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arXiv 2023年

作者： Tong, Hao Liu, Shengyan Fang, Kejie Holonyak Micro and Nanotechnology Laboratory Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Mechanical bound states in the continuum (BICs) present an alternative avenue for developing high-frequency, high-Q mechanical resonators, distinct from the conventional band structure engineering method. While symmetry-protected mechanical BICs have been realized in phononic crystals, the observation of accidental mechanical BICs-whose existence is independent of mode symmetry and tunable by structural parameters-has remained elusive. This challenge is primarily attributed to the additional radiation channel introduced by the longitudinal component of elastic waves. Here, we employ a coupled wave theory to predict and experimentally demonstrate mechanical accidental BICs within a high-aspect-ratio gallium arsenide phononic crystal grating. We observe the merging process of accidental BICs with symmetry-protected BICs, resulting in reduced acoustic radiation losses compared to isolated BICs. This finding opens up new possibilities for phonon trapping using BIC-based systems, with potential applications in sensing, transduction, and quantum measurements. Copyright © 2023, The Authors. All rights reserved.

关键词： Elastic waves

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Investigating the Vision Transformer Model for Image Retrieval Tasks

arXiv

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arXiv 2021年

作者： Gkelios, Socratis Boutalis, Yiannis Chatzichristofis, Savvas A. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus

This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Transformer network while it does not require any training data to adjust parameters. In image retrieval tasks, the use of Handcrafted global and local descriptors has been very successfully replaced, over the last years, by the Convolutional Neural Networks (CNN)-based methods. However, the experimental evaluation conducted in this paper on several benchmarking datasets against 36 state-of-the-art descriptors from the literature demonstrates that a neural network that contains no convolutional layer, such as Vision Transformer, can shape a global descriptor and achieve competitive results. As fine-tuning is not required, the presented methodology’s low complexity encourages adoption of the architecture as an image retrieval baseline model, replacing the traditional and well adopted CNN-based approaches and inaugurating a new era in image retrieval approaches. © 2021, CC BY.

关键词： Convolution

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***: A differential evolution package for the analytic continuation of imaginary time correlation functions

arXiv

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arXiv 2024年

作者： Neuhaus, James Nichols, Nathan S. Banerjee, Debshikha Cohen-Stead, Benjamin Maier, Thomas A. Del Maestro, Adrian Johnston, Steven Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Institute of Advanced Materials and Manufacturing The University of Tennessee KnoxvilleTN37996 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831-6494 United States

We introduce the *** package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the *** package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://***/SmoQySuite/*** or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://***/***/stable/. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Learning Eco-Driving Strategies at Signalized Intersections

arXiv

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arXiv 2022年

作者： Jayawardana, Vindula Wu, Cathy MIT Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society CambridgeMA02139 United States

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits. Copyright © 2022, The Authors. All rights reserved.

关键词： Travel time

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Tensor-Monopole-Induced Topological Boundary Effects in Four-Dimensional Acoustic Metamaterials

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Physical Review Letters 2025年第18期134卷 186601-186601页

作者： Qingyang Mo Shanjun Liang Cuicui Lu Jie Zhu Shuang Zhang New Cornerstone Science Laboratory Department of Physics University of Hong Kong 999077 Hong Kong China School of Professional Education and Executive Development The Hong Kong Polytechnic University Hong Kong China Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Center for Interdisciplinary Science of Optical Quantum and NEMS Integration School of Physics Beijing Institute of Technology Beijing 100081 China Institute of Acoustics School of Physics Science and Engineering Tongji University 200092 Shanghai China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong China Quantum Science Center of Guangdong-Hong Kong-Macao Great Bay Area 3 Binlang Road Shenzhen China Materials Innovation Institute for Life Sciences and Energy (MILES) HKU-SIRI Shenzhen People’s Republic of China

