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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 240-260页

作者： Ibrahim, Mostafa Ekin, Sabit Imran, Ali Texas A&M University Department of Engineering Technology College StationTX77843 United States Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom The University of Oklahoma AI4Networks Research Center School of Electrical and Computer Engineering TulsaOK74135 United States

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users. © 2023 CCBY.

关键词： Markov processes

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Next-Generation Satellite IoT Networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer 35

Next-Generation Satellite IoT Networks: A HAPS-Enabled Solut...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Fettes, Ethan Madoery, Pablo G. Yanikomeroglu, Halim Kurt, Gunes Karabulut Bellinger, Colin Martel, Stephane Ahmed, Khaled Siddiqui, Sameera Lab Department of Systems and Computer Engineering Canada Polytechnique Montréal Poly-Grames Research Center Department of Electrical Engineering Montréal Canada National Research Council Canada Canada Satellite Systems Mda Canada Defence Research and Development Canada Canada

ISBN: (纸本)9798350362244

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research. © 2024 IEEE.

关键词： Delay tolerant networks

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PILOT: AN O(1/K)-CONVERGENT APPROACH FOR POLICY EVALUATION WITH NONLINEAR FUNCTION APPROXIMATION 12

PILOT: AN O(1/K)-CONVERGENT APPROACH FOR POLICY EVALUATION W...

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12th International Conference on Learning Representations, ICLR 2024

作者： Liu, Zhuqing Zhang, Xin Liu, Jia Zhu, Zhengyuan Lu, Songtao Department of Electrical and Computer Engineering The Ohio State University United States Department of Statistics Iowa State University United States IBM Research IBM Thomas J. Watson Research Center United States

Learning an accurate value function for a given policy is a critical step in solving reinforcement learning (RL) problems. So far, however, the convergence speed and sample complexity performances of most existing policy evaluation algorithms remain unsatisfactory, particularly with non-linear function approximation. This challenge motivates us to develop a new path-integrated primal-dual stochastic gradient (PILOT) method, that is able to achieve a fast convergence speed for RL policy evaluation with nonlinear function approximation. To further alleviate the periodic full gradient evaluation requirement, we further propose an enhanced method with an adaptive-batch adjustment called PILOT+. The main advantages of our methods include: i) PILOT allows the use of constant step sizes and achieves the O(1/K) convergence rate to first-order stationary points of non-convex policy evaluation problems;ii) PILOT is a generic single-timescale algorithm that is also applicable for solving a large class of non-convex strongly-concave minimax optimization problems;iii) By adaptively adjusting the batch size via historical stochastic gradient information, PILOT+ is more sample-efficient empirically without loss of theoretical convergence rate. Our extensive numerical experiments verify our theoretical findings and showcase the high efficiency of the proposed PILOT and PILOT+ algorithms compared with the state-of-the-art methods. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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A real-world data collection framework for a fused dataset creation for joint human and remotely operated vehicle monitoring and anomalous command detection

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 432-445页

作者： Rafaella Elia George Plastiras Eva Pettemeridou Antonis Savva Theocharis Theocharides Department of Electrical and Computer Engineering University of CyprusNicosiaCyprus KIOS Research and Innovation Center of Excellence University of CyprusNicosiaCyprus Center for Applied Neuroscience(CAN) University of CyprusNicosiaCyprus

Unmanned Remotely Operated Vehicles(ROVs)are widely used in safety-critical missions,spanning across monitoring,surveillance,and search and rescue *** most cases,the human operator controls the ROV under stressful conditions and in harsh *** physical and mental states of the operator are vital as they may cause involuntary movements,issuing unintentional commands due to fatigue or ***,a joint monitoring mechanism that will assess the operator's commands can be beneficial in preventing potentially catastrophic events,such as *** this end,in this study,we propose a data collection framework for creating a fused dataset,consisting of data obtained from both the human operator and the ROV,and we integrate stress induction to stimulate stress and fatigue that may affect operators during *** optimise the dataset,which includes the operator's physiological signals and inertial measurement unit(IMU)sensor data from the ROV,using data fusion approaches and feature *** evaluate the dataset through the statistical analysis,illustrating a statistical difference between data characterising normal and involuntary ***,we evaluate the optimised and original datasets over a variety of classifiers,demonstrating an accuracy of 94%and 95%,respectively.

关键词： operator fused operators

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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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Dual objective bandit for best channel selection in hybrid band wireless systems

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4115-4125页

作者： Hashima, Sherief M. Fouda, Mostafa Hatano, Kohei Kasban, Hany Mohamed, Ehab Mahmoud Fukuoka819-0395 Japan Engineering Department Nuclear Research center Egyptian Atomic Energy Authority Inshas Cairo13759 Egypt Electrical and Computer Engineering Department College of Science and Engineering Idaho State University PocatelloID83209 United States Faculty of Arts and Science Kyushu University Fukuoka819-0395 Japan Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Wadi Addwasir13759 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

