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arXiv 2024年

作者： Kutz, J. Nathan Reza, Maryam Faraji, Farbod Knoll, Aaron Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA98195 United States Imperial Plasma Propulsion Laboratory Department of Aeronautics Imperial College London London United Kingdom

Reduced order models are becoming increasingly important for rendering complex and multiscale spatio-temporal dynamics computationally tractable. The computational efficiency of such surrogate models is especially important for design, exhaustive exploration and physical understanding. Plasma simulations, in particular those applied to the study of E × B plasma discharges and technologies, such as Hall thrusters, require substantial computational resources in order to resolve the multidimentional dynamics that span across wide spatial and temporal scales. Although high-fidelity computational tools are available to simulate such systems over limited conditions and in highly simplified geometries, simulations of full-size systems and/or extensive parametric studies over many geometric configurations and under different physical conditions are computationally intractable with conventional numerical tools. Thus, scientific studies and industrially oriented modeling of plasma systems, including the important E × B technologies, stand to significantly benefit from reduced order modeling algorithms. We develop a model reduction scheme based upon a Shallow REcurrent Decoder (SHRED) architecture. The scheme uses a neural network for encoding limited sensor measurements in time (sequence-to-sequence encoding) to full state-space reconstructions via a decoder network. Based upon the theory of separation of variables, the SHRED architecture is capable of (i) reconstructing full spatio-temporal fields with as little as three point sensors, even the fields that are not measured with sensor feeds but that are in dynamic coupling with the measured field, and (ii) forecasting the future state of the system using neural network roll-outs from the trained time encoding model. The SHRED reduced order model architecture is demonstrated on the plasma dynamics in a 2D configuration representative of a radial-azimuthal plane of a typical Hall thruster geometry. Copyright © 2024, The Author

关键词： Geometry

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Fusion of Parameterized and Physics-oriented Statistical Surrogate Models for EM Coupling on Wires in Complex Electronic Enclosures

Fusion of Parameterized and Physics-oriented Statistical Sur...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Shen Lin Sangrui Luo Yang Shao Zhen Peng Bisrat D. Addissie Zachary B. Drikas Dept. Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA Tactical Electronic Warfare Division U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

In this paper, we explore a novel approach in the domain of statistical electromagnetics for high-frequency coupling on wires installed in complex electronic enclosures. The study involves the integration of two distinct types of statistical surrogate models: parameterized models, which are based on predefined parameter spaces for internal wire/cable components, and physics-oriented statistical models, which leverage statistical representations of cavity eigenfunctions and eigenvalues. The fusion of parameterized and physics-oriented statistical surrogate models allows for a comprehensive and versatile statistical analysis, accommodating the complex details and variability present in real-world electronic systems. The proposed work has been validated numerically, including the commercial software and traditional Monte Carlo simulation.

关键词： Couplings Uncertain systems Uncertainty Wires Green products Stochastic processes Voltage Software Numerical models Electromagnetics

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Study of the Behavior of Water Droplets under the Influence of a Uniform Electric Field on Samples of Borosilicate Glass

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Journal of engineering Science and Technology Review 2022年第7期14卷 67-70页

作者： Baroutis, Ioannis Danikas, Michael G. Sarathi, Ramanujam Democritus University of Thrace School of Engineering Department of Electrical and Computer Engineering Xanthi67100 Greece Indian Institute of Technology Madras Department of Electrical Engineering High Voltage Laboratory Chennai600 036 India

This paper investigates the behavior of water droplets on the surface of borosilicate glass samples. Borosilicate glass is used in MEMS and this is due to its special properties, such as its excellent insulating property, its inertness to most chemical substances, its easy connection to silicon and its optical permeability. In this paper, water droplet conductivities and wet etched borosilicate glass samples– are investigated. It seems that droplet conductivity plays a dominant role in determining the flashover voltage of the aforementioned samples. © 2021

关键词： Surface discharges

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The Age of Generative AI and AI-Generated Everything

arXiv

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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Outlier-Robust Training of Machine Learning Models

arXiv

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arXiv 2024年

作者： Talak, Rajat Georgiou, Charis Shi, Jingnan Carlone, Luca Laboratory of Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M-estimation, which is popular in robotics and computer vision, and another using a risk-minimization framework, which is popular in deep learning. We first show that a simple modification of the Black-Rangarajan duality provides a unifying view. The modified duality brings out a definition of a robust loss kernel σ that is satisfied by robust losses in both the literatures. Secondly, using the modified duality, we propose an Adaptive Alternation Algorithm (AAA) for training machine learning models with outliers. The algorithm iteratively trains the model by using a weighted version of the non-robust loss, while updating the weights at each iteration. The algorithm is augmented with a novel parameter update rule by interpreting the weights as inlier probabilities, and obviates the need for complex parameter tuning. Thirdly, we investigate convergence of the adaptive alternation algorithm to outlier-free optima. Considering arbitrary outliers (i.e., with no distributional assumption on the outliers), we show that the use of robust loss kernels σ increases the region of convergence. We experimentally show the efficacy of our algorithm on regression, classification, and neural scene reconstruction problems. © 2024, CC BY.

