Yuwen Qin's repositories
Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
data-driven-prediction-of-battery-cycle-life-before-capacity-degradation
Code for Nature energy manuscript
hsds-examples
Examples of using the HSDS Service to Access NREL WIND Toolkit data
LTSF-Linear
This is the official implementation for AAAI-23 Oral paper "Are Transformers Effective for Time Series Forecasting?"
Two_Stage_RUL_Prediction
The code is for the paper "Ma et al. A Two-Stage Integrated Method for Early Prediction of Remaining Useful Life of Lithium-ion Batteries"
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
Awesome-of-Time-Series-Augmentation
A curated list of time series augmentation resources.
HowToLiveLonger
程序员延寿指南 | A programmer's guide to live longer
ielts
IELTS guide and Cambridge English authentic examination papers (4-15, A+G) for programmers. 程序员雅思备考指南+剑雅4-15真题(A类+G类)全套。
Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
PyBaMM
Fast and flexible physics-based battery models in Python
pytorch-Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis
Deep Residual Shrinkage Networks for Intelligent Fault Diagnosis(pytorch) 深度残差收缩网络应用于故障诊断
rex
REsource eXtraction Tool (rex)
tianya-docs
精心收集的天涯神贴,不带水印,方便阅读
time-series-transformers-review
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
Time-Series-Works-Conferences
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, etc.)
time_series_augmentation_recommendation
An example of time series augmentation methods with Keras
tutorials
PyTorch tutorials.
UDTL
Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Wake_Steering_of_Wind_Turbines_using_NREL-s_FLORIS
Increased 3x3 wind farm output by 18.5\% using NREL's FLORIS library in Python with optimum yaw angles to each wind turbine Studied 4 wake models, fatigue life of turbine, optimal power outputs for 3x3 wind farm caused by yaw misalignment of wind turbines