This paper manipulates a creative online learning solution for optimal band/channel assignment in hybrid radio frequency and visible light communication (RF/VLC) systems. Typically, the multiband transmitter (Tx) has no prior information about different channel characteristics including their payoffs (achievable data rate) and energy consumption outcome. Practically, Tx has to choose the best reward arm/band with the lowest energy consumption to prolong its limited battery capacity. Hence, we envision a lightweight cost-aware multi-armed bandit (CA-MAB) as a proper realistic solution to the cumbersome and slowly convergent ordinary band assignment methods, where the transmitter/player intends not only to maximize his cumulative payoff (achievable data rate) but also to mitigate his cost (battery consumption due to the utilized band). Therefore, we propose a dual objective MAB scheme to manage such problem intelligently. Numerical simulations indicate that proposed method outperforms naive MAB versions, including Thompson sampling (TS), Upper Confidence bound (UCB), and traditional hybrid band selection (HBA) approaches, correspondingly. Especially, our proposed algorithm delivers 99% of the optimal performance concerning energy consumption, achievable data rate, and convergence speed. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Millimeter waves

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High throughput and rapid isolation of extracellular vesicles and exosomes with purity using size exclusion liquid chromatography

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Bioactive Materials 2024年第10期40卷 683-695页

作者： Kshipra S.Kapoor Kristen Harris Kent A.Arian Lihua Ma Beatriz Schueng Zancanela Kaira A.Church Kathleen M.McAndrews Raghu Kalluri Department of Cancer Biology Metastasis Research CenterUniversity of Texas MD Anderson Cancer CenterHoustonTXUSA Department of Electrical and Computer Engineering Rice UniversityHoustonTXUSA Department of Bioengineering Rice UniversityHoustonTXUSA Department of Molecular and Cellular Biology Baylor College of MedicineHoustonTXUSA

Extracellular vesicles(EVs)have emerged as potential biomarkers for diagnosing a range of diseases without invasive *** vesicles also offer advantages compared to synthetic vesicles for delivery of various drugs;however,limitations in segregating EVs from other particles and soluble proteins have led to inconsistent EV retrieval rates with low levels of ***,we report a new high-yield(88.47%)and rapid(<20 min)EV isolation method termed size exclusion–fast protein liquid chromatography(SE-FPLC).We show SE-FPLC can effectively isolate EVs from multiple sources including EVs derived from human and mouse cells and serum *** results indicate that SE-FPLC can successfully remove highly abundant protein contaminants such as albumin and lipoprotein complexes,which can represent a major hurdle in large scale isolation of *** high-yield nature of SE-FPLC allows for easy industrial scaling up of EV production for various clinical ***-FPLC also enables analysis of small volumes of blood for use in point-of-care diagnostics in the ***,SE-FPLC offers many advantages over current EV isolation methods and offers rapid clinical translation.

关键词： Extracellular vesicles Size exclusion-fast performance liquid chromatography Isolation methods

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Enhancing Modulation Bandwidth of Parallel Micro-LED Arrays for Visible Light Communication

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第6期46卷 944-947页

作者： Guo, Wen-An Wan, Ming-He Liu, Shi-Biao Liu, Zhong Lu, Ting-Wei Chen, Guo-Long Lu, Yi-Jun Kuo, Hao-Chung Wu, Ting-Zhu Chen, Zhong Xiamen University National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan

In this letter, parallel micro-LED arrays with numbers of 1×1, 1×2, 2×2 and 2×3 were fabricated and the p-electrode patterns of arrays were optimized. The results show that both the light output power (LOP) and modulation bandwidth increase with the parallel array numbers. Moreover, optimizing the p-electrode pattern can effectively facilitate current spreading, mitigate thermal crowding, and enhance the LOP. Under combined with the LOP and thermal effect, the 2 × 2 ring-electrode parallel array achieved the highest modulation bandwidth of 172 MHz and maximum LOP of 5.70 mW, which were improved by 17.28% and 13.98%, respectively. This work provides a reference to design micro-LED chip for high bandwidth. © 1980-2012 IEEE.

关键词： Bandwidth

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Recent advances in heterogeneous catalysis of solar-driven carbon dioxide conversion

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Journal of Environmental Sciences 2024年第6期140卷 165-182页

作者： Jun Xu Farzaneh Arabpour Roghabadi Ying Luo Vahid Ahmadi Qian Wang Zheng Wang Hong He State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China College of Chemistry and Chemical Engineering Henan Polytechnic UniversityJiaozuoHenan 454000China Department of Process Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Optoelectronics and Nanophotonics Research Group Faculty of Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran Graduate School of Engineering Nagoya UniversityFuro-choChikusa-kuNagoya 464-8603Japan

Solar-driven carbon dioxide(CO_(2))conversion including photocatalytic(PC),photoelectrochemical(PEC),photovoltaic plus electrochemical(PV/EC)systems,offers a renewable and scalable way to produce fuels and high-value chemicals for environment and energy *** review summarizes the basic fundament and the recent advances in the field of solar-driven CO_(2)*** the visible-light absorption is an important strategy to improve solar energy conversion *** separation and migration of photogenerated charges carriers to surface sites and the surface catalytic processes also determine the photocatalytic *** engineering including co-catalyst loading,defect engineering,morphology control,surface modification,surface phase junction,and Z-scheme photocatalytic system construction,have become fundamental strategies to obtain high-efficiency *** to photocatalysis,these strategies have been applied to improve the conversion efficiency and Faradaic efficiency of typical PEC *** PV/EC systems,the electrode surface structure and morphology,electrolyte effects,and mass transport conditions affect the activity and selectivity of electrochemical CO_(2)***,the challenges and prospects are addressed for the development of solar-driven CO_(2)conversion system with high energy conversion efficiency,high product selectivity and stability.

关键词： Solar-driven CO_(2)reduction Photocatalytic system Photoelectrochemical system Photovoltaic plus electrochemical system Surface/interface engineering

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