关键词： Contrastive Learning

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Structure Matters: Dynamic Policy Gradient

arXiv

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arXiv 2024年

作者： Klein, Sara Zhang, Xiangyuan Başar, Tamer Weissmann, Simon Döring, Leif School of Business Informatics and Mathematics University of Mannheim Mannheim68138 Germany Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

In this work, we study γ-discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework directly integrates dynamic programming with (any) policy gradient method, explicitly leveraging the Markovian property of the environment. DynPG dynamically adjusts the problem horizon during training, decomposing the original infinite-horizon MDP into a sequence of contextual bandit problems. By iteratively solving these contextual bandits, DynPG converges to the stationary optimal policy of the infinite-horizon MDP. To demonstrate the power of DynPG, we establish its non-asymptotic global convergence rate under the tabular softmax parametrization, focusing on the dependencies on salient but essential parameters of the MDP. By combining classical arguments from dynamic programming with more recent convergence arguments of policy gradient schemes, we prove that softmax DynPG scales polynomially in the effective horizon (1 − γ)−1. Our findings contrast recent exponential lower bound examples for vanilla policy gradient. © 2024, CC BY-SA.

关键词： Markov processes

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Mid-Wave Infrared Black Phosphorus Photodetector Enhanced by an Integrated Metasurface Grating

Mid-Wave Infrared Black Phosphorus Photodetector Enhanced by...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Lien, Max R. Wang, Nan Wu, Jiangbin Soibel, Alexander Gunapala, Sarath D. Wang, Han Povinelli, Michelle L. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Mork Family Department of Chemical Engineering & Materials Science University of Southern California Los AngelesCA90089 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91030 United States

We designed, fabricated, and characterized mid-wave infrared black phosphorus photodetectors with integrated m e t al-insulator-met al metasurface gratings. We show that our metasurface grating increases responsivity ... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Interacting diffusion processes for event sequence forecasting 24

Interacting diffusion processes for event sequence forecasti...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mai Zeng Florence Regol Mark Coates Department of Electrical and Computer Engineering McGill University Montreal QC Canada and International Laboratory on Learning Systems (ILLS) Montreal QC Canada and Mila Québec AI Institute Montreal QC Canada

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Many-Objective Evolutionary Algorithms for Optimization of Vehicle-Road Cooperation Systems Based on Intelligent Wireless Sensor Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Tingting Zhang, Lei Ding, Xuefeng China Three Gorges University School of Economics and Management Yichang443005 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443005 China China Three Gorges University College of Computer and Information Technology Yichang443005 China

The vehicle road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multi-source traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS. Nonetheless, the complexity of communication environment and the massive transmission of information have made VRCS optimization an increasingly difficult task, involving the intricate processes of coverage and routing optimization within IWSNs. This challenge essentially transforms into a very complicated many-objective optimization problem, where multiple conflicting objectives and resource constraints need to be tackled simultaneously. Traditional optimization methods struggle to offer satisfactory solutions. Our study introduces a many-objective evolutionary algorithm that concurrently manages coverage and routing optimization in IWSNs. Initially, we present a three-dimensional sensing model to delineate the perception range of an individual sensor. Subsequently, we propose a comprehensive four-objective optimization model, encompassing coverage, connectivity, energy consumption, and deployment cost, to accurately depict the performance of IWSNs. Especially this integrated model includes a swift connectivity evaluation method and a specialized routing forwarding strategy. Furthermore, we suggest a node sleeping strategy to minimize energy consumption further. To find a solution to the resultant many-objective optimization problem, which refers to numerous objectives and constraints, we developed a swarm optimization algorithm based on rapid fitness evaluation strategy and differential co-evolution, aimed at boosting convergence speed and accuracy of the proposed approach. Comparative experiments indicate that our model and algorithm outperform other algorithms in terms of efficiency and effectiveness. © 2025 IEEE.

关键词： Optimization algorithms

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