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Mean-field games with finitely many players: independent learning and subjectivity

The Journal of Machine Learning Research 2024年第1期25卷 20523-20591页

作者： Bora Yongacoglu Gürdal Arslan Serdar Yüksel Department of Electrical and Computer Engineering University of Toronto Department of Electrical Engineering University of Hawaii at Manoa Department of Mathematics and Statistics Queen's University

Independent learners are agents that employ single-agent algorithms in multi-agent systems, intentionally ignoring the effect of other strategic agents. This paper studies mean-field games from a decentralized learning perspective with two aims: (i) to identify structure that can guide algorithm design, and (ii) to understand emergent behaviour in systems of independent learners. We study a new model of partially observed mean-field games with finitely many players, local action observability, and partial observations of the global state. Specific observation channels considered include (a) global observability, (b) mean-field observability, (c) compressed mean-field observability, and (d) only local observability. We establish conditions under which the control problem of a given agent is equivalent to a fully observed MDP, as well as conditions under which the control problem is equivalent only to a POMDP. Using the connection to MDPs, we prove the existence of perfect equilibrium among memoryless stationary policies under mean-field observability. Using the connection to POMDPs, we prove convergence of learning iterates obtained by independent learners under any of our observation channels. We interpret the limiting values as subjective value functions, which an agent believes to be relevant to its control problem. These subjective value functions are used to propose subjective Q-equilibrium, a new solution concept whose existence is proved under mean-field or global observability. Furthermore, we provide a decentralized independent learning algorithm, and by adapting the recently developed theory of satisficing paths to allow for subjectivity, we prove that it drives play to subjective Q-equilibrium. Our algorithm is decentralized, in that it uses only local information for learning and allows players to use different, heterogeneous policies during play. As such, it departs from the conventional representative agent approach common to other algorithms for mean-f

关键词： multi-agent reinforcement learning independent learners learning in games mean-field games decentralized systems

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On the Convergence of Finite Element Domain Decomposition Methods for Discontinuous Subdomain Interfaces

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4122-4127页

作者： Xin, Xi-Min Gao, Hong-Wei Wang, Shu Jian Lim, Qi Peng, Zhen Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China The University of New Mexico Applied Electromagnetics Group Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States NVIDIA Corporation Santa ClaraCA95050 United States University of Illinois at Urbana–Champaign Department of Electrical and Computer Engineering UrbanaIL61801 United States

In this communication, we propose a finite element domain decomposition method (FE-DDM) with Robin-type transmission conditions (TCs) at discontinuous subdomain interfaces. By definition, these discontinuous interfaces refer to impedance surfaces where electromagnetic (EM) fields exhibit discontinuities, which are common in applications of metamaterials/metasurfaces, frequency-selective surfaces, or thin dielectric layer approximation. Through a detailed convergence study, we propose an optimized set of parameters in the TC. The proposed TC provides convergence for both propagating and evanescent EM waves across subdomain interfaces and is valid for all resistivity values. Numerical experiments are supplied to validate our theoretical analysis, demonstrating the convergence and accuracy of our proposed work. © 1963-2012 IEEE.

关键词： Electromagnetic wave transmission

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm 2

Energy-Efficient Transmission in Wireless Networks Using Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajakumari, S. Hc, Kantharaju Ahmad, Shadab Sharma, Rupa Rani Sudha, S. Ramesh, T. R.M.D. Engineering College Department of Mathematics India Vemana Institute of Technology Department of Ai & Ml Karnataka Bengaluru India Samara University Department of Electrical and Computer Engineering Afar region Ethiopia G.L Bajaj Institute of Technology and Management Department of Applied Science Uttar Pradesh Greater Noida India Sri Ranganathar Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India R.M.K Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly. © 2024 IEEE.

关键词： Energy Efficiency Gravitational Search Algorithm Residual Networks Transmission Optimization

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WIP: A Systematic Approach for Outcome-Based Assessment 54

WIP: A Systematic Approach for Outcome-Based Assessment

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Belkhouche, F. Bendjilali, K. Shippensburg University of Pennsylvania Department of Computer Electrical and Software Engineering ShippensburgPA United States Raritan Valley Community College Department of Mathematics and Computer Science BranchburgNJ United States

ISBN: (纸本)9798350351507

This research-to-practice WIP paper describes methods for improving assessment. Assessment is crucial for pro-gram accreditation and continuous improvement in engineering education. Outcome-based learning is a recent student-centred approach that can be used perform assessment. Under this paradigm, clear outcomes related to the knowledge and skills that the students should have after completing the course are identified and assessed. The student attainments are measured against the outcomes using clear and well-defined metrics. In this paper, an outcome-based strategy is proposed and used for assessing courses in control systems. In order to improve assessment reliability, the learning outcomes are broken down into sub-outcomes, which are used as performance indicators. The advantage of the proposed method is twofold, first it provides more accurate and reliable metrics for measuring student attainment, and second, the assessment results are personalized, allowing to evaluate individual students and identify means to help them. This approach also allows to identify common mistakes and misconceptions. Data are collected for several semesters and analyzed. It turns out that the proposed approach allows to obtain more accurate and reliable assessment results. © 2024 IEEE.

关键词： engineering education

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Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

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computer Systems Science & engineering 2023年第8期46卷 1431-1445页

作者： A.Francis Saviour Devaraj T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Mohamad Adzhar Md Zawawi Mohamad Khairi Ishak Department of IT The University of Technology and Applied Sciences-IbriPO Box:466IbriPostal Code:516Sultanate of Oman Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong Tebal14300PenangMalaysia

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing *** article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for *** presented EMTA-SRSP technique majorly involves the optimal selection of routes in *** accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data *** the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust *** experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

关键词： Security wireless sensor networks trust factor routing protocol privacy

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities 13

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International electrical engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis. © 2025 IEEE.

关键词： Polynomial regression

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Optimizing Video Streaming Costs in the Cloud: A CNN Approach

Optimizing Video Streaming Costs in the Cloud: A CNN Approac...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350359633

As demand for high-quality, on-demand content skyrockets, cost optimization of video streaming in cloud en-vironments has become critical. Addressing this issue, our research introduces Deep Stream Cost (DSC), an innovative deep learning architecture for optimizing cloud-based video streaming costs. DSC employs convolutional neural networks (CNNs), dynamic resource scaling, and task offloading strategies, achieving considerable cost reduction while ensuring optimal video analytics performance. Our results are promising: system efficiency improved by up to 40%, resource usage was curtailed by 30%, and latency was reduced by 25%, all of which are substantial gains over traditional methods. Beyond cost reduction, DSC enhances scalability and availability, thanks to its real-time monitoring, predictive resource provisioning, and cost-awareness feedback loop. DSC's contribution to the development of cost-effective and scalable video delivery systems is highly relevant in today's era of cloud-based services and increasing demand for efficient resource utilization. © 2023 IEEE.

关键词： Convolutional neural networks

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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

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computer Systems Science & engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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Regenerative Braking Capabilities in E-Bike Vehicles: Comparison Between two Drive Architectures

Regenerative Braking Capabilities in E-Bike Vehicles: Compar...

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2024 IEEE International Conference on electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electrification Conference, ESARS-ITEC 2024

作者： Minervini, Marcello Giangrande, Paolo Corti, Fabio Malighetti, Paolo Mantione, Lorenzo University of Bergamo Department of Engineering and Applied Sciences Dalmine Italy Dinfo Università di Firenze Firenze Italy University of Bergamo Department of Management Information and Production Engineering Dalmine Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350373905

A comparative study between two regenerative braking strategies implemented on a 100 kg (bike + user) overall mass e-bike system is presented. The simulation models are built in MATLAB Simulink® environment and a commercial in-wheel direct-drive brushless DC (BLDC) motor is assumed as benchmark electrical machine. In the paper, a brief literature review about regenerative braking is firstly presented. Secondly a simulation comparison between two drive schemes is reported. The first scheme is composed by a buck converter and an inverter with six-step commutation. The second scheme, however, doesn't have the buck converter and uses two different Pulse Width Modulation techniques to control the inverter for motoring function and electric braking function. The aim of the work is to study the possibilities of the implementation and the advantages of this kind of brake, useful for extending the autonomy range of a LEV. © 2024 IEEE.

关键词： MATLAB

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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