reconfiguration model are reformulated into a quantum-compatible form. Depending on the quantum-supported scales and characteristics, quantum approximate optimization algorithm (QAOA) and proposed quantum annealing-embedded alternating direction method of multipliers (QA-ADMM) algorithm are applied to obtain feasible solutions. With the utilization of IBM Qiskit and D-Wave Leap™ cloud platforms, the results verify the effectiveness of proposed approach by comparing with classical computing.

关键词： Optimization models Computational modeling Power distribution Fault location Logic gates approximation algorithms Quantum annealing Integrated circuit modeling Optimization Resilience

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Research on Bi-directional Cooperative Optimal Scheduling of Novel Power System Source and Load Based on Particle Swarm Optimization Algorithm

Research on Bi-directional Cooperative Optimal Scheduling of...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Lei Li Hao Ning Xiangli Peng Wen Liu Shan Yang State Grid Hubei Information & Telecommunication Company Wuhan Hubei China State Grid Hubei Electric Power Co. Ltd. Wuhan Hubei China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the transformation of energy structure and the increase of power demand, novel power system faces the challenge of fluctuation and uncertainty of renewable energy. To enhance the efficiency, stability, and cost-effectiveness of the system, this study introduces the Particle Swarm Optimization (PSO) algorithm. The goal is to achieve optimal bi-directional cooperative scheduling of both the source and load. In this paper, IEEE33-node power grid is selected as the experimental framework, and a power system model including distributed generation and various loads is constructed. The objective of the optimization is to reduce the overall operating cost of the system, which includes power purchase cost, distributed power supply operating cost, load reduction cost and load transfer compensation cost. PSO algorithm uses mixed coding method to deal with decision variables such as distributed power output, load shedding and load transfer, and designs reasonable fitness function and updating strategy. The simulation results demonstrate that the optimized scheduling scheme significantly smooths the output curve of distributed power sources, effectively lowers the system’s total operating costs, and ensures stable operation and reliable power supply. Compared to the Genetic Algorithm (GA) and Ant Colony Algorithm (ACO), the PSO algorithm can identify the near-optimal solution in fewer iterations, exhibiting strong global search capabilities and faster convergence. This study not only verifies the effectiveness and superiority of PSO algorithm in the bi-directional cooperative optimal scheduling of source and load, but also provides theoretical support and technical reference for the economic operation and stable power supply of novel power system.

关键词： Renewable energy sources Costs Heuristic algorithms Simulation Optimal scheduling Bidirectional control Power system stability approximation algorithms Stability analysis Particle swarm optimization

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Optimizing Lateral Motion Control of an Autonomous Ground Vehicle Using Modified Particle Swarm Optimization with Model Predictive Controller

Optimizing Lateral Motion Control of an Autonomous Ground Ve...

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Computer, Electronics & Electrical Engineering & their Applications (IC2E3), International Conference on

作者： Abhishek Thakur Sudhansu Kumar Mishra Ankit Kumar Subrat Kumar Swain Subhendu Kumar Behera Department of EEE BIT Mesra Ranchi India Department of ETC DRIEMS Autonomous Engg College Cuttack India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

This research presents a strategy to enhance the lateral movement control of Autonomous Ground Vehicles (AGVs) by combining a Modified Particle Swarm Optimization (MPSO) approach with a Model Predictive Controller (MPC). Concentrating on the AGV's position and orientation, this technique aims to boost agility and precision. We assess this method against algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Batch Gradient Descent (BGD) analyzing their performance based on steering wheel angle, lateral position and tracking accuracy. Furthermore, the study utilizes sign and rank tests for an examination further confirming the efficacy of the MPSO based MPC approach. The results indicate that our method outperforms the algorithms across all metrics showcasing its potential to significantly enhance AGV side to side movement control. This progress shows promise in enhancing AGV performance, in driving scenarios, underscoring the vital importance of accurate and effective lateral motion control.

关键词： Accuracy Computational modeling Wheels Bicycles Predictive models approximation algorithms Particle swarm optimization Motion control Vehicle dynamics Genetic algorithms

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Verification of Approximate Prognosability via Barrier Certificates

Verification of Approximate Prognosability via Barrier Certi...

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IEEE Conference on Decision and Control

作者： Weijie Dong Bingzhuo Zhong Xiang Yin Majid Zamani Department of Automation Shanghai Jiao Tong University China Thrust of Artificial Intelligence Information Hub Hong Kong University of Science and Technology (Guangzhou) China Department of Computer Science University of Colorado Boulder USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the verification of approximate prognosability for discrete-time control systems with continuous state set in the context of fault prognosis. Existing works on this topic rely on constructing finite abstractions, which lead to significant computation burden. To address this challenge, we propose an abstraction-free method via barrier certificates. Specifically, we consider a notion of socalled approximate $(M, \delta)$-prognosability requiring that every fault, characterized by entering a fault region, can be predicted before its occurrence under observation precision $\delta$ and once an alarm is issued, fault will occur for sure within M time instants. Then, we propose a verification scheme based on an M-deterministic finite automaton over an augmented system of the original system. Then, we reduce the verification of (the lack of) approximate ($M, \delta$)-prognosability to a safety verification problem, which can be checked effectively by barrier certificates. Furthermore, a counter-example guided inductive synthesis framework is proposed to compute these barrier certificates.

关键词： Automata Control systems approximation algorithms Safety Prognostics and health management

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Near-field Source DOA Estimation Algorithm Based on De-impact Function under Impulse Noise

Near-field Source DOA Estimation Algorithm Based on De-impac...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xingchao Xu Baohe Pang Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In addressing the issue of localizing near-field source signals in the presence of impulse noise, this manuscript introduces a novel de-impact function designed for preprocessing received signals to alleviate the interference caused by impulse noise. Simulation experiments verify the effectiveness of the proposed de-impact function in effectively suppressing impulse noise. Furthermore, to augment estimation precision, a novel algorithm for near-field source localization is proposed. Building upon the framework of correlation entropy theory, a new correntropy covariance matrix is formulated to replace the original signal covariance matrix for signal processing. Through a succession of low-bias approximations between near-field and far-field scenarios, the algorithm converts the original 2-D search into 1-D operations. Additionally, the conventional spectral peak search is replaced by polynomial solving to further mitigate the computational complexity of the algorithm.

关键词： Location awareness Direction-of-arrival estimation Noise Signal processing algorithms Estimation Interference approximation algorithms

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TrendSharing: A Framework to Discover and Follow the Trends for Shared Mobility Services

TrendSharing: A Framework to Discover and Follow the Trends ...

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International Conference on Data Engineering

作者： Jiexi Zhan Han Wu Peng Cheng Libin Zheng Lei Chen Chen Jason Zhang Xuemin Lin Wenjie Zhang East China Normal University Shanghai China Sun Yat-sen University Guangzhou China HKUST(GZ) and HKUST Guangzhou Hong Kong SAR China The Hong Kong Polytechnic University HK SAR China Shanghai Jiaotong University Shanghai China The University of New South Wales Sydney Australia

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the development of ubiquitous smart devices, shared mobility services, such as food delivery, ridesharing and crowdsourced parcel delivery, and the related problems, such as task assignment and route planning have drawn much attention from academia and industry. Specifically, shared mobility services enable one worker to deliver more than one package/passenger together such that their routes can share some common sub-routes. Tardiness (the exceeded time) can harm users' experience and reduce the revenue of workers and platforms, which is not well handled in the existing studies. In this paper, we propose a framework, TrendSharing, to minimize the total tardiness when serving all tasks. In TrendSharing, we first build a flow tree to group tasks together. Then, we propose a concept of trend, which represents a group of tasks with high sharability in the flow tree. Furthermore, we devise a decision factor $\epsilon$ -score to properly select the trend from the flow tree. In addition, we devise an indicator k-regret to quantify the likelihood of tardiness for each task and devise a greedy algorithm to conduct task assignment. We observe that the insertion operation that is widely used by existing works has little effect on the objective of minimizing total tardiness. Thus, we adopt a simple yet effective strategy, which will continuously append newly planned routes to the workers' existing routes. Moreover, we design an algorithm to plan a route for the trend with an approximation ratio of 2.5. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real datasets.

