deep-learning

CLDDGAN for CWT Generation and Fault Classification Using LiteFormer2D

This study presents CLDDGAN for generating high-quality synthetic CWT data and fault classification using the lightweight LiteFormer2D architecture for rotating machinery fault diagnosis.

An Efficient Approach for Synthetic Data Generation and Fault Diagnosis for Rotating Machinery

This study explores the use of an Auxiliary Classifier Wasserstein GAN with Gradient Penalty (ACWGAN-GP) for synthetic data generation and fault diagnosis in rotating machinery, addressing data scarcity and class imbalance challenges.

Large-Scale DRL Exploration with Tunable Adaptive Exploration-Exploitation

Deep reinforcement learning-based reactive planner for large-scale Lidar-based autonomous robot exploration in 2D action space with novel alpha conditioning for exploration-exploitation control.