$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

[해외논문] Machine learning-based automated image processing for quality management in industrial Internet of Things 원문보기

International journal of distributed sensor networks, v.15 no.10, 2019년, pp.155014771988355 -   

Rahmatov, Nematullo (The School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea) ,  Paul, Anand (The School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea) ,  Saeed, Faisal (The School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea) ,  Hong, Won-Hwa (The School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, South Korea) ,  Seo, HyunCheol (The School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, South Korea) ,  Kim, Jeonghong (The School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea)

Abstract AI-Helper 아이콘AI-Helper

The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used tech...

참고문헌 (44)

  1. 10.1007/978-3-642-33905-9 

  2. Malamas, Elias N, Petrakis, Euripides G.M, Zervakis, Michalis, Petit, Laurent, Legat, Jean-Didier. A survey on industrial vision systems, applications and tools. Image and vision computing, vol.21, no.2, 171-188.

  3. Moganti, Madhav, Ercal, Fikret, Dagli, Cihan H., Tsunekawa, Shou. Automatic PCB Inspection Algorithms: A Survey. Computer vision and image understanding : CVIU, vol.63, no.2, 287-313.

  4. International conference on signal processing applications and technology (ICSPAT’98) Kim KH 

  5. International conference on signal processing applications and technology Chung Y 

  6. 10.4103/0256-4602.110555 

  7. Paul, Anand, Bharanitharan, K., Wang, Jhing-Fa. Region similarity based edge detection for motion estimation in H.264/AVC. IEICE Electronics Express, vol.7, no.2, 47-52.

  8. International conference on signal processing applications and technology (ICSPAT’98) Khandogin I 

  9. Tretter, Daniel, Bouman, Charles A., Khawaja, Khalid W., Maciejewski, Anthony A.. A multiscale stochastic image model for automated inspection. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.4, no.12, 1641-1654.

  10. Paul, Anand. Adaptive Search Window for High Efficiency Video Coding. Journal of signal processing systems : for signal, image, and video technology, vol.79, no.3, 257-262.

  11. 10.4103/0256-4602.107336 

  12. Chen, Shangong, Lin, Bin, Han, Xuesong, Liang, Xiaohu. Automated inspection of engineering ceramic grinding surface damage based on image recognition. International journal of advanced manufacturing technology, vol.66, no.1, 431-443.

  13. Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.F., Debeir, O.. Automatic grading of Bi-colored apples by multispectral machine vision. Computers and electronics in agriculture, vol.75, no.1, 204-212.

  14. Yuen, C.W.M., Wong, W.K., Qian, S.Q., Chan, L.K., Fung, E.H.K.. A hybrid model using genetic algorithm and neural network for classifying garment defects. Expert systems with applications, vol.36, no.2, 2037-2047.

  15. 10.4304/jsw.5.6.573-578 

  16. Dietterich, Thomas G., Lathrop, Richard H., Lozano-Pérez, Tomás. Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence, vol.89, no.1, 31-71.

  17. 2005 IEEE international conference on multimedia and expo (ICME 2005) Zhang C 1142 

  18. Paul, Anand, Rho, Seungmin, Bharnitharan, K.. Interactive scheduling for mobile multimedia service in M2M environment. Multimedia tools and applications, vol.71, no.1, 235-246.

  19. 2009 IEEE conference on computer vision and pattern recognition (CVPR 2009) Babenko B 983 

  20. Paul, Anand, Pinjari, Hameed, Hong, Won-Hwa, Seo, Hyun Cheol, Rho, Seungmin. Fog Computing-Based IoT for Health Monitoring System. Journal of sensors, vol.2018, 1-7.

  21. A vision of cyber-physical cloud computing for smart networked systems Simmon E 10.6028/NIST.IR.7951 

  22. Wang, Yunbo, Vuran, Mehmet C., Goddard, Steve. Cyber-physical systems in industrial process control. SIGBED review, vol.5, no.1, 1-2.

  23. 2nd international conference on applied and theoretical information systems research Zhou J 

  24. 2006 IEEE international symposium on industrial electronics Vasilic S 469 

  25. ACM T Embed Comput S Paul A 25 12 2 2013 

  26. Wu, Jianxin, Bai, Xiang, Loog, Marco, Roli, Fabio, Zhou, Zhi-Hua. Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision. Pattern recognition, vol.71, 444-445.

  27. Encyclopedia of machine learning Ray S 701 2011 10.1007/978-0-387-30164-8_569 

  28. Reka, S.S., Ramesh, V.. Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming. Perspectives in science, vol.8, 169-171.

  29. Proceedings of the 1st ACM/IEEE international conference on cyber-physical systems Parolini L 168 

  30. 2010 9th IEEE/IAS international conference on industry applications (INDUSCON) Zhao P 1 

  31. Peng Zhao, Suryanarayanan, S., Simoes, M. G.. An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology. IEEE transactions on industry applications, vol.49, no.1, 322-330.

  32. ACM T Embed Comput S Paul A 40 11 2012 

  33. Lee, Jay, Bagheri, Behrad, Kao, Hung-An. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing letters, vol.3, 18-23.

  34. Paul, Anand, Rho, Seungmin. Probabilistic Model for M2M in IoT networking and communication. Telecommunication systems, vol.62, no.1, 59-66.

  35. Proceedings of the 2003 IEEE 46th Midwest symposium on circuits and systems Paul A 

  36. Paul, Anand. Real-Time Power Management for Embedded M2M Using Intelligent Learning Methods. ACM transactions on embedded computing systems, vol.13, no.5, 1-22.

  37. Daniel, Alfred, Paul, Anand, Ahmad, Awais, Rho, Seungmin. Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS). Wireless personal communications, vol.87, no.2, 461-484.

  38. Paul, Anand, Daniel, Alfred, Ahmad, Awais, Rho, Seungmin. Cooperative Cognitive Intelligence for Internet of Vehicles. IEEE systems journal, vol.11, no.3, 1249-1258.

  39. Proceeding of the 2013 research in adaptive and convergent systems Paul A 45 

  40. Intelligent vehicular networks and communications: fundamentals, architectures and solutions Paul A 2016 

  41. Paul, Anand, Ahmad, Awais, Rathore, M. Mazhar, Jabbar, Sohail. Smartbuddy: defining human behaviors using big data analytics in social internet of things. IEEE wireless communications, vol.23, no.5, 68-74.

  42. A comparison of multi-instance learning algorithms Dong L 2006 

  43. Cheplygina, V., Tax, D.M.J., Loog, M.. Multiple instance learning with bag dissimilarities. Pattern recognition, vol.48, no.1, 264-275.

  44. MIL: a MATLAB toolbox for multiple instance learning Tax DMJ 2011 

LOADING...

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

유발과제정보 저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로