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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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An Efficient Impersonation Attack Detection Method in Fog Computing

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computers, Materials & Continua 2021年第7期68卷 267-281页

作者： Jialin Wan Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Ahsan Shah Sadaqat Ur Rehman Muhammad Hanif Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s *** the fog layer is located between cloud and end-users,it can provide more convenience and timely services to ***,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the *** attacker acts as an impersonator to impersonate other legitimate ***,in this work,we present a detection technique to secure the FC ***,we model a physical layer key generation based on wireless channel *** generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver *** compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and *** simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

关键词： Fog computing double Sarsa attack detection physical layer key security

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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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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Electrical architecture for ultrafast charging station

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Transportation Research Procedia 2023年 70卷 170-177页

作者： David Zambrano-Prada Alexandra Blanch-Fortuna José A. Barrado-Rodrigo Luis Vázquez-Seisdedos Oswaldo López-Santos Abdelali El Aroudi Luis Martinez-Salamero Universitat Rovira i Virgili Department of Electronics Electrical and Automatic Control Engineering (DEEEA) School of Electrical and Computer Engineering Avda. Països Catalans 26 Tarragona 43007 Spain

EVs will play an important role in transport, climate change and electrification. However, there is a consensus that one of the main obstacles to full market penetration of EVs is the so-called range anxiety (fear of not having enough charge in the vehicle). Electric vehicle charging infrastructures have two opposing approaches to their use. For urban routes, where distances travelled, and EV usage times are low, on-board alternative current (AC) chargers (Level 1 and 2) can meet the charging requirements in most situations. The solution is a gradual increase of the existing electrical infrastructure and charging points, together with the integration of new energy sources to cope with the new loads. On the other hand, on highway routes, EVs require large amount of energy in a short time in order to provide a continuous trip. Nowadays, electric charging stations provide fast charging points for vehicles by connecting external chargers that supply direct current (DC) directly to the battery bank (Level 3) and these can take up to 30 minutes. However, a time of more than 10 minutes is considered to be a very long time in contrast to the time required to fill the tank of internal combustion vehicles. In this context, ultrafast charging is defined with charging time to 10 minutes or less. This report proposes the electrical architecture of an ultrafast charging station for electric vehicles (EVs), which is based on a hybrid energy distribution system made up of an AC bus and two DC buses, each one of different voltage. The proposal is the result of a statistical analysis based on Monte Carlo simulations, which uses real traffic data and stochastic models to estimate the number of vehicles, the energy required and the time to charge each EV that would access the station. Once the architecture is established, the operating modes of the station are defined, where a storage system is contemplated to provide service reliability, and which is sized following load levelling and

关键词： Type your keywords here separated by semicolons

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Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems 10

Designing a Secure Smart Remote Patient Monitoring and Warni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Sparsification of Deep Neural Networks via Ternary Quantization

Sparsification of Deep Neural Networks via Ternary Quantizat...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Luca Dordoni Andrea Migliorati Giulia Fracastoro Sophie Fosson Tiziano Bianchi Enrico Magli Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

In recent years, the demand for compact deep neural networks (DNN s) has increased consistently, driven by the necessity to deploy them in environments with limited resources such as mobile or embedded devices. Our work aims to tackle this challenge by proposing a combination of two techniques: sparsification and t ernarization o f network parameters. We extend the plain binarization by introducing a sparsification interval centered around O. The network parameters falling in this interval are set to 0 and effectively removed from the net-work topology. Despite the increased complexity required by the ternarization scheme compared to a binary quantizer, we obtain remarkable sparsity rates that yield parameter distri-butions with significantly compressible sources with entropy lower than 1 bits/symbol.

关键词： Training Quantization (signal) Accuracy Network topology Conferences Artificial neural networks Machine learning Entropy Topology Complexity theory

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A New LMI-Based Non-Iterative Design Method for Static Output Feedback controllers for Linear Systems 25

A New LMI-Based Non-Iterative Design Method for Static Outpu...

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25th International Conference on Mechatronics Technology, ICMT 2022

作者： Oya, Hidetoshi Hayakawa, Satoshi Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Department of Electronics and Control Engineering Ehime Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (数字)9781665461955

ISBN: (纸本)9781665461955

This paper presents a new non-iterative LMI-based design method for static output feedback controllers for linear systems. The proposed design method is derived from the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems. In this paper, we show the new non-iterative LMI-based design procedure for static output feedback controllers for linear systems, and the effectiveness of the proposed controller design approach is shown through a simple numerical example. © 2022 IEEE.

关键词： Linear systems

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Finite-control-Set Model Predictive control with Reduced Computational Burden in Cascaded H-Bridge Permanent Magnet Synchronous Motor Drives for EV Applications

Finite-Control-Set Model Predictive Control with Reduced Com...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Filippo Gemma Jacopo Riccio Giulia Tresca Andrea Volpini Pericle Zanchetta Electrical Computer and Biomedical Engineering Dept University of Pavia Pavia IT Power Electronics Machines and Control Group University of Nottingham Nottingham UK

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This article introduces an enhanced Finite-control-Set Model Predictive control (FCS-MPC) for Permanent Magnet Synchronous Motor (PMSM) drives powered by Multi-level Cascaded H-Bridge (CHB) inverters. The work focuses on reducing the algorithm computational complexity for torque control operations, ensuring State of Charge (SOC) balancing and reduced switching losses. The innovation lies in the utilization of adaptive Look-up Tables (LUTs) and an ad hoc objective function. Simulation results are provided to confirm the effectiveness of the proposed control approach in achieving accurate drive control with reduced switching frequency and enhanced SOC management. Additionally, the computational overhead of the proposed approach was measured on the target control platform to validate the reduction in computing complexity.

关键词： Accuracy Switching frequency Simulation Torque control Switching loss control systems Prediction algorithms Permanent magnet motors Table lookup Predictive control

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ConQ: Binary Quantization of Neural Networks via Concave Regularization

ConQ: Binary Quantization of Neural Networks via Concave Reg...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Andrea Migliorati Giulia Fracastoro Sophie Fosson Tiziano Bianchi Enrico Magli Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The increasing demand for deep neural networks (DNNs) in resource-constrained systems propels the interest in heavily quantized architectures such as networks with binarized weights. However, despite huge progress in the field, the gap with full-precision performance is far from closed. Today's most effective methods for quantization are rooted in proximal gradient descent theory. In this work, we propose ConQ, a novel concave regularization approach to train effective DNNs with binarized weights. Motivated by theoretical investigation, we argue that the proposed concave regularizer, which allows the removal of the singularity point at 0, presents a more effective shape than previously considered models in terms of accuracy and convergence rate. We present a theoretical convergence analysis of ConQ, with specific i nsights o n b oth c onvex a nd n on-convex s ettings. An extensive experimental evaluation shows that ConQ outperforms the accuracy of competing regularization methods for networks with binarized weights.

关键词： Gradient methods Accuracy Quantization (signal) Limiting Shape Conferences Machine learning Artificial neural networks Propulsion Convergence

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