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Optimized coordinated control of LFC and SMES to enhance frequency stability of a real multi-source power system considering high renewable energy penetration

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Protection and Control of Modern Power Systems 2018年第1期3卷 416-430页

作者： gaber Magdy g.shabib Adel A.Elbaset Yasunori Mitani Department of Electrical Engineering Kyushu Institute of TechnologyTobata-KuKitakyushu-shiFukuoka 804-8550Japan Department of Electrical Engineering Faculty of Energy EngineeringAswan UniversityAswan 81528Egypt Higher Institute of Engineering and technology King MarriottAlexandria 23713Egypt Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia 61517Egypt

With rapidly growing of Renewable Energy Sources(RESs)in renewable power systems,several disturbances influence on the power systems such as;lack of system inertia that results from replacing the synchronous generators with RESs and frequency/voltage fluctuations that resulting from the intermittent nature of the ***,the modern power systems become more susceptible to the system instability than conventional power ***,in this study,a new application of Superconducting Magnetic Energy Storage(SMES)(i.e.,auxiliary Load Frequency Control(LFC))has been integrated with the secondary frequency control(i.e.,LFC)for frequency stability enhancement of the Egyptian Power System(EPS)due to high RESs ***,the coordinated control strategy is based on the PI controller that is optimally designed by the Particle Swarm Optimization(PSO)algorithm to minimize the frequency deviations of the *** EPS includes both conventional generation units(i.e.,non-reheat,reheat and hydraulic power plants)with inherent nonlinearities,and RESs(i.e.,wind and solar energy).System modelling and simulation results are carried out using Matlab/Simulink^(■)*** simulation results reveal the robustness of the proposed coordinated control strategy to preserve the system stability of the EPS with high penetration of RESs for different contingencies.

关键词： Renewable energy sources(RESs) Load frequency control(LFC) Egyptian power system(EPS) Superconducting magnetic energy storage(SMES)

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Microgrid dynamic security considering high penetration of renewable energy

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Protection and Control of Modern Power Systems 2018年第1期3卷 245-255页

作者： g.Magdy Emad A.Mohamed g.shabib Adel A.Elbaset Yasunori Mitani1 Department of Electrical Engineering Kyushu Institute of TechnologyTobata-KuKitakyushu-shiFukuoka 804-8550Japan Department of Electrical Engineering Faculty of Energy EngineeringAswan UniversityAswan 81528Egypt Higher Institute of Engineering and Technology King MarriottAlexandria 23713Egypt Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia 61517Egypt

This paper presents a coordination strategy of Load Frequency Control(LFC)and digital frequency protection for an islanded microgrid(Mg)considering high penetration of Renewable Energy Sources(RESs).In such Mgs,the reduction in system inertia due to integration of large amount of RESs causes undesirable influence on Mg frequency stability,leading to weakening of the ***,sudden load events,and short circuits caused large frequency fluctuations,which threaten the system security and could lead to complete blackouts as well as damages to the system ***,maintaining the dynamic security in Mgs is one of the important challenges,which considered in this paper using a specific design and various data conversion stages of a digital over/under frequency relay(OUFR).The proposed relay will cover both under and over frequency conditions in coordination with LFC operation to protect the Mg against high frequency *** prove the response of the proposed coordination strategy,a small Mg was investigated for the *** proposed coordination method has been tested considering load change,high integration of ***,the sensitivity analysis of the presented technique was examined by varying the penetration level of RESs and reducing the system *** results reveal the effectiveness of the proposed coordination to maintain the power system frequency stability and *** addition,the superiority of the OUFR has been approved in terms of accuracy and speed response during high disturbances.

关键词： Digital frequency relay Load frequency control(LFC) Microgrid Renewable energy sources(RESs) Dynamic

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Diabetic Retinopathy Severity Classification Using Data Fusion and Ensemble Transfer Learning

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Journal of Software Engineering and Applications 2025年第1期18卷 1-23页

作者： shabib Aftab Samia Akhtar Department of Computer Science Virtual University of Pakistan Lahore Pakistan

Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.

关键词： Diabetic Retinopathy Fundus Images CLAHE Augmentation Optimizer

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Corrosion resistance and thermal stability of sputtered Fe44Al34Ti7N15 and Al61Ti11N28 thin films for prospective application in oil and gas industry

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Progress in Natural Science:Materials International 2021年第5期31卷 688-697页

作者： Mahbub Alam Maruf Syed Muhammad Mujtaba Rizvi Mohammed Noor-A-Alam Donghyun Shin Waseem Haider Ishraq shabib School of Engineering and Technology Central Michigan University Mechanical Engineering Indiana University-Purdue University Columbus

