repository
Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
3. Theses(Master)
뉴럴 네트워크 기반 연성 인쇄 회로 기판 검사 시스템
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박상철 | - |
| dc.contributor.author | Cho, Kang Hoon | - |
| dc.date.accessioned | 2018-11-08T08:20:59Z | - |
| dc.date.available | 2018-11-08T08:20:59Z | - |
| dc.date.issued | 2015-08 | - |
| dc.identifier.other | 20774 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/12890 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :산업공학과,2015. 8 | - |
| dc.description.tableofcontents | Chapter 1 Introduction 1 Section 1 The Background of a Research 2 Section 2 Inspection of F-PCB (Flexible Printed Circuit Board) 4 Section 3 The related Research 7 Section 4 Purpose of Research and Organization 8 Chapter 2 Neural Network 9 Section 1 The Introduction of Neural Network 10 Section 2 Model of Neural Network 13 Section 3 Learning Method of Neural Network 14 Chapter 3 Technique of Image Filtering 18 Section 1 Introduction of Technique: Previous Image Processing 19 Section 2 Sweep Searching Filtering 22 Section 3 Island Filtering 27 Chapter 4 Extraction of Parameter: Image Characteristic 31 Section 1 Object Images Characteristic 32 Section 2 Method to Extract Parameter : Object Images Characteristic 33 Section 3 Method of Application Parameter Characteristic 43 Chapter 5 Implementation Inspection System for F-PCB and its Result 45 Section 1 The Implementation and Result of Inspection System based on Neural Network 46 Chapter 6 Conclusion and Hereafter Research 48 Bibliography 51 Abstract 53 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 뉴럴 네트워크 기반 연성 인쇄 회로 기판 검사 시스템 | - |
| dc.title.alternative | An Inspection System for Flexible Printed Circuit Board based on Neural Network | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 산업공학과 | - |
| dc.date.awarded | 2015. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 705505 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000020774 | - |
| dc.subject.keyword | Filtering | - |
| dc.subject.keyword | Flexible Printed Circuit Board | - |
| dc.subject.keyword | Inspection | - |
| dc.subject.keyword | Neural Network | - |
| dc.description.alternativeAbstract | This thesis judges the excessive images among images which are objects to inspection in visual inspection, and suggests the inspection system based on neural network to minimize the images which was regarded as faulty image from image of fair quality. F-PCB (Flexible Printed Circuit Board) operates to inspection quality firstly by the inspection device, the image regarded as faulty should be the object to inspection in macrograph. The objects of visual inspection need to take second inspection by professional people, but they shall be wasted and shall decrease their unity of qualities because the number of images are too much. For these matters, there need to have the inspection system for figuring out whether something would be fair quality or faulty. To structure the proposed inspection system, using the neural network can solve these matters. Neural network have been known as the level of studying or the level of testing. In the stage of studying, it can be far different depending on the data; consistency of data is important. Visual inspection images involve noisy due to the place or position of inspection device. This paper suggest ‘Sweep Searching Filtering,’ ‘Island Filtering’ to find a key from these matters because images involving noisy let down the consistent data. Moreover, it also suggests four different types of methods to extract a parameter, characteristic of images for studying. The suggested inspection system in this studying will judge the excessive images, have an objective to minimize the faulty image from image of fair quality, and demonstrate the efficiency of inspection system through the studied neural network. | - |
- Appears in Collections:
- Special Graduate Schools > Graduate School of Science and Technology > Department of Industrial Engineering > 3. Theses(Master)
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
-
- License
- STATISTICS
- Total Visit :3,742,424
- Total Download :1,818
- Today View :20,082
