A random feature mapping method based on the AdaBoost algorithm and results fusion for enhancing classification performance
W Shan, D Li, S Liu, M Song, S Xiao… - Expert Systems with …, 2024 - Elsevier
The feature mapping method can improve data separability, enhance data representation
ability, and reduce data processing complexity. However, on the one hand, the existing …
ability, and reduce data processing complexity. However, on the one hand, the existing …
Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective
The convergence of physics-informed and machine learning has led to the emergence of
Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability …
Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability …
OHCA-GCN: A novel graph convolutional network-based fault diagnosis method for complex systems via supervised graph construction and optimization
Fault diagnosis is crucial for ensuring the safe and stable operation of complex systems.
Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged …
Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged …
Predicting Vulnerable Road User Behavior With Transformer-Based Gumbel Distribution Networks
This study introduces the crossing intention and trajectory with the Transformer networks
(CITraNet) prediction model to process multimodal input data of vulnerable road users …
(CITraNet) prediction model to process multimodal input data of vulnerable road users …
A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
EH Cui, S Shao - arXiv preprint arXiv:2405.12390, 2024 - arxiv.org
Principal curve is a well-known statistical method oriented in manifold learning using
concepts from differential geometry. In this paper, we propose a novel metric-based principal …
concepts from differential geometry. In this paper, we propose a novel metric-based principal …
[PDF][PDF] Research on dependent evidence combination based on principal component analysis
X Su, S Shang, L Xiong, Z Hong… - Mathematical Biosciences …, 2024 - aimspress.com
Dempster-Shafer evidence theory, as a generalization of probability theory, is a powerful
tool for dealing with a variety of uncertainties, such as incompleteness, ambiguity, and …
tool for dealing with a variety of uncertainties, such as incompleteness, ambiguity, and …