Papers

Title 1: Deep learning-based 3D printer fault detection
- Submitted (ICUFN 2021 - Accepted)

Title 2: An inference time-efficient 3D printer fault detection using CNN
- Submitted (KICS 2021 - Accepted)

Title 3: Edge AI-based 3D Printer Monitoring and Fault Prediction
- Summer Intensive 2021

Title 4: Dataset Anomalies

Last Week's Progress

[1] ICUFN 2021
- Booked hotel and ticket

[3] Summer Intensive
- Introduction
- Surveyed related works
- Collect dataset

This Week Todo's

[1] ICUFN 2021
- Prepare slides for the conference

[3] Summer Intensive
- Collect dataset
- Raspberry Pi Monitoring
- CNN-LSTM (after collecting enough data)

Project Progress

3D Monitoring Project

Last Week's Project

1. Gathered 3D models for dataset collection
2. Surveyed related works (image processing)
3. Start collecting the dataset

This Week's Project

1. Collect dataset (image-based)
2. Study-related works (image processing, fault prediction)

Monthly Goals

1. Journal paper
- Search and review related works
- Compare conventional and machine learning methods

2. Learn more about machine learning and deep learning with Python
- Study for online tutorials
- Read articles
- Re-simulate what I learn

3. Summer Intensive 2021
- Finish collecting dataset (image-based)
- Finish my paper

Annual Goals

1. Publish two or three conference papers (Domestic and International)
- KICS Summer 2021 (done)
- ICUFN 2021 (accepted)

2. Publish at least one International Journal
- Summer Intensive 2021