Machine Learning in Industrial IoT Applications for Safety, Security, Asset Localization, Quality Assurance, and Sustainability in Smart Production

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 49 - 66

Published: June 30, 2024

This study explores the integration of machine learning techniques, notably Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), with industrial production processes for quality assurance. The emphasis is on examining performance SVM CNN through a rigorous assessment precision, recall, F1 score in Performance Metrics Evaluation. Additionally, tests algorithms against existing baseline approaches, evaluating their accuracy efficiency fault identification. results reveal consistent strong CNN, highlighting revolutionary potential revolutionizing control systems. findings provide essential insights into properties each algorithm, demonstrating ability to outperform methods contribute more versatile efficient approach assurance settings.

Language: Английский

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An Advanced Hybrid Algorithm (haDEPSO) for Engineering Design Optimization Integrating Novel Strategies for Enhanced Performance

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 189 - 208

Published: June 28, 2024

This research presents haDEPSO, a pioneering hybrid technique for engineering design optimization. Combining the strengths of Differential Evolution (DE) and Particle Swarm Optimization (PSO), haDEPSO offers versatile answer to difficulties contemporary optimization settings. The methodology combines precise integration DE's robust exploration capabilities with PSO's efficient exploitation tactics, ensuring adaptability across diverse problem environments. Through 10 trials, performance measures such as fitness function value, convergence speed, diversity meter reveal haDEPSO's consistent power. Scalability testing reveals algorithm's effectiveness in addressing situations varying sizes, yet challenges occur particularly massive instances. These findings contribute deep knowledge restrictions, driving subsequent advancements better applicability

Language: Английский

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A Comparative Analysis of Meta-Heuristic Algorithms for Optimal Configuration of Hybrid Renewable Energy Systems for Remote Villages

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 143 - 167

Published: June 28, 2024

In the search for sustainable and reliable energy solutions, deployment of hybrid renewable systems (HRES) has developed as a promising approach mainly powering remote villages that lack access to centralized grids. The optimal configuration these leads complex optimization problem through demanding application meta-heuristic algorithms efficiently direct massive solution space recognize most cost-effective setup. Numerous have been engaged this purpose. Through comparative analysis various algorithms, particle swarm helps in obtaining improved solutions. Particle (PSO) occurs powerful effective technique addressing task determining configurations positioned villages.

Language: Английский

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A Novel Approach for Optimizing Wire Electric Discharge Machining of Mg-Cu-RE-Zr Alloy Using Machine Learning Algorithm

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 43 - 64

Published: June 28, 2024

This study focuses on the optimisation of wire electric discharge machining (WEDM) process for WE43 alloy using machine learning methods. The alloy, made magnesium (Mg), copper (Cu), rare earth (RE) elements, and zirconium (Zr), is extensively employed in aerospace automotive sectors its lightweight high-strength features. research applies three models—artificial neural networks (ANN), random forest (RF), decision trees (DT)—to optimize important parameters, including current (A), pulse (P On), off Off). A full experimental design based Taguchi L27 array undertaken, methodically altering each parameter at levels. Material removal rate (MRR) chosen as response variable optimisation. parameters are adjusted by use techniques, with ANN emerging most accurate predictor, obtaining an accuracy 96.7%.

Language: Английский

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Enhancing Operational Cost Savings in Electric Utilities on Global Optimization in Power System Planning and Operation

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 302 - 329

Published: June 28, 2024

Operational cost savings in electric utilities using the application of genetic algorithms power system planning and operation characterize an innovative approach that involves computational intelligence to optimize complex decision-making processes grid functioning. Electric involve various challenges which managing generation, transmission distribution are necessary meet ever-growing demand for electricity with reduction operational costs. These overcome aid a algorithm. In field planning, engaged configuration expansion distribution.

Language: Английский

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