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arXiv 2024年

作者： Yu, Jiajia Xiao, Quan Chen, Tianyi Lai, Rongjie The Department of Mathematics Duke University United States The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States The Department of Mathematics Purdue University United States

In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel problem. The primary benefit of our bilevel formulation lies in maintaining the convexity of the objective function and the linearity of constraints in the forward problem. Our paper focuses on inverse mean-field games characterized by unknown obstacles and metrics. We show numerical stability for these two types of inverse problems. More importantly, we, for the first time, establish the identifiability of the inverse mean-field game with unknown obstacles via the solution of the resultant bilevel problem. The bilevel approach enables us to employ an alternating gradient-based optimization algorithm with a provable convergence guarantee. To validate the effectiveness of our methods in solving the inverse problems, we have designed comprehensive numerical experiments, providing empirical evidence of its efficacy. © 2024, CC BY-NC-ND.

关键词： Inverse problems

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A Method of Predicting Worker’s Skill Level in SMEs for Estimating Changeover Time 23

A Method of Predicting Worker’s Skill Level in SMEs for Est...

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Proceedings of the 13th International Conference on the Internet of Things

作者： Yuto Shibuya Taiga Yamauchi Shogo Ogihara Takafumi Kawasaki Yuki Okura Takeshi Iwamoto Electrical and Computer Engineering Graduate School of Engineering Toyama Prefectural University Japan \t\t\t\t\t\t Electrical and Computer Engineering Graduate School of Engineering Toyama Prefectural University Japan \t\t\t\t\t\t Department of Information Systems Engineering Toyama Prefectural University Japan

ISBN: (纸本)9798400708541

IoT has been introduced to improve production efficiency in small and medium-sized manufacturing companies, and it is mainly aimed at measuring machinery statuses. The improvement of changeover time is significant for production efficiency and the changeover time is depends on the skill level of each worker. Thus, measuring the statuses of human-mediated processes is needed. Moreover, if the skill levels of each worker were quantified, it would be possible to predict setup changeover time based on the quantitative evaluation. Therefore, we define the components of worker proficiency in small and medium-sized manufacturing companies, measure and quantify them, and estimate the proficiency level of each worker. This study enables accurate prediction of setup time by realizing the above. It was 60% to predict the setup changeover time more accurately than the scheduled time, and some components showed a proportional relationship with skill level.

关键词： Changeover Time IoT Manufacturing Skilled Workers

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Competitive Multi-armed Bandit Games for Resource Sharing

arXiv

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arXiv 2025年

作者： Li, Hongbo Duan, Lingjie Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore

In modern resource-sharing systems, multiple agents access limited resources with unknown stochastic conditions to perform tasks. When multiple agents access the same resource (arm) simultaneously, they compete for successful usage, leading to contention and reduced rewards. This motivates our theoretical study of competitive multi-armed bandit (CMAB) games. In this paper, we study a new N-player K-arm competitive MAB game, where non-myopic players (agents) compete with each other to form diverse private estimations of unknown arms over time. Their possible collisions on the same arms and the time-varying nature of arm rewards make the policy analysis here more involved than the existing studies for myopic players. We explicitly analyze the threshold-based structures of the social optimum and the existing selfish policy, showing that the latter causes prolonged convergence times (Formula presented), while the socially optimal policy with coordinated communication reduces it to (Formula presented). Based on the policy comparison, we prove that the competition among selfish players for the best arm can result in an infinite price of anarchy (PoA), indicating an arbitrarily large efficiency loss compared to the social optimum. We further prove that no informational (non-monetary) mechanism (including Bayesian persuasion) can reduce the infinite PoA, as strategic misreporting by non-myopic players undermines such approaches. To address this, we propose a Combined Informational and Side-Payment (CISP) mechanism, which provides socially optimal arm recommendations with proper informational and monetary incentives to players according to their diverse and time-varying private beliefs. Our CISP mechanism keeps ex-post budget balanced for the social planner and ensures truthful reporting from players, thereby achieving the minimum PoA = 1 and the same convergence time as the social optimum. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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KKL Observer Synthesis for Nonlinear systems via Physics-Informed Learning

arXiv

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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Deep Unfolded Tensor Robust PCA With Self-Supervised Learning

Deep Unfolded Tensor Robust PCA With Self-Supervised Learnin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Harry Dong Megna Shah Sean Donegan Yuejie Chi Department of Electrical and Computer Engineering Carnegie Mellon University Materials and Manufacturing Directorate Air Force Research Laboratory

Tensor robust principal component analysis (RPCA), which seeks to separate a low-rank tensor from its sparse corruptions, has been crucial in data science and machine learning where tensor structures are becoming more prevalent. While powerful, existing tensor RPCA algorithms can be difficult to use in practice, as their performance can be sensitive to the choice of additional hyperparameters, which are not straightforward to tune. In this paper, we describe a fast and simple self-supervised model for tensor RPCA using deep unfolding by only learning four hyperparameters. Despite its simplicity, our model expunges the need for ground truth labels while maintaining competitive or even greater performance compared to supervised deep unfolding. Furthermore, our model is capable of operating in extreme data-starved scenarios. We demonstrate these claims on a mix of synthetic data and real-world tasks, comparing performance against previously studied supervised deep unfolding methods and Bayesian optimization baselines.

