Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis
AH Elsheikh - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract Machine learning (ML) methods have received immense attention as potential
models for modeling different manufacturing systems. This paper presents a comprehensive …
models for modeling different manufacturing systems. This paper presents a comprehensive …
Hybridization of hybrid structures for time series forecasting: A review
Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and developing a forecasting framework with a high degree of accuracy is one of the most …
and developing a forecasting framework with a high degree of accuracy is one of the most …
Ventilation diagnosis of angle grinder using thermal imaging
A Glowacz - Sensors, 2021 - mdpi.com
The paper presents an analysis and classification method to evaluate the working condition
of angle grinders by means of infrared (IR) thermography and IR image processing. An …
of angle grinders by means of infrared (IR) thermography and IR image processing. An …
Effective machine learning model combination based on selective ensemble strategy for time series forecasting
The success of ensemble forecasting heavily depends on the selection and combination of
component models as proven by numerous studies that show the superior performance of …
component models as proven by numerous studies that show the superior performance of …
A comprehensive review on deep learning approaches for short-term load forecasting
Y Eren, İ Küçükdemiral - Renewable and Sustainable Energy Reviews, 2024 - Elsevier
The balance between supplied and demanded power is a crucial issue in the economic
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
Cooperative ensemble learning model improves electric short-term load forecasting
Efficient models for short-term load forecasting (STLF) plays a crucial role in establishing the
companies' energetic planning due to their importance in electric power distribution and …
companies' energetic planning due to their importance in electric power distribution and …
Short-term electrical load forecasting through heuristic configuration of regularized deep neural network
An accurate electrical load forecasting is essential for optimal grid operation. The paper
presents a methodology for the short-term commercial building electrical load forecasting …
presents a methodology for the short-term commercial building electrical load forecasting …
Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision
Contamination on insulators may increase the surface conductivity of the insulator, and as a
consequence, electrical discharges occur more frequently, which can lead to interruptions in …
consequence, electrical discharges occur more frequently, which can lead to interruptions in …
Comparing generative adversarial networks architectures for electricity demand forecasting
This paper introduces short-term load forecasting (STLF) using Generative Adversarial
Networks (GAN). STLF was explored using several Artificial Intelligence based methods that …
Networks (GAN). STLF was explored using several Artificial Intelligence based methods that …
Review of cyberattack implementation, detection, and mitigation methods in cyber-physical systems
With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …