Vincent Chang - 專業領域

個人多年的實務經驗，主要有三個技術：機器視覺技術、智慧製造系統技術與人工智慧技術。倘能強化實務與理論的鏈結，深信有助於學習能力的提升，亦有助於日後的求職生涯。

With many years of personal practical experience, there are three main technologies: machine vision technology, smart manufacturing system, and artificial intelligence technology technology. If the link between practice and theory can be strengthened, I believe this will help enhance learning ability for future job opportunities.

1. 機器視覺技術

2. 智慧製造技術

3. 人工智慧技術

1. Machine Vision Technology

2. Smart manufacturing technology

3. Artificial Intelligence Technology

機器視覺技術-精密視覺導引模組技術(Machine vision technology-precision vision guidance module technology)：
- VGR (Vision Guided Robotic)為主之檢測與定位整合在3C產業應用規劃
- VGR (Vision Guided Robotic)-based detection and positioning integration in 3C industry application.
- 在精密視覺導引定位在金屬製品取放應用部分，利用3D視覺技術估測金屬製品工件的位置與方位，然後取放至定位，之後做後續的加工。
- In the application part of precision vision guidance and positioning for picking and placing metal products, 3D vision technology is used to estimate the position and orientation of the metal product workpiece, and then pick and place it to the positioning, and then perform subsequent processing.

2. 智慧製造技術(Smart manufacturing technology )：

玻璃製程品質回授與BGA植球檢測。在玻璃製程品質回授方面，協助開發完成多機生產狀態及高反光工件品質可視化技術，即時獲取生產狀態資訊，線上分析異常缺陷。
Glass process quality feedback and BGA ball placement testing. In terms of glass process quality feedback, we assisted in the development and completion of multi-machine production status and highly reflective workpiece quality visualization technology, real-time access to production status information, and online analysis of abnormal defects.
BGA植球檢測方面，開發出創新的故障狀態記錄系統，使設備更易於維修，進而提昇產能。
In terms of BGA ball placement detection, an innovative fault status recording system was developed to make the equipment easier to maintain and thereby increase production capacity.

3. 人工智慧技術(Artificial intelligence technology )：

在LCD AOI方面，利用類神經網路技術進行瑕疵檢測與自動瑕疵分類。自動瑕疵分類技術，可使最終使用者因瑕疵的種類分析進一步達到製程的改良。
In terms of LCD AOI, neural network technology is used for defect detection and automatic defect classification. Automatic defect classification technology allows end users to further improve the process based on defect type analysis.
模糊車牌辨識技術，本技術可克服監視系統普遍因成像品質不佳而無法辨識之問題，經由車牌辨識結果，協助警方鑑識人員辨識模糊或殘缺之車號影像。
Fuzzy license plate recognition technology. This technology can overcome the common problem of surveillance systems being unable to recognize due to poor imaging quality. Through the license plate recognition results, it can help police evaluate personnel identify blurred or incomplete vehicle number images.

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聯絡方式：VincentChang0304@ncut.edu.tw