Neural network paradigm for classification of defects on PCB
A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed base...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2003
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| Online Access: | http://eprints.utm.my/2086/1/JTMKK39%28D%29bab9.pdf |
| _version_ | 1825909180084518912 |
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| author | Heriansyah, Rudi Syed Al-Attas, Syed Abdul Rahman Zabidi, Muhammad Mun'im Ahmad |
| author_facet | Heriansyah, Rudi Syed Al-Attas, Syed Abdul Rahman Zabidi, Muhammad Mun'im Ahmad |
| author_sort | Heriansyah, Rudi |
| collection | ePrints |
| description | A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. Thousands of defective patterns have been used for training, and the neural network is tested for evaluating its performance. A defective PCB image is used to ensure the function of the proposed technique. |
| first_indexed | 2024-03-05T17:58:19Z |
| format | Article |
| id | utm.eprints-2086 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:58:19Z |
| publishDate | 2003 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-20862017-11-01T04:17:39Z http://eprints.utm.my/2086/ Neural network paradigm for classification of defects on PCB Heriansyah, Rudi Syed Al-Attas, Syed Abdul Rahman Zabidi, Muhammad Mun'im Ahmad TK Electrical engineering. Electronics Nuclear engineering A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. Thousands of defective patterns have been used for training, and the neural network is tested for evaluating its performance. A defective PCB image is used to ensure the function of the proposed technique. Penerbit UTM Press 2003-12 Article PeerReviewed application/pdf en http://eprints.utm.my/2086/1/JTMKK39%28D%29bab9.pdf Heriansyah, Rudi and Syed Al-Attas, Syed Abdul Rahman and Zabidi, Muhammad Mun'im Ahmad (2003) Neural network paradigm for classification of defects on PCB. Jurnal Teknologi D (39D). pp. 87-104. ISSN 0127-9696 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Heriansyah, Rudi Syed Al-Attas, Syed Abdul Rahman Zabidi, Muhammad Mun'im Ahmad Neural network paradigm for classification of defects on PCB |
| title | Neural network paradigm for classification of defects on PCB |
| title_full | Neural network paradigm for classification of defects on PCB |
| title_fullStr | Neural network paradigm for classification of defects on PCB |
| title_full_unstemmed | Neural network paradigm for classification of defects on PCB |
| title_short | Neural network paradigm for classification of defects on PCB |
| title_sort | neural network paradigm for classification of defects on pcb |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/2086/1/JTMKK39%28D%29bab9.pdf |
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