Fe-and Al-based thin-film metallic glass coatings(Fe44Al34Ti7N15and Al61Ti11N28) were fabricated using magnetron co-sputtering technique, and their corrosion performances compared against wrought 316 L stainless steel. The results of gI-XRD and XPS analyses demonstrated amorphous structure and oxide layer formation on the surface of the fabricated thin films, respectively. The potentiodynamic（PD） polarization test in chloridethiosulfate（NH4Cl + Na2S2O3） solution revealed lower corrosion current(Icorr)（0.42 ± 0.02 μA/cm2and0.086 ± 0.001 μA/cm2Vs. 0.76 ± 0.05 μA/cm2）, lower passivation current(Ipass)（1.45 ± 0.03 μA/cm2and1.83 ± 0.07 μA/cm2Vs. 1.98 ± 0.04 μA/cm2）, and approximately six-fold higher breakdown potential（Ebd） for Fe-and Al-based coatings than those of wrought 316 L stainless steel. Electrochemical Impedance Spectroscopy（EIS） of both films showed 4-and 2-fold higher charge transfer resistance（Rct）, 7-and 2.5-times higher film resistance（Rf）, lower film capacitance values（Qf）(10 ± 2.4 μS-sacm-2, and 5.41 ± 0.8 μS-sacm-2Vs. 18 ± 2.21 μSsacm-2), and lower double-layer capacitance values（Qdl）(31.33 ± 4.74 μS-sacm-2, and 15.3 ± 0.48 μS-sacm-2Vs.43 ± 4.23 μS-sacm-2), indicating higher corrosion resistance of the thin films. Cyclic Voltammetry（CV） scan exhibited that the passive films formed on the Fe-and Al-based coatings were more stable and less prone to pitting corrosion than the wrought 316 L stainless steel. The surface morphology of both films via SEM endorsed the CV scan results, showing better resistance to pitting corrosion. Furthermore, the thermal analysis via TgA and DSC revealed the excellent thermal stability of the thin films over a wide temperature range typically observed in oilgas industries.

关键词： Thin films Metallic glasses X-ray diffraction Photoelectron spectroscopy Corrosion Thermal analysis

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Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

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Computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas shabib Aftab Muhammad Adnan Khan Taher Mghazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare ***,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction

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Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

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Computers, Materials & Continua 2021年第11期69卷 2717-2731页

作者： Munir Ahmad Majed Alfayad shabib Aftab Muhammad Adnan Khan Areej Fatima Bilal Shoaib Mohammad Sh.Daoud Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past *** and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of *** technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular *** computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample *** is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and ***,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML *** recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve ***-time patient data were extracted using the fuzzy-based model stored in the cloud.

关键词： Machine learning fusion cardiovascular disease data fusion fuzzy system disease prediction

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Joint Channel and Multi-User Detection Empowered with Machine Learning

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Computers, Materials & Continua 2022年第1期70卷 109-121页

作者： Mohammad Sh.Daoud Areej Fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid shabib Aftab College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversitySeongnam13557South Korea Department of Computer Science College of Science and TechnologyWenzhou Kean University325060USA Department of Computer Science Hameeda Rasheed Institute of Science and TechnologyMultan66000Pakistan

The numbers of multimedia applications and their users increase with each passing *** multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network *** this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two *** first stage estimates the channel parameters,and the second performsmulti-user *** proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural *** study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO *** FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

关键词： Channel and multi-user detection minimum mean square error multiple-input and multiple-output minimum mean channel error bit error rate

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A New Coordinated Fuzzy-PID Controller for Power System Considering Electric Vehicles

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Energy and Power Engineering 2017年第4期9卷 425-435页

作者： g. Magdy g. shabib Adel A. Elbaset Thongchart Kerdphol Yaser Qudaih Yasunori Mitani Department of Electrical and Electronics Engineering Kyushu Institute of Technology Tobata-Ku Kitakyushu-Shi Fukuoka Japan Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia Egypt

With penetration growing of renewable energy sources which integrated into power system have caused problems on grid stability. Electric Vehicles (EV) are one of the renewable energy sources that can bring significant impacts to power system during their charging and discharging operations. This article established a model of single machine infinite bus (SMIB) power system considering EV as a case study of load disturbance for power system oscillation. The objective of this research is to enhance stability and overcome the drawbacks of traditional control algorithms such as power system stabilizer (PSS), PID controller and fuzzy logic controller (FLC). The implementation’s effect of FLC parallel with PID controller (Fuzzy-PID) has been shown in this paper. The speed deviation (?ω) and electrical power (Pe) are the important factors to be taken into consideration without EV (only change in mechanical torque), EV with change in the mechanical torque and sudden plug-in EV. The obtained result by nonlinear simulation using Matlab/Simulink of a SMIB power system with EV has shown the effectiveness of using (Fuzzy-PID) against all disturbances.

关键词： Power Systems Electric Vehicles (EV) Power System Stabilizer (PSS) Fuzzy Logic Controller (FLC) PID Controller (Fuzzy-PID)

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eXRUP: A Hybrid Software Development Model for Small to Medium Scale Projects

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Journal of Software Engineering and Applications 2013年第9期6卷 446-457页

作者： ghulam Rasool shabib Aftab Shafiq Hussain Detlef Streitferdt Department of Computer Science COMSATS Institute of Information Technology Lahore Pakistan Department of Engineering Computing and Technology University of Sunderland Sunderland UK Software Architectures and Product Lines group The Ilmenau University of Technology Ilmenau Germany

The conventional and agile software development process models are proposed and used nowadays in software industry to meet emergent requirements of the customers. Conventional software development models such as Waterfall, V model and RUP have been predominant in industry until mid 1990s, but these models are mainly focused on extensive planning, heavy documentation and team expertise which suit only to medium and large scale projects. The Rational Unified Process is one of the widely used conventional models. Agile process models got attention of the software industry in last decade due to limitations of conventional models such as slow adaptation to rapidly changing business requirements and they overcome problems of schedule and cost. Extreme Programming is one of the most useful agile methods that provide best engineering practices for a good quality product at small scale. XP follows the iterative and incremental approach, but its key focus is on programming, and reusability becomes arduous. In this paper, we present characteristics, strengths, and weaknesses of RUP and XP process models, and propose a new hybrid software development model eXRUP (eXtreme Programming and Rational Unified Process), which integrates the strengths of RUP and XP while suppressing their weaknesses. The proposed process model is validated through a controlled case study.

关键词： Process Models Agile Modeling eXtreme Programming Rational Unified Process Process Evaluation

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