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Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

Computers, Materials & Continua 2022年第6期71卷 4275-4293页

作者： Waleed Rafique Ayesha Khan Ahmad Almogren jehangir arshad Adnan Yousaf Mujtaba Hussain Jaffery Ateeq Ur Rehman Muhammad Shafiq Department of Electrical&Computer Engineering COMSATS University Islamabad Lahore CampusLahore54000Pakistan Syed Babar Ali School of Science and Engineering Lahore University of Management SciencesLahore54000Pakistan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Department of Electrical Engineering Superior UniversityLahore54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

关键词： Harmonics compensation soft computing technique hybrid fuzzy PSO based PI controller HSAPF power quality

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Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

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Computers, Materials & Continua 2023年第11期77卷 1795-1814页

作者： Arsal Javaid Areeb Abbas jehangir arshad Mohammad Khalid Imam Rahmani Sohaib Tahir Chauhdary Mujtaba Hussain Jaffery Abdulbasid S.Banga Department of Electrical and Computer Engineering COMSATS University IslamabadLahore CampusLahore54000Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah211Oman

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this work.The addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine discomfort.This novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on it.Additionally,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted trees.The proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in Simulink.The results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing works.The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

关键词： Posture detection FSR sensor machine learning real-time KNN

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Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

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Computers, Materials & Continua 2022年第4期71卷 1153-1169页

作者： jehangir arshad Ayesha Khan Mariam Aftab Mujtaba Hussain Ateeq Ur Rehman Shafiq Ahmad Adel M.Al-Shayea Muhammad Shafiq Department of Electrical&Computer Engineering COMSATS University IslamabadLahore Campus54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Controlling feedback control systems in continuous action spaces has always been a challenging problem.Nevertheless,reinforcement learning is mainly an area of artificial intelligence(AI)because it has been used in process control for more than a decade.However,the existing algorithms are unable to provide satisfactory results.Therefore,this research uses a reinforcement learning(RL)algorithm to manage the control system.We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient(DDPG).The actor-critic scenario using DDPG is implemented to build the RL agent.In addition,a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.The RL algorithms are robust and proficient in using trial and error to search for the best strategy.Our proposed algorithm is a deep deterministic policy gradient,in which a large amount of training data trains the agent.Once the system is trained,the agent can automatically adjust the control parameters.The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment.The performance of the proposed RL algorithm is compared with a proportional integral derivative(PID)controller and a linear quadratic regulator(LQR)controller.The simulation results of the proposed scheme are promising for the feedback control problems.

关键词： Feedback control systems reinforcement learning artificial intelligence

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A Quantum Spatial Graph Convolutional Network for Text Classification

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Computer Systems Science & Engineering 2021年第2期36卷 369-382页

作者： Syed Mustajar Ahmad Shah Hongwei Ge Sami Ahmed Haider Muhammad Irshad Sohail M.Noman jehangir arshad Asfandeyar Ahmad Talha Younas College of Computer Science and Technology Dalian University of TechnologyDalian116024China Department of Computing University of WorcesterUK Department of Information Engineering Chang’an UniversityXi’an710054China Department of Cell Biology and Genetics Shantou University Medical CollegeShantou515041China Electrical and Computer Engineering Department COMSATS University IslamabadLahore CampusPakistan School of Information&Communication Engineering Dalian University of Technology Dalian116024China Electrical and Computer Engineering Department COMSATS University IslamabadSahiwal CampusPakistan

The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbationsG of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.

关键词： Text classiﬁcation deep learning graph convolutional networks semi-supervised learning GPUs performance improvements

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