Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory. - Chun-Yao Lee; Truong-An Le (IEEE Access, 2020)

Identifying Faults of Rolling Element Based on Persistence Spectrum and Convolutional Neural Network With ResNet Structure. - Chun-Yao Lee; Truong-An Le (IEEE Access, 2021)

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines. - Chun-Yao Lee; Truong-An Le (IEEE Access, 2021)

Optimized Approach of Feature Selection Based on Binary Genetic Algorithm in Classification of Induction Motor Faults. - Truong-An Le (RICE, 2022)

Intelligent Fault Diagnosis for BLDC With Incorporating Accuracy and False Negative Rate in Feature Selection Optimization. - Chun-Yao Lee; Chen-Hsu Hung; Truong-An Le (IEEE Access, 2022)

A Feature Selection Approach Hybrid Grey Wolf and Heap-Based Optimizer Applied in Bearing Fault Diagnosis. - Chun-Yao Lee; Truong-An Le; Yu-Ting Lin (IEEE Access, 2022)

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions. - Chun-Yao Lee; Guang-Lin Zhuo; Truong-An Le (Sensors, 2022)

A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model. - Chun-Yao Lee; Truong-An Le; Chun-Lin Hung (IEEE Access, 2023)