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Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

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Computers, Materials & Continua 2022年第12期73卷 4981-4994页

作者： Haykel Marouani Fahad Awjah Almehmadi rihem.farkh Habib Dhahri Department of Applied Mechanical Engineering College of Applied EngineeringMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia Department of Applied Electrical Engineering College of Applied EngineeringMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia Department of Information Science College of Applied Computer SciencesMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia

In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are *** the COE depends on site specification constants and initialized parameters of wind turbine,the focus was on the design optimization of rotor radius,hub height and rated *** on literature,the COE is converted to the Saudi Arabia ***,the constrained wind turbine optimization problem is ***,non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 *** results show that as altitude rises,the optimal rotor radius grows,but the optimal hub height and rated power drop,resulting in an increase in ***,the non-Gaussian method display a faster convergence compared to the classical particle swarm *** findings will be useful as a reference for wind turbine design at high ***,it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.

关键词： Wind turbine high altitude energy cost particle swarm optimization Levy distribution

Deep Learning Control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： rihem.farkh Saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud Mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

Stabilizing Sets of PI/PID Controllers for Unstable Second Order Delay System

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International Journal of Automation and computing 2014年第2期11卷 210-222页

作者： rihem.farkh Kaouther Laabidi Mekki Ksouri Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical EngineeringNational Engineering School of TunisTunis El Manar University

In this paper, the problem of stabilizing an unstable second order delay system using classical proportional-integralderivative(PID) controller is considered. An extension of the Hermite-Biehler theorem, which is applicable to quasi-polynomials, is used to seek the set of complete stabilizing proportional-integral/proportional-integral-derivative(PI/PID) parameters. The range of admissible proportional gains is determined in closed form. For each proportional gain, the stabilizing set in the space of the integral and derivative gains is shown to be a triangle.

关键词： Hermite-Biehler theorem stability PI/PID controllers second order unstable delay system stability region.

Optimal Robust Control for Unstable Delay System

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Computer Systems Science & Engineering 2021年第2期36卷 307-321页

作者： rihem.farkh Khaled A.Aljaloud Moufida Ksouri Faouzi Bouani King Saud University Riyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical EngineeringNational Engineering School of TunisTunis El Manar UniversityTunis1002Tunisia

Proportional-Integral-Derivative control system has been widely used in industrial *** uncertain and unstable systems,tuning controller parameters to satisfy the process requirements is very *** general,the whole system’s performance strongly depends on the controller’s efficiency and hence the tuning process plays a key role in the system’s *** paper presents a robust optimal Proportional-Integral-Derivative controller design methodology for the control of unstable delay system with parametric uncertainty using a combination of Kharitonov theorem and genetic algorithm optimization based *** this study,the Generalized Kharitonov Theorem(GKT)for quasi-polynomials is employed for the purpose of designing a robust controller that can simultaneously stabilize a given unstable second-order interval plant family with time *** a constructive procedure based on the Hermite-Biehler theorem,we obtain all the Proportional-Integral-Derivative gains that stabilize the uncertain and unstable second-order delay *** Algorithms(GAs)are utilized to optimize the three parameters of the PID controllers and the three parameters of the system which provide the best control that makes the system robust stable under ***,the method uses genetic algorithms to determine the optimum parameters by minimizing the integral of time-weighted absolute error ITAE,the Integral-Square-Error ISE,the integral of absolute error IAE and the integral of time-weighted Square-Error *** validity and relatively effortless application of presented theoretical concepts are demonstrated through a computation and simulation example.

关键词： Unstable time-delay system interval plants generalized Kharitonov theorem PID controller Hermite-Biehler theorem stability region genetic algorithms optimum PID controller optimum system parameters

Computer Vision-Control-Based CNN-PID for Mobile Robot

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Computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： rihem.farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

Intelligent Autonomous-Robot Control for Medical Applications

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Computers, Materials & Continua 2021年第8期68卷 2189-2203页

作者： rihem.farkh Haykel Marouani Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a *** has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected *** COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a *** artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic *** officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent *** propose an advanced controller for a service robot to be used in *** type of robot is deployed to deliver food and dispense medications to individual *** autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its *** criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

关键词： Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system

An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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Computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab rihem.farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

《可持续发展设计指南》选载(之九) 如何为建筑设计高能效多功能外观

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建设科技 2007年第6期 80-84页

作者： Serge SALAT Salem farkh Serge SALAT Salem farkh

一、建立多功能外围护之思路所谓外围护是指将建筑物围合其中的外墙或面层。在生物气候建筑中．建筑围护结构是十分重要的，功能上有建筑内外之间形成一个封闭的过滤器的作用。

