Table of Contents

2021 Project Page
- Transfer learning for battery parameter estimation
  - Meeting Notes

2021 Project Page

Transfer learning for battery parameter estimation

Meeting Notes

2021/10/6

Attendee: Hanbing, Yanru, Yang
Meeting summary:
- Yanru: on reading: Unsupervised Domain Adaptation for Semantic Segmentation via CBST
- Hanbing
  - research: optimal transport and domain adaptation problem
  - reading: Joint distribution optimal transportation for domain adaptation; Few-Shot Cross Domain Battery Capacity Estimation
  - slides: optimal transport and domain adaptation

TODO:
- Yanru: make a plan of future work; deeper understanding of papers/ better presentation of work
- Hanbing: do some research on the application of adversarial network to domain adaptation problem

2021/10/13

Attendee: Hanbing, Yanru, Yang
Meeting summary:
- Yanru:
  - reading: transferable semi-supervised semantic segmentation; Adversarial Examples for Semantic Segmentation and Object Detection
  - experiment: tried MIoU metric for H-score evaluation; on coding new dataset
- Hanbing:
  - reading: Adversarial Discriminative Domain Adaptation
  - research: Adversarial network in Domain Adaptation
  - slides: adversarial domain adaptation
TODO:
- Yanru:
  - more relative reading on transfer learning & U-net (also maybe some survey on semantic segmentation & related area);
  - experiment redesign
    - target and source setting (H-score more suitable for task transfer)
    - dataset with fewer noise & lower difficulty
    - new calculate method for H-score (devision depending on label instead of pixel location)
- Hanbing:
  - deeper reading ADDA to find its advantage over other adversarial domain adaptation works
  - modify the ADDA model to deal with battery features

2021/10/20

Attendee: Hanbing, Yanru, Yang
Meeting summary:
- Yanru:
  - experiment: H-score trial on task transfer using ADE20K – especially good results for 'sky' task
- Hanbing:
  - experiment: ADDA using battery pulse data
  - slides:adda_test.pptx
TODO:
- Yanru:
  - recode the experiment with pytorch
  - cityscape dataset preprocessing
  - change of target data size
  - reading
- Hanbing:
  - change convolution layer to fully connect or conv1D
  - transform battery data into frequency domain

2021/11/10

Attendee: Hanbing, Yang
Meeting summary:
- Hanbing:
  - experiment: unsupervised fully connection ADDA using battery pulse data
TODO:
- Hanbing:
  - experiment:
    1. source only: divide source dataset into training set and testing set to see if model is overfitting
    2. target only: using only target data to get target baseline model
    3. transfer learning: finetuning target feature extractor and source classifier useing a few target label

2021/11/14

Attendee: Yanru, Yang
Meeting summary:
1. A brief review of several papers related to spatial-temporal data analyses (project_reading.pdf)
2. Notes: notes1114.png
TODO: write background part of project plan

2021/11/17

Attendee: Hanbing, Yang
Meeting summary:
- Hanbing:
  - experiment:
    1. source only and target only on original net and simplified net
    2. add penalty term on target model
TODO:
- Hanbing:
  - experiment:
    1. finetunning target model useing a few target label
    2. change visual form of result