关键词： Tensors Signal processing algorithms Self-supervised learning Signal processing Transformers Task analysis Speech processing

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人工神经网络与遗传算法预测液体晃荡参数的比较

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哈尔滨工程大学学报（英文版） 2024年第2期23卷 292-301页

作者： Hassan Saghi Mohammad Reza Sarani Nezhad Reza Saghi Sepehr Partovi Sahneh Department of Civil Engineering Hakim Sabzevari UniversitySabzevarIran Department of Electrical and Computer Engineering University of BirjandBirjandIran State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian116024China Department of Marine Engineering Amirkabir University of TechnologyTehranIran

This paper develops a numerical code for modelling liquid *** coupled boundary element-finite element method was used to solve the Laplace equation for inviscid fluid and nonlinear free surface boundary *** Nakayama and Washizu’s results,the code performance was *** the developed numerical mode,we proposed artificial neural network(ANN)and genetic algorithm(GA)methods for evaluating sloshing loads and comparing *** compare the efficiency of the suggested methods,the maximum free surface displacement and the maximum horizontal force exerted on a rectangular tank’s perimeter are *** can be seen from the results that both ANNs and GAs can accurately predict η_(max) and F_(max).

关键词： Sloshing loads Fluid structure interactions Potential flow analysis Artificial neural network Genetic algorithm

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Resource Efficiency vs Performance Isolation Tradeoff in Network Slicing

arXiv

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arXiv 2023年

作者： Nikolaidis, Panagiotis Zoulkarni, Asim Baras, John Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS i states that its resource demand should be met for PiH fraction of time, and for PiL ≤ PiH fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler. © 2023, CC BY.

关键词： Bandwidth

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A Multiport Configuration of Dual Active Bridge CLLLC Resonant Converter for Solar Powered Electric Vehicle Fast Charging

A Multiport Configuration of Dual Active Bridge CLLLC Resona...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Md Safayatullah Reza Rezaii Mohammad Nilian Issa Batarseh Global eDrive Systems Ford Motor Company Dearborn Michigan USA Department of Electrical and Computer Engineering University of Central Florida Orlando Florida USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper introduces a three-port dc-dc-dc converter based on the dual active bridge with a CLLLC resonant tank and interleaved boost topology that integrates Photovoltaic (PV), electric vehicle (EV) battery, and dc-link situated between ac-dc stage and dc-dc stage of the EV dc fast charger. The power flow between port-1 (dc-link) and port-3 (EV) occurs due to the phase shift between input and output bridge switching of dual active bridge resonant (DABR) topology. We employ triple phase shift (TPS) modulation that secures zero voltage switching (ZVS) of all the switches for a wide range of EV battery voltage and hence, increases efficiency of the converter. The circuit acts as an interleaved boost topology between port-1 (dc-link) and port-2 (PV) that reduces the circulating current. The operating principles of different power flow modes have been discussed in detail. A 1 kW experimental prototype has been built. The results demonstrate peak efficiencies for various power flow paths: port1 to port-3 achieves 97.1%, port-2 to port-3 reaches 96.4%, and port-2 to port-1 attains 95.3%.

关键词： Phase modulation Bridge circuits Voltage Switches Zero voltage switching Electric vehicles Batteries Topology Fast charging Load flow

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks

Improving Information Freshness in Edge-Assisted Smart Grids...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Hossam Farag Mostafa Kotb Čedomir Stefanović Mikael Gidlund Department of Electronic Systems Aalborg University Denmark Department of Electrical Engineering Aswan University Egypt Department of Informatics Universität Hamburg Germany Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high re-sponsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL.

关键词： Measurement Computational modeling Predictive models Power system stability Routing Routing protocols Stability analysis Smart grids Sensors Standards

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Mechanical Design with Experimental Verification of a Lightweight Exoskeleton Chair

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Journal of Bionic engineering 2021年第2期18卷 319-332页

作者： Zihao Du Zefeng Yan Tiantian Huang Ou Bai Qin Huang Bin Han State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Human Cyber-Physical Systems Laboratory Department of Electrical and Computer EngineeringFlorida International UniversityMiami 33174USA Department of Rehabilitation Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China

In this study,a human-chair model was developed as the basis for a wearable-chair design.A prototype chair,HUST-EC,based on the model was fabricated and *** the optimization under the golden divisional method,an optimized simulation of the operating mode with the lowest chair height was implemented.A novel multi-link support structure has been established with parameters optimized using Matlab *** stress analysis of the solid models was conducted to ensure the adequate support from the designed chair for the *** subjects participated in the evaluation experiment,who performed both static tasks and dynamic *** experimental results consisted of subjective evaluation and objective *** experimental data demonstrate that(1)the HUST-EC can effectively reduce the activation level of related muscles at a variety of tasks;(2)the plantar pressure was reduced by 54%–67%;(3)the angle between the upper body and the vertical axis was reduced by 59%–77%;(4)the subjective scores for chair comfortability,portability,and stability were all higher than *** results further revealed that the designed chair can reduce the musculoskeletal burden and may improve work efficiency.

关键词： exoskeleton wearable chair bionic robot mechanism design ergonomics

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