foryichuanqi

foryichuanqi's Stars

• gabrieleangeletti/Deep-Learning-TensorFlow

  Ready to use implementations of various Deep Learning algorithms using TensorFlow.

  Language:Python9659161376

• cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

  Fully Convlutional Neural Networks for state-of-the-art time series classification

  Language:Python6772720202

• XifengGuo/DEC-keras

  Keras implementation for Deep Embedding Clustering (DEC)

  Language:Python4771032162

• ZhangWei1993/Mechanical-Fault-Diagnosis-Based-on-Deep-Learning

  CNN for mechanical fault diagnosis

  Language:Python28512685

• zhuofupan/Tensorflow-Deep-Neural-Networks

  用Tensorflow实现的深度神经网络。

  Language:Python136111955

• schwxd/LSTM-Keras-CMAPSS

  Using LSTM to predict Remaining Useful Life of CMAPSS Dataset

  Language:Python811021

• jaromiru/cwcf

  Source code for paper Classification with Costly Features using Deep Reinforcement Learning.

  Language:Python535323

• foryichuanqi/RESS-Paper-2022.09-Remaining-useful-life-prediction-by-TaFCN

  The source code of paper: Trend attention fully convolutional network for remaining useful life estimation in the turbofan engine PHM of CMAPSS dataset. Signal selection, Attention mechanism, and Interpretability of deep learning are explored.

  Language:Python49352

• dlaredo/NASA_RUL_-CMAPS-

  Remaining Useful Life (NASA CMAPS Dataset)

  Language:TeX412111

• foryichuanqi/ADVEI-Paper-2023.11-Zero-shot-fault-diagnosis-by-attribute-fusion-transfer

  Zero-shot fault diagnosis on the Tennessee–Eastman process by attribute fusion transfer. Paper: Attribute fusion transfer for zero-shot fault diagnosis

  Language:Python21110

• foryichuanqi/ADVEI-Paper-2024.3-Degradation-path-approximation-for-remaining-useful-life-estimation

  Remaining useful life prediction. Degradation path approximation (DPA) is a highly easy-to-understand and brand-new solution way for data-driven RUL prediction. Many research directions on DPA can be further studied.

  Language:Python10200

• foryichuanqi/TKDE-Paper-2023.10-Time-series-classification-by-MEB-ResNet

  Paper: Multi-Scale Ensemble Booster for Improving Existing TSD Classifiers. We proposed a highly easy-to-use performance enhancement framework called multi-scale ensemble booster(MEB), helping existing time series classification methods achieve performance leap. Our proposed MEB-ResNet achieved the most advanced time series classification ability.

  Language:Python7200

• tbonus/catGAN

  Language:Python410