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

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

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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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Dual-Mode Asymmetric Structures with Visible and Infrared Spectral engineering for All-Season Radiative Thermoregulation

SSRN

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SSRN 2024年

作者： Wang, Tong Zhang, Shuqi Zhu, Qian Ding, Zhiyi Zhang, Jie Zhang, Yu Du, Yanping Wu, Limin Gu, Min Zhang, Yinan Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Materials and Chemistry University of Shanghai for Science and Technology Shanghai200093 China Department of Materials Science State Key Laboratory of Molecular Engineering of Polymers Fudan University Shanghai200433 China University of Exeter United Kingdom

Passive daytime radiative cooling is of tremendous interest but would be overcooling during cold nights or winter days, exacerbating the heating cost, especially in high-latitude areas. Integrating the heating and cooling in one photothermal system can avoid the cooling penalties and potential barriers for wide practical scenarios. Herein, we demonstrate dual-mode asymmetric structures with visible and infrared spectral engineering for all-season radiative thermoregulation. The cooling mode with a solar reflectivity of 0.95 and a mid-infrared emissivity of 0.98 endows a comparable daytime subambient cooling of 9.8 °C with a net cooling power of 76.6 W/m2. Meanwhile, the heating mode with a solar absorptivity of 0.88 and a mid-infrared emissivity of 0.28 yields a daytime above-ambient heating of 16.3 °C with a net heating power of 667.8 W/m2. Promisingly, the optimal spectra regulation amplitude (ηR =7.92, ηE = 3.5), thermoregulation capability (∆Tcooling-heating = 26.1 °C), superhydrophobicity and environmental durability make the dual-mode asymmetric film a viable pathway for building and human thermal managements, showing great potential in electricity savings and CO2 emission reduction. Our dual-mode asymmetric structures offer new possibilities in designing next-generation radiative cooling materials and may address the longstanding problem of achieving all-season thermoregulation, greatly widening the scope of use. © 2024, The Authors. All rights reserved.

关键词： Energy conservation

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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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Near-ultrastrong nonlinear light-matter coupling in superconducting circuits

arXiv

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arXiv 2024年

作者： Ye, Yufeng Kline, Jeremy B. Yen, Alec Cunningham, Gregory Tan, Max Zang, Alicia Gingras, Michael Niedzielski, Bethany M. Stickler, Hannah Serniak, Kyle Schwartz, Mollie E. Brien, Kevin P.O. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States

The interaction between an atom and an electromagnetic mode of a resonator is of both fundamental interest and is ubiquitous in quantum technologies. Most prior work studies a linear light-matter coupling of the form gσˆx(â + â†), where g measured relative to photonic (ωa) and atomic (ωb) mode frequencies can reach the ultrastrong regime (g/ωa > 10−1). In contrast, a nonlinear light-matter coupling of the form χ2 σˆzâ†â has the advantage of commuting with the atomic σˆz and photonic â†â Hamiltonian, allowing for fundamental operations such as quantum-non-demolition measurement. However, due to the perturbative nature of nonlinear coupling, the state-of-the-art χ/max(ωa, ωb) is limited to −2. Here, we use a superconducting circuit architecture featuring a quarton coupler to experimentally demonstrate, for the first time, a near-ultrastrong χ/max(ωa, ωb) = (4.852±0.006)×10−2 nonlinear coupling of a superconducting artificial atom and a nearly-linear resonator. We also show signatures of light-light nonlinear coupling (χâ†âbˆ†bˆ), and χ/2π = 580.3 ±0.4 MHz matter-matter nonlinear coupling (χ4 σˆz,aσˆz,b) which represents the largest reported ZZ interaction between two coherent qubits. Such advances in the nonlinear coupling strength of light, matter modes enable new physical regimes and could lead to applications such as orders of magnitude faster qubit readout and gates. Copyright © 2024, The Authors. All rights reserved.

关键词： Atoms

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Realization of Resonant Optical Metasurfaces with Maximal and Tunable Chiral Response

Realization of Resonant Optical Metasurfaces with Maximal an...

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CLEO: science and Innovations, S and I 2022

作者： Shi, Tan Deng, Zi-Lan Geng, Guangzhou Zeng, Yixuan Hu, Guangwei Overvig, Adam Li, Junjie Qiu, Cheng-Wei Alù, Andrea Kivshar, Yuri S. Li, Xiangping Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou510632 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100191 China Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Nonlinear Physics Center Australian National University CanberraACT2601 Australia

We employ the physics of chiral bound state in the continuum and suggest planar chiral metasurfaces with near-perfect circular dichroism (CD). We demonstrate experimentally metasurfaces with CD=0.9 with Q factor excee... 详细信息

ISBN: (纸本)9781557528209

关键词： Dichroism

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Floquet-Mode Traveling-Wave Parametric Amplifiers

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PRX Quantum 2022年第2期3卷 020306-020306页

作者： Kaidong Peng Mahdi Naghiloo Jennifer Wang Gregory D. Cunningham Yufeng Ye Kevin P. O’Brien Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA

Simultaneous ideal quantum measurements of multiple single-photon-level signals would advance applications in quantum information processing, metrology, and astronomy but require the first amplifier to be simultaneously broadband, quantum limited, and directional. However, conventional traveling-wave parametric amplifiers support broadband amplification at the cost of increased added noise and are not genuinely directional due to non-negligible nonlinear backward-wave generation. In this work, we introduce a new class of amplifiers that encode the information in the Floquet modes of the system. Such Floquet-mode amplifiers prevent information leakage and overcome the trade-off between quantum efficiency (QE) and bandwidth. Crucially, Floquet-mode amplifiers strongly suppress the nonlinear forward-backward wave coupling and are therefore genuinely directional and readily integrable with qubits, clearing another major obstacle toward broadband ideal quantum measurements. Furthermore, Floquet-mode amplifiers are insensitive to out-of-band impedance mismatch, which otherwise may lead to gain ripples, parametric oscillations, and instability in conventional traveling-wave parametric amplifiers. Finally, we show that a Floquet-mode Josephson traveling-wave parametric amplifier implementation can simultaneously achieve >20dB gain and a QE of η/ηideal>99.9% of the quantum limit over more than an octave of bandwidth. The proposed Floquet scheme is also widely applicable to other platforms, such as kinetic inductance traveling-wave amplifiers and optical parametric amplifiers.

关键词： Metamaterials Quantum information with solid state qubits Quantum metrology Superconducting quantum optics Superconducting devices Superconducting qubits

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Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

arXiv

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arXiv 2022年

作者： Zhang, Weixia Li, Dingquan Min, Xiongkuo Zhai, Guangtao Guo, Guodong Yang, Xiaokang Ma, Kede MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Network Intelligence Research Department Peng Cheng Laboratory China Department of Computer Science and Electrical Engineering West Virginia University United States Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made vision systems. Here we make one of the first attempts to examine the perceptual robustness of NR-IQA models. Under a Lagrangian formulation, we identify insightful connections of the proposed perceptual attack to previous beautiful ideas in computer vision and machine learning. We test one knowledge-driven and three data-driven NR-IQA methods under four full-reference IQA models (as approximations to human perception of just-noticeable differences). Through carefully designed psychophysical experiments, we find that all four NR-IQA models are vulnerable to the proposed perceptual attack. More interestingly, we observe that the generated counterexamples are not transferable, manifesting themselves as distinct design flows of respective NR-IQA methods. © 2022, CC BY.

关键词： Image quality

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