Name: Dingliang Chen
Type: User
Company: Chongqing University
Bio: I am currently working toward the PhD degree in the School of Mechanical and Vehicle Engineering, Chongqing University
Location: Shapingba District, Chongqing
Blog: https://dingliangchen.gitee.io/
Dingliang Chen's Projects
Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
2019科大讯飞工程机械赛题-亚军
Deep Learning for humans
Keras Denoising Autoencoder
Chinese (zh-cn) translation of the Keras documentation.
MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
《统计学习方法》的代码实现
基于LSTM神经网络的时间序列预测
Bayesian Optimization implementation for text classifiction
Multi-scale Attention Convolutional Neural Network for Time Series Classification
Reproduce MAML in Pytorch with omniglot dataset.
Gated Recurrent Unit implementation in MATLAB
Experiments with Mass Conserving LSTMs
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
Meta-Learning for Few-Shot Time Series Forecasting
Models built with TensorFlow
Motion Planning Networks
Prediction of Remaining Useful Life (RUL) of NASA Turbofan Jet Engine using libraries such as Numpy, Matplotlib and Pandas. Prediction is done by training a model using Keras (TensorFlow).
NLSTM Nested LSTM in Pytorch
NeuroAI-UW seminar, a regular weekly seminar for the UW community, organized by NeuroAI Shlizerman Lab.
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
This is a repository of code and experiment data for paper <A probability confidence CNN model and its application in mechanical fault diagnosis>
this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.
Predicting the Remaining Useful Life (RUL) of simulated turbofan data using Keras and LSTM.
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
One model for RUL and fault prognostic prediction on XJTU bearing dataset