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12th International Electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Experimental design of paraffin/methylated melamine-formaldehyde microencapsulated composite phase change material and the application in battery thermal management system

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Journal of Materials science & Technology 2024年第2期169卷 124-136页

作者： Que Huang Silong Wang Jichun He Dengji Xu Safaa NAbdou Mohamed MIbrahim Shiqi Sun Yanjun Chen Handong Li Ben Bin Xu Changcheng Liu Zeinhom M.El-Bahy Zhanhu Guo School of Environment and Safety Engineering North University of ChinaTaiyuan 030051China Institute of Advanced Energy Materials and Systems North University of ChinaTaiyuan 030051China School of Resources and Safety Engineering Central South UniversityChangsha 410010China School of Medical and BioInformation Engineering Northeastern UniversityShenyang 110819China Department of Chemistry Khurmah University CollegeTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Chemistry College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mechanical and Construction Engineering Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle Upon Tyne NE18STUK College of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr City 11884CairoEgypt

In order to maintain the optimal operating temperature of the battery surface and meet the demand for thermal storage technology,battery thermal management system based on phase change materials has attracted increasing *** this work,a kind of core-shell structured microcapsule was synthesized by an in-situ polymerization,where paraffin was used as the core,while methanol was applied to mod-ify the melamine-formaldehyde shell to reduce toxicity and improve thermal ***,three different types of heat conductive fillers with the same content of 10 wt.%,i.e.,nano-Al_(2)O_(3),nano-ZnO and carbon nanotubes were added,generating *** microcapsules were uniform,and were not affected by the thermal fillers,which were evenly dispersed *** composite sample with carbon nanotubes(10 wt.%)showed the highest thermal conductivity of 0.50 W/(m K)and latent heat of 139.64 J/***,according to the leakage testing and battery charge/discharge experiments,compared with Al_(2)O_(3)and ZnO,the addition of carbon nanotubes remarkably enhances the heat storage ability as latent heat from 126.98 J/g for the prepared sample with Al_(2)O_(3)and 125.86 J/g for the one with ZnO,then to 139.64 J/g,as well as dissipation performance as a cooling effect by decreasing the sur-face temperature of battery from 2%to 12%of microcapsule,composite sample with carbon nanotubes presents a broad application prospect in battery thermal management system and energy storage field.

关键词： Microcapsule Phase change material Battery Carbon nanotube Safety

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EQUILIBRIA AND STABILITY OF ONE MESSENGER RNA AND TWO MICRO RNA DYNAMICS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第4期84卷 3-8页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

In this paper we study an ODE model on the microRNA-mRNA dynamics. We prove the existence of two equilibrium points (one with strictly positive compo-nents) and obtain a biologically consistent, sufficient asymptotic stability condition for the strictly positive equilibrium. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Asymptotic stability

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Advancing osteoarthritis research:the role of AI in clinical,imaging and omics fields

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Bone Research 2025年第2期13卷 307-319页

作者： Jingfeng Ou Jin Zhang Momen Alswadeh Zhenglin Zhu Jijun Tang Hongxun Sang Ke Lu Shenzhen Hospital Southern Medical UniversityShenzhenChina Faculty of Computer Science and Control Engineering Shenzhen University of Advanced TechnologyShenzhenChina Department of Orthopaedic Surgery The First Affiliated Hospital of Chongqing Medical UniversityChongqingChina

Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal *** diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint *** limitations result in delayed diagnoses and inconsistent ***,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and *** advancements in artificial intelligence(AI)offer transformative potential to address these *** review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics *** consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA *** findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.

关键词： subjective clinical assessments analysis omics data degenerative joint disease osteoarthritis imaging techniques clinical assessment biological datasetsmaking diagnostic methodsincluding

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Social Radars: Finding Targets in Cyberspace for Cybersecurity

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 279-282页

作者： Lili Fan Changxian Zeng Yutong Wang Jiaqi Ma Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Dalian Minzu UniversityDalian 116600China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Civil and Environmental Engineering University of CaliforniaLos AngelesCA 90024 USA IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.

关键词： prompt summarized insight

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Model Identification and control of Electromagnetic Actuation in Continuous Casting Process With Improved Quality

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 203-215页

作者： Isabela Birs Cristina Muresan Dana Copot Clara Ionescu Research Group of Dynamical Systems and Control Faculty of Engineering and ArchitectureGhent University and with EEDT Decision&ControlFlanders Make ConsortiumTech Lane Science Park 125 and 131B-9052 GhentBelgium.I.Birs the Automation Department Faculty of Automation and Applied InformaticsTechnical University of Cluj-NapocaRomania IEEE

This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.

关键词： Electromagnetic actuator fractional order control fractional order system model non-Newtonian material

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Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat

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Neural Computing and Applications 2024年第31期36卷 19667-19690页

作者： Zheng, Yifan Xin, Bin He, Bin Ding, Yulong Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-to-end output by combining with deep neural network, multi-agent reinforcement learning (MARL) has made remarkable achievements in multi-UAV autonomous air combat maneuvering decision-making (MUAAMD). However, the implementation of effective cooperative policy learning remains a challenging issue for MARL methods with centralized training decentralized execution (CTDE) paradigm. This paper proposes a MARL-based method to improve the performance of cooperation in MUAAMD. Firstly, considering the constraints of dynamic and limited perception for UAVs in the realistic air combat scenario, the MUAAMD problem is formulated based on partially observable Markov game (POMG) model. Secondly, a novel efficient MARL algorithm named the mean policy-based proximal policy optimization (MP3O) is introduced. Specifically, a joint policy optimization mechanism is constructed by estimating the policies of neighboring agents in group as a mean-field approximation while training, which enables both centralized evaluation and improvement of cooperative policy under the CTDE paradigm. Thirdly, by combining with three improvement techniques, a cooperative decision-making framework for MUAAMD based on MP3O is proposed. Empirically, results of simulations and comparative experiments validate the effectiveness of proposed method in promoting cooperative policy learning in resolving MUAAMD problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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Computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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Computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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