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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Effective Two-Stage Double Auction for Dynamic Resource Trading in Edge Networks via Overbooking

arXiv

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arXiv 2025年

作者： Wu, Sicheng Liwang, Minghui Wang, Deqing Wang, Xianbin Wu, Chao Tang, Junyi Li, Li Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China The Department of Electrical and Computer Engineering Western University ON Canada The School of Public Affairs Zhejiang University Hangzhou310027 China School of Information Science and Engineering Lanzhou University Lanzhou China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

To facilitate responsive and cost-effective computing resource scheduling and service delivery over edge-assisted mobile networks, this paper investigates a novel two-stage double auction methodology via utilizing an interesting idea of resource overbooking to overcome dynamic and uncertain nature from edge servers (sellers) and demand from mobile devices (as buyers). The proposed auction integrates multiple essential factors such as social welfare maximization and decision-making latency (e.g., the time for determining winning seller-buyer pairs) reduction, by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for handling residual resource demands during actual transactions. In particular, by applying a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Through extensive experiments, we demonstrate good performance in social welfare, time efficiency, and computational scalability, outstripping conventional methods in dynamic edge computing settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and Weighted Centroid Method

SSRN

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SSRN 2023年

作者： Sankaranarayanan, Suresh Rajaram, V. Srividhya, S. Lorenz, Pascal Rodrigues, Joel J.P.C. Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India University of Haute Alsace Colmar France Qingdao266555 China Instituto de Telecomunicações Covilhã6201-001 Portugal

Localization of nodes is critical for acquiring access to diverse nodes that would provide services in remote places. Single-anchor localization techniques suffer from a co-linearity problem, resulting in poor performance. The reliable multiple anchor selection method can be computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. There has been work where researchers have employed nature-inspired algorithms over conventional optimization method towards localization of unknown nodes in WSN. These methods have challenges like longer time and lot of processing power to solve. In addition, these methods are sensitive to parameter selection impacting the ***, we in this research work have employed the nature-inspired crow search algorithm for selecting the suitable anchors from the population thereby reducing errors in identifying the location of unknown nodes. In addition, the weighted centroid method is proposed for the identification of the exact location of an unknown node. This makes the weighted centroid method and the crow search algorithm a more trustworthy and efficient way for node localization in wireless sensor networks. The Crow Search-based Weighted Centroid Localization (CS-WCL) has outperformed WCL and DV-Hop with the use of the best anchor node in terms of Localization Error, Total Data packets, and Energy Consumption. This resulted in CSWCL outperforming other nature-inspired algorithms for node localization. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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ASSESSING THE ROBUSTNESS OF CRITICAL BEHAVIOR IN STOCHASTIC CELLULAR AUTOMATA

arXiv

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arXiv 2022年

作者： Pontes-Filho, Sidney Lind, Pedro Nichele, Stefano Department of Computer Science Oslo Metropolitan University Oslo Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway AI Lab - OsloMet Artificial Intelligence Lab Oslo Norway NordSTAR Nordic Center for Sustainable and Trustworthy AI Research Oslo Norway Department of Holistic Systems Simula Metropolitan Centre for Digital Engineering Oslo Norway Department of Computer Science and Communication Østfold University College Halden Norway

There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able to remain in it under perturbations. In this work, we address the question of robustness of critical systems to noise. In particular, we investigate the robustness of stochastic cellular automata (CAs) at criticality. A stochastic CA is one of the simplest stochastic models showing criticality. The transition state of stochastic CA is defined through a set of probabilities. We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise. We present the results using error metrics of the resulting power-law fitting, such as Kolmogorov-Smirnov statistic and Kullback-Leibler divergence. We discuss the implication of our results in regards to future realization of brain-inspired artificial intelligence *** Codes 68T01 © 2022, CC BY.

关键词： Stochastic models

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Summary of Energy *** Conference 2022

引用

Energy informatics 2022年第SUPPL 4期5卷 1-4页

作者： Ma, Zheng Jørgensen, Birte Holst Chen, Guangchao Madsen, Henrik Duan, Hongbo da Silva, Luiz Carlos Pereira Jørgensen, Bo Nørregaard SDU Center for Energy Informatics The Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense Denmark Department of Wind Energy Society Market and Policy Technical University of Denmark Lyngby Denmark College of Materials Sciences and Opto-Electronic Technology University of Chinese academy of sciences Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark School of Economics & Management University of Chinese Academy of Sciences Beijing China Campinas Brazil

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes

Seamless Decision-Making in the Big Data Era: A Modular Appr...

