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Optically controlled quantum thermal gate

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Physical Review B 2020年第24期101卷 245402-245402页

作者： Ravi T. Wijesekara Sarath D. Gunapala Mark I. Stockman Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA Department of Physics and Astronomy Georgia State University Atlanta Georgia 30303 USA

The ability to actively control heat flows in the nanoscale can open up a plethora of opportunities for applications that require thermal management and regulation. We show that it is possible to build a three-terminal quantum thermal gating device which regulates the thermal conductivity between two of its terminals in response to light incident upon the remaining terminal. We model our device as three mutually coupled two-level systems which interact thermally and optically with their environment. To incorporate the thermal interactions of our device, we adopt an open quantum systems framework under the Born-Markov approximation. We subsequently use detailed quantum mechanical state analysis to illustrate its operating principle. Through numerical simulations, we further explore the nonlinear relationship between the optical field amplitude and the thermal conductivity of the device in the steady-state regime. Based on our investigations, we find that the energy-gating behavior of our device is highly efficient in that it can control a significantly larger thermal energy flow compared to the amount of energy it absorbs from the optical field in the process. The approach we have taken to analyze the system, in particular the graphical representations we developed to intuitively and concisely represent the quantum states, energy flows, and the relationships between them, could be of value in analyzing other similar thermo-optical systems. Thus, we envision that both the device concept and its analysis technique would be useful to researchers working in this area.

关键词： Electronic transitions Open quantum systems Thermal properties Transistors

SpectralGPT: Spectral Remote Sensing Foundation Model

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arXiv 2023年

作者： Hong, Danfeng Zhang, Bing Li, Xuyang Li, Yuxuan Li, Chenyu Yao, Jing Yokoya, Naoto Li, Hao Ghamisi, Pedram Jia, Xiuping Plaza, Antonio Gamba, Paolo Benediktsson, Jon Atli Chanussot, Jocelyn Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China School of Mathematics Southeast University Nanjing210096 China Graduate School of Frontier Sciences the University of Tokyo Chiba277–8561 Japan Geospatial Data Management Technical University of Munich Munich85521 Germany Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Institute of Advanced Research in Artificial Intelligence Vienna1030 Austria School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10003 Spain Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Faculty of Electrical and Computer Engineering University of Iceland Reykjavik102 Iceland Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data;2) leverages 3D token generation for spatial-spectral coupling;3) captures spectrally sequential patterns via multi-target reconstruction;4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Multi-label Classification with High-rank and High-order Label Correlations

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

Multi-objective optimization for microgrid sizing, electric vehicle scheduling and vehicle-to-grid integration

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Lucas Zenichi Terada Marcelo Montandon Magalhães Juan Carlos Cortez João Soares Zita Vale Marcos J. Rider School of Electrical and Computer Engineering (FEEC) Universidade Estadual de Campinas (UNICAMP) Campinas 13083-852 São Paulo Brazil GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory ISEP Polytechnic of Porto Rua Dr. Antonio Bernardino de Almeida 431 Porto 4200-072 Portugal

This paper presents a multi-objective mixed integer linear programming (MILP) framework for the sizing of a microgrid that integrates distributed energy resource (DER), such as thermal generator (TG), photovoltaic system (PV) systems, and battery energy storage system (BESS), alongside electric vehicle (EV) scheduling and the procurement of electric vehicle charging station (EVCS). The proposed formulation incorporates uncertainty in generation and demand profiles, as well as contingencies that model off-grid scenarios, by means of a scenario-based stochastic programming approach. By employing a linearization approach that eliminates the need for additional binary variables for charging and discharging decisions, the optimization simultaneously minimizes total cost, greenhouse gas (GHG) emissions, and EV idle time. The model also determines an optimal vehicle-to-grid (V2G) price through a Nash equilibrium, which balances the interests of both the system operator and EV owners. Numerical results indicate that allowing moderate EV idle time can reduce the required number of EVCS, thus lowering capital investment without substantially affecting emissions. Furthermore, scenarios with stringent GHG constraints lead to a higher share of PV and BESS, increasing overall cost but reducing emissions. A case study demonstrates that the optimized microgrid can effectively handle off-grid conditions, with BESS and EV contributions maintaining supply reliability.

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

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Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management

Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

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Franklin Open

Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

Dipole-matter interactions governed by the asymmetry of Maxwell equations

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arXiv 2023年

作者： Zhong, Yuhan Wang, Chan Bian, Chenxu Chen, Xuhuinan Chen, Jialin Zhu, Xingjian Hu, Hao Low, Tony Chen, Hongsheng Zhang, Baile Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Physics Zhejiang University Hangzhou310027 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Physics University of Minnesota MinneapolisMN55455 United States Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Directionally molding the near-field and far-field radiation lies at the heart of nanophotonics and is crucial for applications such as on-chip information processing and chiral quantum networks. The most fundamental model for radiating structures is a dipolar source located inside a homogeneous matter. However, the influence of matter on the directionality of dipolar radiation is oftentimes overlooked, especially for the near-field radiation. We show that the dipole-matter interaction is intrinsically asymmetric and does not fulfill the duality principle, originating from the inherent asymmetry of Maxwell’s equations, i.e., electric charge and current are ubiquitous but their magnetic counterparts are non-existent to elusive. Moreover, we find that the asymmetric dipole-matter interaction could offer an enticing route to reshape the directionality of not only the near-field radiation but also the far-field radiation. As an example, both the near-field and far-field radiation directionality of Huygens dipole (located close to a dielectric-metal interface) would be reversed, if the dipolar position is changed from the dielectric region to the metal region. © 2023, CC BY.

关键词： Maxwell equations