关键词： Greedy algorithms Industries Shared transport Market research approximation algorithms User experience Planning

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FPT approximations for Capacitated Sum of Radii and Diameters

arXiv

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arXiv 2024年

作者： Filtser, Arnold Gadekar, Ameet Bar-Ilan University

The Capacitated Sum of Radii problem involves partitioning a set of points P, where each point p ∈ P has capacity Up, into k clusters that minimize the sum of cluster radii, such that the number of points in the cluster centered at point p is at most Up. We begin by showing that the problem is APX-hard, and that under gap-ETH there is no parameterized approximation scheme (FPT-AS). We then construct a ≈ 5.83-approximation algorithm in FPT time (improving a previous ≈ 7.61 approximation in FPT time). Our results also hold when the objective is a general monotone symmetric norm of radii. We also improve the approximation factors for the uniform capacity case, and for the closely related problem of Capacitated Sum of Diameters. © 2024, CC BY.

关键词： approximation algorithms

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Graph Low-Rank Non-Negative Matrix Factorization with Auto-Encoders for Fault Detection

Graph Low-Rank Non-Negative Matrix Factorization with Auto-E...

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International Conference on Information Science and Technology (ICIST)

作者： Yabing Liu Shanshan Yu Wei Guo Man-Fai Leung School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University State Ethnic Affairs Commission Wuhan China Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics $T^{2}$ (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets.

关键词： Dimensionality reduction Accuracy Fault detection Computational modeling Noise Feature extraction approximation algorithms Robustness Optimization Tuning

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Physical Layer Security in Bistatic Backscatter Communications for Passive Internet of Things

Physical Layer Security in Bistatic Backscatter Communicatio...

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International Conference on Communication Technology (ICCT)

作者： Sihao Li Huang-Jie Zhou Gang Yang Ning Jin Ying-Chang Liang Department of Mobile Commun. and Terminal Research (DMCTR) China Telecom Research Institute (CTRI) Guangzhou China Shenzhen Institute for Advanced Study (SIAS) University of Electronic Science and Technology of China (UESTC) Shenzhen China SIAS National Key Lab. of Wireless Communications (NKLWC) UESTC Shenzhen and Chengdu China DMCTR CTRI Guangzhou China NKLWC UESTC Chengdu China Center for Intelligent Networking and Communications UESTC Chengdu China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

In this paper, we consider a bistatic backscatter communication system for passive Internet of Things, which includes a carrier transmitter (CT), a dual-antenna backscatter device (BD), and a legitimate user (LU). Due to the risk of being eavesdropped, one of the BD’s antennas is utilized to reflect artificial noise to disturb the eavesdropper (Eve), while the other reflects secret message symbols to the LU. First, to analyze the system’s reliability and security, we derive the outage probability and intercept probability in (semi-)closed form. Then, to enhance the system’s physical layer security, we maximize the secrecy rate by jointly optimizing the CT’s beamforming vector and the BD’s power reflection coefficient, subject to the CT’s maximum transmit power and BD’s power reflection coefficient constraints. To solve this non-convex problem, we propose an alternating-optimization based algorithm that alternately optimizes the variables using the dinkelbach’s transform and successive convex approximation technique. Numerical simulations show that: 1) the analytical results coincide with the simulation results, and the performance trade-off of reliability and security can be realised by optimizing the CT’s transmit power; 2) the proposed optimization algorithm can significantly improve the system’s secrecy rate compared to the benchmarks; 3) the system’s secrecy rate increases for a longer Eve-to-BD distance and a larger number of CT antennas.

关键词： Noise Symbols Physical layer security Reflector antennas approximation algorithms Reflection coefficient Internet of Things Security Reliability Backscatter

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