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View article: Curriculum-Enhanced Adaptive Sampling for Physics-Informed Neural Networks: A Robust Framework for Stiff PDEs
Curriculum-Enhanced Adaptive Sampling for Physics-Informed Neural Networks: A Robust Framework for Stiff PDEs Open
Physics-Informed Neural Networks (PINNs) often struggle with stiff partial differential equations (PDEs) exhibiting sharp gradients and extreme nonlinearities. We propose a Curriculum-Enhanced (CE) Adaptive Sampling framework that integrat…
View article: Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems
Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems Open
The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algo…
View article: Early Childhood Mathematics Curriculum in the light of the standards of the National Council of Mathematics Teachers
Early Childhood Mathematics Curriculum in the light of the standards of the National Council of Mathematics Teachers Open
This study aims at exploring the preschool mathematics curriculum in light of the National Council of Teachers of Mathematics Standards. The study researched a sample of (140) preschool educators. An analysis of the mathematical content of…
View article: Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm
Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm Open
This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SC…
View article: Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems
Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems Open
In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: fir…
View article: Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM
Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM Open
In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation tim…
View article: Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems Open
Ensuring the validity of measurements in wind energy systems (WES) is a challenging task in system diagnosis and data validation. This work, therefore, elaborates on the development of new approaches aimed at improving the operation of WES…
View article: Quantified Database for Methane Dehydroaromatization Reaction
Quantified Database for Methane Dehydroaromatization Reaction Open
Direct conversion of methane to aromatics, known as methane dehydroaromatization (MDA) is a very promising one‐step conversion route that could significantly reduce CO 2 emissions and simplify the process. There is an increasing interest i…
View article: An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization
An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization Open
The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particl…
View article: Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems
Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems Open
Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem …
View article: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM Open
The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirec…
View article: Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation
Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation Open
Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA…
View article: Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems
Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems Open
Wind energy (WE) is one of the most important technology to produce energy and an efficient source of renewable energy (RE) available in the atmospheric environment due to different air-currents spread over the stratosphere and troposphere…
View article: Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest
Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest Open
This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-b…
View article: Table of Contents
Table of Contents Open
Optimization of Three-Phase Feeder Load Balancing Using Smart Meters Optimisation de l'équilibrage de la charge d'une alimentation triphasée à l'aide de compteurs intelligents L. Alhmoud and W. Marji 18 Investigation on Connected System of…
View article: Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems
Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems Open
In this paper, special attention is paid to the detection and diagnosis of various incipient faults of uncertain wind energy conversion (WEC) systems. The proposals will enhance the monitoring and diagnosis of the WEC system while taking i…
View article: Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems
Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems Open
One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learni…
View article: Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems
Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems Open
Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regress…
View article: STUDY ON PINHOLE LEAKS IN GAS PIPELINES: CFD SIMULATION AND ITS VALIDATION
STUDY ON PINHOLE LEAKS IN GAS PIPELINES: CFD SIMULATION AND ITS VALIDATION Open
In the present study, the computational fluid dynamics (CFD) simulations of pinhole leaks (1.27-3.3mm) in a low-pressure, up to 2.5 bars, air pipeline which has 16 mm (0.62 inch) inner diameter has been performed by using a 3D transient DE…
View article: Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems
Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems Open
This paper proposes a novel Reduced Gaussian Process Regression (RGPR)‐based Random Forest (RF) technique (RGPR‐RF) for fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems. First, two RGPR models are proposed to dea…
View article: A Novel Leak Detection Approach in Water Distribution Networks
A Novel Leak Detection Approach in Water Distribution Networks Open
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, cr…
View article: Online statistical hypothesis test for leak detection in water distribution networks
Online statistical hypothesis test for leak detection in water distribution networks Open
This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, …
View article: A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation Open
This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valu…
View article: Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects
Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects Open
Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converte…