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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

computer systems Science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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A Novel Zero Common-Mode Voltage Modulation Strategy for Dual Three-Phase PWM Inverter Based on Unified Voltage Modulation

A Novel Zero Common-Mode Voltage Modulation Strategy for Dua...

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Energy, Power and Electrical engineering (EPEE), International Conference on

作者： Jinpeng Kan Yongqing Meng Changyi Li Wenjie Chen Fengjiao Li Yixin Shi School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation.

关键词： Pulse width modulation inverters Performance evaluation Simulation Modulation Voltage Switches Permanent magnet motors Inverters Permanent magnets Switching circuits

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Design of a Multi-Storey Model of an Automated Garage House Based on ATmega2560 Microcontroller 5

Design of a Multi-Storey Model of an Automated Garage House ...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Jacko, Patrik Kalinov, Andrii Burdilna, Evgeniia Babjak, Jaroslav Technical University of Košice Department of Theoretical and Industrial Electrical Ingeneering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This article describes in detail the implementation of an automated parking house model based on ATmega2560 microcontroller that enables simple and efficient parking of cars in precisely determined positions. Described model offers to user convenient parking of the car model. In addition to the automated parking system, the system provides several additional functions realized by displacement sensors and smoke sensors. One of the goals of this paper is to eliminate potentially dangerous situations and collisions during parking and thereby increase safety in this area. The garage house model is not designed solely for parking purposes but also as a practical teaching tool in the field of sensors and actuators. It is precisely in this area that students can expand their skills in designing and implementing programs that can enable all the functions of the garage house - temperature and smoke detection, precise positioning of the platform through stepper motors, display outputs, and more. At the same time, they will learn teamwork, where the functionality of the garage house is divided into multiple parts, and individual teams of students must collaborate to successfully solve and create a fully functional garage house software. © 2023 IEEE.

关键词： Microcontrollers

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh 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 Department of System Programming South Ural State UniversityChelyabinsk454080Russia 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 Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Unveiling drug-induced side effects through network-based analysis: An update

Unveiling drug-induced side effects through network-based an...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Funari, Alessio Conte, Federica Paci, Paola Sapienza University of Rome Institute for Systems Analysis and Computer Science 'Antonio Ruberti' National Research Council Department of Translational and Precision Medicine Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome17177 Italy

ISBN: (纸本)9798350386226

In this article, we propose an updated version of our previously developed model for predicting drug-side effect associations, applied to two case studies: long QT syndrome and asthma. The classifier accepts the name of a specific drug side effect as input and outputs a list of drugs potentially associated with this side effect. By simulating how drug effects propagate within the interactome using the Random Walk with Restart algorithm, the classifier identifies genes potentially associated with the onset of the side effect. Based on the rationale that the more a drug perturbs these genes, the more likely it is to cause the side effect, the model identifies drugs potentially linked to the onset of the side effect. Moreover, the model enables the categorization of drugs into chemical subclasses using the ClassyFire schema, facilitating the analysis of complex side effects, such as asthma, through more specific mechanisms. The results show that the model identifies both drugs known to be associated with certain side effects, as well as drugs not officially reported by the FDA, demonstrating its generalizability and practical relevance. This method is also adaptable for analyzing other side effects. © 2024 IEEE.

关键词： drug side effect network medicine random walk

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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Virtual power circle based PQ decoupling technique for a virtual synchronous generator connected to a weak grid

Virtual power circle based PQ decoupling technique for a vir...

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Power Tech Conference

作者： Daniel Angelo Kisinga Paul Trodden Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

High penetration of renewable energy (RE) generations in power systems results into a low inertia-weak power grid. To increase inertia of the latter systems, the RE to grid interfacing inverters can be operated to mimic synchronous generators. This technology is known as virtual synchronous generator (VSG). However, as the grid weakens there is severe coupling between active power (P) and reactive power (Q). Hence, a VSG requires a PQ decoupling technique for its successful operation under this case (connection to the weak power grid). Therefore, this paper proposes a virtual power circle with variable center and radius for independent control of both P and Q. The method is implemented using virtual impedance. The efficacy of the proposed scheme to decouple PQ is validated using a synchronverter connected to the weak grid in MATLAB/Simulink environment.

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Design of a Mechatronic Endoskeleton Robot for Inspection Tasks: Model Reference Adaptive control (MRAC) for Serpentine Locomotion with Stability Analysis 17

Design of a Mechatronic Endoskeleton Robot for Inspection Ta...

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17th International Conference on Developments in esystems engineering, DeSE 2024

作者： Al Mhdawi, Ammar K. Wright, Nick Younis, Mohammed I. Humaidi, Amjad J. School of Engineering and Sustainable Development De Montfort University LeicesterLE19BH United Kingdom Department of Electrical and Electronic Engineering Newcastle University Newcastle United Kingdom Department of Computer Engineering University of Baghdad Iraq Department of Control and Systems Engineering University of Technology Baghdad Iraq

ISBN: (纸本)9798350368697

An advantage of bio-inspired robots is the versatility of their locomotion on a wide range of terrains that conventional robots are not able to traverse. The snake-like robot, which is a mechanism designed to move in the manner of a biological snake, is an example of a bio-inspired platform. This paper aims to implement and validate, using real and simulation tests on flat terrain, a snake-like endoskeleton robot that permits serpentine movement with adaptive control capabilities. The prototype mechatronic robot was comprised of eleven segments that were 3D printed sequentially. A simulation using Matlab’s Multibody Mechanics tool determines the necessary torques for each motor. In this simulation, a parameterized virtual model of the robot is created, where rectilinear gaits are programmed. In accordance with simulation and experimental results, the robot undertakes different times to reach the goal trajectory or end points depending on the frequency, angular position, and wave length (duty cycle). As a result, there is a high degree of similarity between the simulation tests and those conducted with the prototype endoskeleton. Furthermore, the robot is equipped with machine vision capabilities that allow it to detect faults within oil and gas pipes for the purpose of inspection and maintenance. Furthermore, a Model Reference Adaptive control (MRAC) method based on Lyapunov stability analysis and MIT rules is proposed. The theoretical analysis and numerical simulation show that the designed trajectory tracking control law can make the multi-joint snake-like robot track the trajectory of the front joint when the robot encounters disturbances and stabilize the angular position and velocity of the first two head joints when disturbances occur. © 2024 IEEE.

关键词： Model reference adaptive control

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt 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 Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques

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Digital Communications and Networks 2025年第3期11卷 622-633页

作者： Vermander, Patrick Mancisidor, Aitziber Gravina, Raffaele Cabanes, Itziar Fortino, Giancarlo Department of Automatic Control and System Engineering University of the Basque Country (UPV/EHU) Bilbao 48013 Spain Department of Informatics Modeling Electronics and Systems Engineering University of Calabria (UNICAL) Rende 87036 Italy

Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system's learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects. © 2024 Chongqing University of Posts and Telecommunications

关键词： Anomaly detection Assistive technology Individualization Pressure sensors Sitting posture monitoring Unsupervised techniques Wheelchair

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