Gauge field theory provides the mathematical and conceptual framework to describe and understand topological singularities such as Weyl points and magnetic monopoles. While singularities associated with vector electromagnetic gauge fields have been well studied, those of higher-form tensor gauge fields, like the four-dimensional (4D) tensor monopoles predicted by string theory, have remained largely theoretical or limited to experimental demonstration in pure synthetic dimensions, thereby not allowing investigations of the associated boundary effects. Here, we present a 4D system with tensor monopoles using engineered acoustic metamaterials. Our momentum space combines three real momentum dimensions and a geometric parameter as the fourth. By varying this fourth momentum, we experimentally reveal two distinct topological surface states in 3D subsystems: Fermi-arc surface states in a gapless subsystem and Dirac-cone surface states in a gapped subsystem. Our work introduces a novel platform for exploring new topological structures associated with tensor gauge field and topological phenomena in higher dimensions.

关键词： Momentum

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A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

SAE International Journal of Electrified Vehicles

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SAE International Journal of Electrified Vehicles 2021年第2期11卷

作者： Khalatbarisoltani, Arash Kandidayeni, Mohsen Boulon, Loic Hu, Xiaosong Hydrogen Research Institute Department of Electrical and Computer Engineering Université du Québec À Trois-Rivières QCG8Z 4M3 Canada Université de Sherbrooke Canada Uqtr Hydrogen Research Institute Canada State Key Laboratory for Mechanical Transmission Department of Automotive Engineering Chongqing University Chongqing400044 China Advanced Vehicle Engineering Centre Cranfield University CranfieldMK43 0AL United Kingdom

An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack. The contribution of the suggested multi-agent approach lies in the development of a fully decentralized learning strategy composed of several connected local modules. The performance of the proposed approach is investigated through several simulations and experimental tests. The results indicate the advantage of the established Dec-RL control scheme in convergence speed and optimization criteria. © 2021 SAE International Journal of Electrified Vehicles.

关键词： Multi agent systems

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Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

Green Routing Game: Strategic Logistical Planning using Mixe...

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IEEE Conference on Decision and Control

作者： Hampei Sasahara György Dán Saurabh Amin Henrik Sandberg Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.

关键词： Costs Pollution Green products Internal combustion engines Games Routing Planning

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Multi-Functional Beamforming Design for Integrated Sensing, Communication, and Computation

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IEEE Transactions on Communications 2024年

作者： Zhao, Yapeng Wu, Qingqing Chen, Wen Zeng, Yong Liu, Ruiqi Mei, Weidong Hou, Fen Ma, Shaodan Shanghai Jiao Tong University Department of Electronic Engineering 200240 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518057 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Macau Department of Electrical and Computer Engineering 999078 China

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints. © 1972-2012 IEEE.

关键词： Convex optimization

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A Cryptographic-Based Approach for Electricity Theft Detection in Smart Grid

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computers, Materials & Continua 2020年第4期63卷 97-117页

作者： Khelifi Naim Benahmed Khelifa Bounaama Fateh Laboratory of Energetic in Arid Zones(ENERGARID) Department of Electrical EngineeringFaculty of TechnologyTahri Mohammed University of BecharBechar08000Algeria Department of Mathematics and Computer Science Faculty of Exact SciencesTahri Mohammed University of BecharBechar08000Algeria

In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid *** been treated earlier by several researchers,various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses,aiming primarily to the identification of suspicious incoming *** meshes have been extensively used in smart grid communication architectures due to their facility,lightness of conception and low cost installation;however,the communicated packets are still exposed to be intercepted maliciously in order either to falsify pertinent information like the smart meter readings,or to inject false data instead,aiming at electricity theft during the communication *** this reason,this paper initiates a novel method based on RSA cryptographic algorithm to detect electricity fraud in smart *** new method consists of generating two different cryptograms of one electricity measurement before sending,after which the recipient is used to find the same value after decrypting the two cyphers in a normal ***,a fraudulent manipulation could occur during the transmission *** presented method allows us to kill two birds with one ***,satisfactory outcomes are shown:the algorithm accuracy reaches 100%,from one hand,and the privacy is protected thanks to the cryptology concept on the other hand.

关键词： Electricity consumption fraud detection RSA algorithm security smart grid

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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