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IEEE Open Access Journal of Power and Energy 2021年 8卷 77-84页

作者： Shetye, Komal S Kumar, Ramyaa Rathna Klauber, Cecilia Mao, Zeyu Overbye, Thomas J Gannon, Jennnifer Henderson, Michael Department of Electrical and Computer Engineering Texas AM University College StationTX77843 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Computational Physics Inc. BoulderCO80301 United States

Monitoring the elements associated with geomagnetic disturbances (GMDs), such as the earth's magnetic field, can help mitigate their negative impacts on the power grid. While there are existing magnetometers monitoring this in certain locations, they are currently sparse and have several gaps in coverage such as the southern US region. This paper describes the recently developed network of six magnetometers in the US state of Texas. Aspects such as the site selection, physical description of a magnetometer station, and data communication are described. Data quality is tested using correlation analysis among these magnetometers and pre-existing magnetic observatories. A real-time magnetic field data streaming and visualization setup is developed, with provisions to make the data available to researchers and industry. © 2020 IEEE.

关键词： Data visualization

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Graph Convolutional Networks based Muti-Label Deep Cross-Modal Hashing 13

Graph Convolutional Networks based Muti-Label Deep Cross-Mod...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Peng, Yi Zhang, Nian Jiang, Xin Xiong, Jiang School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing40044 China Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing40044 China College of Mathematics and Statistics Chongqing Three Gorges University Chongqing40044 China

ISBN: (纸本)9798350313925

Recently, multi-label deep cross-modal hashing (MDCH), which incorporates deep neural networks, hashing and multi-label learning for cross-modal retrieval tasks, has achieved excellent cross-modal retrieval results and thus became a highly popular area of research. Nevertheless, many existing MDCH methods concentrate on extracting information from multi-modal data, while neglecting the abundant semantic information in multiple labels. Few MDCH methods incorporate multi-label information, but they often treat labels as independent entities, ignoring the relationships between categories, which hinders the establishment of semantic connections among multi-modal data. In order to tackle the aforementioned challenges, we propose a graph convolutional networks based multi-label deep cross-modal hashing method (GMCH) in this paper. GMCH leverages two deep neural networks to generate hash representations from the original image-text pairs, during this process, a graph convolutional network is introduced to capture the category correlations of multi-labels and supervise the training of hash mapping. Experimental results on two commonly employed datasets validate the efficacy of the proposed GMCH method. You can find the code for our proposed GCMH at https://***/licher12/***. © 2023 IEEE.

关键词： Semantics

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A General Framework for Equivariant Neural Networks on Reductive Lie Groups 37

A General Framework for Equivariant Neural Networks on Reduc...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Batatia, Ilyes Geiger, Mario Munoz, Jose Smidt, Tess Silberman, Lior Ortner, Christoph Engineering Laboratory University of Cambridge CambridgeCB2 1PZ United Kingdom Department of Chemistry ENS Paris-Saclay Université Paris-Saclay Gif-sur-Yvette91190 France Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States EIA University FTA Group Antioquia Colombia Department of Mathematics University of British Columbia VancouverBCV6T 1Z2 Canada

ISBN: (纸本)9781713899921

Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quantum mechanics, quantum chromodynamics, molecular dynamics, computer vision, and imaging. In this paper, we present a general Equivariant Neural Network architecture capable of respecting the symmetries of the finite-dimensional representations of any reductive Lie Group G. Our approach generalizes the successful ACE and MACE architectures for atomistic point clouds to any data equivariant to a reductive Lie group action. We also introduce the lie-nn software library, which provides all the necessary tools to develop and implement such general G-equivariant neural networks. It implements routines for the reduction of generic tensor products of representations into irreducible representations, making it easy to apply our architecture to a wide range of problems and groups. The generality and performance of our approach are demonstrated by applying it to the tasks of top quark decay tagging (Lorentz group) and shape recognition (orthogonal group). © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Lie groups

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Orbital angular momentum of spin electromagnetic wave

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Results in Physics 2024年 61卷

作者： Sun, Zhiwei Du, Mingzhu Guo, Zhenkun Xu, Ruolei Zhao, Junming Feng, Yijun School of Electronic Science and Engineering Nanjing University 210023 China School of Microelectronics and Communication Engineering Chongqing University 400044 China Shanghai Radio Equipment Research Institute Shanghai Academy of Spaceflight Technology 201109 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Spin angular momentum (SAM) and Orbital angular momentum (OAM) are two distinct forms of electromagnetic angular momentum (AM). Generally, circularly-polarized (CP) wave is deemed as the typical form of spin wave, which carries SAM only. In this work, the OAM characteristic of spin wave is found after stating the CP wave into the cylindrical coordinate: the spin wave contains two components along r and ϕ directions, which carry both SAM and OAM. Based on the OAM character of spin wave, an electromagnetic waveform with SAM in the center and SAM-OAM beside is demonstrated. In addition, since spin wave carries OAM naturally, a spin–orbit coupling mechanism is performed, which demands no spin Pancharatnam–Berry phase nor any phase modulation structure. All the results have been experimentally verified. Our research can be utilized in the theoretical studies of rotational electromagnetic and may be potentially applied in metamaterial, information and quantum research areas. © 2024

关键词： Angular momentum Helical beam waves Polarization-phase Spatial-phase Spin-orbit coupling

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Optimizing resource allocation in flex-grid entanglement distribution networks

Optimizing resource allocation in flex-grid entanglement dis...

