A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach
Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of
the most important of them. Sometimes in industrial environments, it is difficult and even …
the most important of them. Sometimes in industrial environments, it is difficult and even …
Combustion condition predictions for C2-C4 alkane and alkene fuels via machine learning methods
The accurate and rapid prediction of hydrocarbon type was a precondition for the utilization
of fossil fuels with high efficiency and safety. In this study, machine learning based …
of fossil fuels with high efficiency and safety. In this study, machine learning based …
Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management
In this research, an exergetic mathematical model is proposed for closed-loop food supply
chain network design considering economic, environmental, and social aspects. The …
chain network design considering economic, environmental, and social aspects. The …
Prediction of normalized shear modulus and damping ratio for granular soils over a wide strain range using deep neural network modelling
Dynamic properties, such as shear modulus and damping ratio, are critical for civil
engineering applications and essential for accurate dynamic response analysis. This study …
engineering applications and essential for accurate dynamic response analysis. This study …
A hybrid wavelet-deep learning approach for vibration-based damage detection in monopile offshore structures considering soil interaction
Structural health monitoring (SHM) is crucial in the early stage of damage formation for the
life-cycle service of offshore structures. The influence of soils on vibration-based damage …
life-cycle service of offshore structures. The influence of soils on vibration-based damage …
Exploring neural motion transfer for unsupervised remote physiological measurement: A practicality study
T Liu, H Xiao, Y Sun, A Zhao, K Zuo, H Wen, H Li… - Digital Signal …, 2024 - Elsevier
Remote Photoplethysmography (rPPG) is a method to measure cardiac activity without the
need for any contact-based sensors, garnering attention due to its non-invasive and …
need for any contact-based sensors, garnering attention due to its non-invasive and …
[PDF][PDF] GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images
S Aggarwal, I Gupta, A Kumar, S Kautish… - Mathematical …, 2024 - aimspress.com
Convolutional Neural Networks (CNNs) have received substantial attention as a highly
effective tool for analyzing medical images, notably in interpreting endoscopic images, due …
effective tool for analyzing medical images, notably in interpreting endoscopic images, due …
[PDF][PDF] An Embarrassingly Simple Method to Compromise Language Models
J Wang - 2024 - preprints.org
Language models like BERT dominate current NLP research due to their robust
performance, but they are vulnerable to backdoor attacks. Such attacks cause the model to …
performance, but they are vulnerable to backdoor attacks. Such attacks cause the model to …
Machine Learning Methods for Handwriting Recognition
Y Peng - 2023 - preprints.org
Abstract Machine learning is a fundamental aspect of artificial intelligence that involves the
development of algorithms and models that allow computers to learn and make predictions …
development of algorithms and models that allow computers to learn and make predictions …