引用

International Conference on Electrical, Communication, and computer Engineering (ICECCE)

作者： Mohamed Zemmouri Fatma Zohra Laalam Okba Kazar Yassine Himeur Adel Oulefki Chahrazad Toumi Wathiq Mansoor Shadi Attala Artificial Intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla Algeria Department of Computer Science - LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security - College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah United Arab Emirates

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source.

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Partially Observable Markov Decision Processes in Robotics: A Survey

arXiv

引用

arXiv 2022年

作者： Lauri, Mikko Hsu, David Pajarinen, Joni Department of Informatics Universität Hamburg Germany Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Automation Aalto University Finland Intelligent Autonomous Systems Technische Universität Darmstadt Germany

Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

引用

IEEE/SICE International Symposium on System Integration

作者： Mert Bozkurtlar Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Management Istanbul Technical University Harbiye Maçka/Beşiktaş/İstanbul Türkiye Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Ayazaga Sariyer/İstanbul Türkiye Japan Society for the Promotion of Science (JSPS) at the Nakadai Lab Tokyo Institute of Technology Honda Research Institute Japan Co. Ltd Saitama Japan

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments.

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Comparison of two newly suggested power, refrigeration, and hydrogen production, for moving towards sustainability schemes using improved solar-powered evolutionary algorithm optimization

引用

Chemosphere 2023年第1期336卷 139160页

作者： Hai, Tao Abd El-Salam, Nasser M. Kh, Teeba Ismail Chaturvedi, Rishabh El-Shafai, Walid Farhang, Babak School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Guizhou Duyun558000 China Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Natural Science Department Community College King Saud University Riyadh12642 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University UP Mathura281001 India Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Energy Technology Aalborg University Denmark

In the third millennium, developing countries will confront significant environmental problems such as ozone depletion, global warming, the shortage of fossil resources, and greenhouse gas emissions. This research looked at a multigenerational system that can generate clean hydrogen, fresh water, electricity, heat, and cooling. The system's components include Rankine and Brayton cycles, an Organic Rankine Cycle (ORC), flash desalination, an Alkaline electrolyzer, and a solar heliostat. The proposed process has been compared for two different start-up modes with a combustion chamber and solar heliostat to compare renewable and fossil fuel sources. This research evaluated various characteristics, including turbine pressure, system efficiency, solar radiation, and isentropic efficiency. The energy and exergy efficiency of the proposed system were obtained at around 78.93% and 47.56%, respectively. Exergy study revealed that heat exchangers and alkaline electrolyzers had the greatest exergy destruction rates, at 78.93% and 47.56%, respectively. The suggested system produces 0.04663 kg/s of hydrogen. Results indicate that at the best operational conditions, the exergetic efficiency, power, and hydrogen generation of 56%, 6000 kW, and 1.28 kg/s is reached, respectively. Also, With a 15% improvement in the Brayton cycle's isentropic efficacy, the quantity of hydrogen produced increases from 0.040 kg/s to 0.0520 kg/s. © 2023 Elsevier Ltd

关键词： Solar energy

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Panel Session: A Vision for the Next 15 Years of Computing Education

Panel Session: A Vision for the Next 15 Years of Computing E...

引用

Frontiers in Education (FIE) Conference

作者： Adrienne Decker Monica M. McGill Briana Morrison Manuel A. Pérez-Quiñones Aman Yadav Department of Engineering Education University at Buffalo Buffalo NY USA Institute for Advancing Computing Education Peoria IL USA Computer Science University of Virginia Charlottesville VA USA Software and Information Systems University of North Carolina Charlotte Charlotte NC USA College of Education and College of Natural Science Michigan State University East Lansing MI USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

The session will present the final report of an NSF sponsored workshop tasked with creating a vision for computing education for the next 15 years. Within several broad themes identified, the panelists were asked to probe further into where the field should be heading and what difficult questions we need to tackle. We will use the final report as a basis for discussion with the attendees and ask them the same hard questions we asked ourselves when writing this report to see how these questions shape their views of where computing education needs to be in the next 15 years.

关键词： Shape Conferences Education Writing Probes

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