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2022 IEEE Photonics Conference, IPC 2022

作者： Alnas, Jude Alshowkan, Muneer Nageswara Rao, S.V. Peters, Nicholas A. Lukens, Joseph M. University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL35401 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce a model for the distribution of frequency-polarization hyper-entangled photon pairs in a flexible-grid optical network. In order to optimize entanglement fidelity and entangled bit rate, we apply a geneti... 详细信息

ISBN: (数字)9781665434874

ISBN: (纸本)9781665434874

关键词： Genetic algorithms

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Research on Biomimetic Design Methods for Humanoid Robot Thigh*

Research on Biomimetic Design Methods for Humanoid Robot Thi...

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IEEE International Conference on Robotics and Biomimetics

作者： Daming Nie Anhuan Xie Lingyu Kong Yu Zhang Gang Zheng Yili Fu Jason Gu Intelligent Robot Research Center Zhejiang Laboratory Hangzhou China School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

Human bones have formed the preferred configuration for high-strength and lightweight after long-time evolution. Taking human’s longest and strongest bone - the femur - as an example, it is consist of two characteristic layers, i.e. the substantia compacta and the substantia spongiosd. This article innovatively imitates the structural characteristics of human femur, the thigh of humanoid robot is designed in form of "variable thickness shell + variable density lattice". The thickness of shell and the density of lattice are adjusted by the initial stress distribution individually. Results show that the weight of shell and lattice of the thigh structure can be reduced by 20% under reasonable mapping relationship of "stress - shell thickness" and "stress - lattice rod diameter", while the structural stiffness meets the application requirements. Finally, the limiting factors of the "variable thickness shell + variable density lattice" structure designing approach are analyzed, and potential measures for optimizing the design method of the humanoid robot thigh in the future are described.

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A 0.9–1.4-GHz Q-Enhanced CMOS Tunable Bandpass Filter With Constant Fractional Bandwidth and Two Transmission Zeros

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IEEE Microwave and Wireless Technology Letters 2023年第6期33卷 667-670页

作者： Kun Li Xiang Li Shuangyang Wu Pei-Ling Chi Tao Yang School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan

A tunable differential $Q$ -enhanced bandpass filter covering 0.9–1.4 GHz with constant fractional bandwidth (FBW) is proposed using 40-nm CMOS technology in this letter. The proposed filter is constructed by two resonators which consist of ON-chip integrated inductors and varactors. The inductors of the two resonators are placed close to each other to form the mainline magnetic coupling of the filter. Besides the mainline coupling, the source port and load port are connected directly by capacitors, forming the source-to-load coupling. With the proposed structure, two transmission zeros (TZs) can be obtained, which can greatly increase the stopband rejection. Meanwhile, nMOS cross-coupled pairs are used as the $Q$ -enhanced cells to reduce the insertion loss (IL) and further improve the filter selectivity. A filter prototype based on 40-nm CMOS process is fabricated to validate the proposed structure. The center frequency (CF) of the proposed filter can be tuned from 0.9 to 1.4 GHz while keeping the FBW constant at 8%.

关键词： Resonator filters Band-pass filters Microwave filters Filtering theory Couplings Equivalent circuits Magnetic separation

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Short-term household load forecasting model based on variational mode decomposition and gated recurrent unit with attention mechanism

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 143-153页

作者： Lin, Fu Liu, Shi-Jian Chao, Han-Chieh Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The household load is an important part of the load. Accurate short-term household load forecasting is of great help to issues such as power price formulation, demand response, or power transmission. This paper proposed a forecasting model based on Variational Mode Decomposition (VMD) and Gated Recurrent Unit (GRU) combined with self-attention mechanism (SAM) and attention mechanism (AM) for short-term household load forecasting. First, we use VMD to decompose household electricity load data, extract sub-sequences with strong periodic information, and reorganize the original data sets with sub-sequences and data pre-processed. The new data sets contain more abundant information. Secondly, GRU combined with SAM and AM forecasting model for learning and training to obtain results. Finally, RMSE and MAPE evaluation functions are used for method evaluation to verify the superiority of the proposed scheme. The RMSE and MAPE of the proposed method are 0.193 kW and 15.567%, respectively, which are more favorable than traditional GRU. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Variational mode decomposition

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Piezoelectric Nanocavity Interface for Strong Coupling between a Superconducting Circuit, Phonon, and Spin

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Physical Review Applied 2023年第6期19卷 064051-064051页

作者： Hamza Raniwala Stefan Krastanov Lisa Hackett Matt Eichenfield Dirk R. Englund Matthew E. Trusheim Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Sandia National Laboratories Albuqurque New Mexico USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA DEVCOM Army Research Laboratory Adelphi Maryland 20783 USA

We introduce a hybrid tripartite quantum system for strong coupling between a semiconductor spin, a mechanical phonon, and a microwave excitation of a superconducting circuit. Consisting of a piezoelectric resonator with an integrated diamond strain concentrator, this system achieves microwave-acoustic and spin-acoustic coupling rates of approximately megahertz or greater, allowing simultaneous ultrahigh cooperativities (approximately 103 and approximately 102, respectively). From finite-element modeling and master-equation simulations, we estimate superconducting-circuit-to-spin quantum state transfer fidelities exceeding 0.95 on the basis of separately demonstrated device parameters. We anticipate that this device will enable hybrid quantum architectures that leverage the advantages of both superconducting circuits and solid-state spins for information processing, memory, and networking.

关键词： Optomechanics Piezoelectric devices Quantum information with hybrid systems Quantum interconnects Semiconductor quantum optics Superconducting quantum optics Transducers

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