April 2020 - February 2021
1. Collecting the data to train the model by using python(scraping).
2. Observe the result that comes out from the model, and improve the accuracy by the filtering method using python script.
3. Scripting to test the API that backend established, and also the static works.
4. Project managing.
July 2016 - July 2019
Automated production line planning, feasibility evaluation, cost analysis, as well as mechanism design, appearance design, drawing and requisition of parts and commercially available parts, and assisting in equipment on-site assembly, debugging, and commissioning operations.
2019 - 2021
2011 - 2015
Python Deep Learning RESTfulAPI Android Data Mining linux Java
English — Intermediate Chinese — Native
This is a thesis project which uses 3 neuron networks and SVM to accomplish the sorting work by appearance. The experimental results show that our proposed system can achieve up to 84.5% accuracy rate by testing 800 images.
Using FastAPI to create a shortened url service, including a post and a get method to achieve database saving and redirect. The hash behind the service is MD5, and the DB is SQLite, query in ORM mode.
A light work to accomplish background removal using openCV. In this case, a low frequency filter is used to detect the black border and trim.
This case is my former work as a R&D assistant, using requests framework and Postman to test out the AI model result and collect multiple data to analyze the performance of the training model.