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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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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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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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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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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach

Control of Isolated AC Microgrids with Constant Power Loads:...

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Mediterranean Conference on control and Automation (MED)

作者： Grigoris Michos George C. Konstantopoulos Paul A. Trodden Visakan Kadirkamanathan Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street Sheffield UK Department of Electrical and Computer Engineering University of Patras Rion Greece

This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System

Prediction of Radiated Electromagnetic Interference Based on...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yixin Shi Wenjie Chen Fengjiao Li Jinpeng Kan Yongqing Meng Guang Yang 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)9798350377798

ISBN: (纸本)9798350377804

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems.

关键词： Motor drives Accuracy Silicon carbide Electromagnetic interference Layout Predictive models Motors Topology Gallium nitride Cables

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