Szl0123

Hybrid-Battery-Feature-Selection

Physics-infromed-autoencoder

phygnn

physics-guided neural networks (phygnn)

STExplainer

[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"

Li-ion-Battery-Prognosis-Based-on-Hybrid-Bayesian-PINN

Code used to generate the results of the paper: Nascimento et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks.

Datasets

Some private test datasets

PyBatteryID

Data-driven Battery Model Identification in LPV Framework using Python

MXiongThesis

Code

RDARENet

Single Image Deraining Using a Recurrent Dual-Attention-Residual Ensemble Network

SDATR

Official Code for "Knowing what it is: Semantic-enhanced Dual Attention Transformer" (TMM2022)

RUL-predication-of-Li-ion-batteries-using-GPR-with-IHIs

Determining the lifespan of lithium-ion batteries is important for safety and reliability. This study uses voltage, current, and time data to estimate battery health and remaining useful life. It focuses on Gaussian Process Regression (GPR).

RUL_Battery

Battery-Competition-2024

battery capacity and RUL prediction

LSTM-GANS-RUL-Prediction-for-Lithium-ion-Bateries

This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).

RUL-code

Code for the paper titled Maximizing the performance of data-driven capacity estimation for lithium-ion battery

BatteryRUL_LSTM

Li-ion-Battery-RUL-Prediction

Capstone Project

Battery_RUL

Battery_RUL

RUL-KNN

Battery remaining useful life using K Nearest Neighbors

BatteryML

MB-vit

MvFIF

Multivariate Fast Iterative Filtering

SELF_GS

A stacking ensemble learning for genomic prediction

DualAttentionSeq2Seq

Analysis of Time Series data using Seq2Seq LSTM and 2 attention layers

dual_stage_attention_rnn

A Tensorflow Implementation of Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

CNN-with-Dual-Local-and-Global-Attention

Implementaion of Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction

temporal_fusion_transformer_pytorch

Stoch-predict-with-Tranformer-LSTM

stock predict with MLP,CNN,RNN,LSTM,Transformer and Transformer-LSTM