关键词：建筑设计多功能外观能效建筑围护结构生物气候建筑外围护建筑物

Performance and calibration of quark/gluon-jet taggers using 140 fb^(-1) of pp collisions at √s=13 TeV with the ATLAS detector

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Chinese Physics C 2024年第2期48卷 1-25页

作者： G. Aad B. Abbott K. Abeling N.J. Abicht S.H. Abidi A. Aboulhorma H. Abramowicz H. Abreu Y. Abulaiti B.S. Acharya C. Adam Bourdarios L. Adamczyk S.V. Addepalli M.J. Addison J. Adelman A. Adiguzel T. Adye A.A. Affolder Y. Afik M.N. Agaras J. Agarwala A. Aggarwal C. Agheorghiesei A. Ahmad F. Ahmadov W.S. Ahmed S. Ahuja X. Ai G. Aielli A. Aikot M. Ait Tamlihat B. Aitbenchikh I. Aizenberg M. Akbiyik T.P.A. Åkesson A.V. Akimov D. Akiyama N.N. Akolkar K. Al Khoury G.L. Alberghi J. Albert P. Albicocco G.L. Albouy S. Alderweireldt M. Aleksa I.N. Aleksandrov C. Alexa T. Alexopoulos F. Alfonsi M. Algren M. Alhroob B. Ali H.M.J. Ali S. Ali S.W. Alibocus M. Aliev G. Alimonti W. Alkakhi C. Allaire B.M.M. Allbrooke J.F. Allen C.A. Allendes Flores P.P. Allport A. Aloisio F. Alonso C. Alpigiani M. Alvarez Estevez A. Alvarez Fernandez M. Alves Cardoso M.G. Alviggi M. Aly Y. Amaral Coutinho A. Ambler C. Amelung M. Amerl C.G. Ames D. Amidei S.P. Amor Dos Santos K.R. Amos V. Ananiev C. Anastopoulos T. Andeen J.K. Anders S.Y. Andrean A. Andreazza S. Angelidakis A. Angerami A.V. Anisenkov A. Annovi C. Antel M.T. Anthony E. Antipov M. Antonelli F. Anulli M. Aoki T. Aoki J.A. Aparisi Pozo M.A. Aparo L. Aperio Bella C. Appelt A. Apyan N. Aranzabal S.J. Arbiol Val C. Arcangeletti A.T.H. Arce E. Arena J-F. Arguin S. Argyropoulos J.-H. Arling O. Arnaez H. Arnold G. Artoni H. Asada K. Asai S. Asai N.A. Asbah J. Assahsah K. Assamagan R. Astalos S. Atashi R.J. Atkin M. Atkinson H. Atmani P.A. Atmasiddha K. Augsten S. Auricchio A.D. Auriol V.A. Austrup G. Avolio K. Axiotis G. Azuelos D. Babal H. Bachacou K. Bachas A. Bachiu F. Backman A. Badea P. Bagnaia M. Bahmani A.J. Bailey V.R. Bailey J.T. Baines L. Baines O.K. Baker E. Bakos D. Bakshi Gupta V. Balakrishnan R. Balasubramanian E.M. Baldin P. Balek E. Ballabene F. Balli L.M. Baltes W.K. Balunas J. Balz E. Banas M. Bandieramonte A. Bandyopadhyay S. Bansal L. Barak M. Barakat E.L. Barberio D. Barberis M. Barbero M.Z. Barel K.N. Barends T. Barillari M-S. Barisits T. Barklow P. Baron D.A. Baron Mo The ATLAS Collaboration

The identification of jets originating from quarks and gluons,often referred to as quark/gluon tagging,plays an important role in various analyses performed at the Large Hadron Collider,as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced *** paper describes the measurement of the efficiencies of quark/gluon taggers developed within the ATLAS Collaboration,using √s=13 TeV proton–proton collision data with an integrated luminosity of 140 fb^(-1) collected by the ATLAS *** taggers with high performances in rejecting jets from gluon over jets from quarks are studied:one tagger is based on requirements on the number of inner-detector tracks associated with the jet,and the other combines several jet substructure observables using a boosted decision tree.A method is established to determine the quark/gluon fraction in data,by using quark/gluon-enriched subsamples defined by the jet *** in tagging efficiency between data and simulation are provided for jets with transverse momentum between 500 GeV and 2 TeV and for multiple tagger working points.

关键词： ATLAS JET QUARK GLUON TAGGING