$\require{mediawiki-texvc}$

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.

An enhanced prediction model for the on-line monitoring of the sensors using the Gaussian process regression

Journal of mechanical science and technology, v.33 no.5, 2019년, pp.2249 - 2257  

Lee, Sungyeop ,  Chai, Jangbom

초록이 없습니다.

참고문헌 (20)

  1. Artificial Intelligence Review A Christopher 11 11 1997 10.1023/A:1006559212014 A. Christopher, M. Andrew and S. Stefan, Locally weighted learning, Artificial Intelligence Review, 11 (1997) 11-73. 

  2. Theory of Probability & Its Applications E A Nadaraya 9 1 141 1964 10.1137/1109020 E. A. Nadaraya, On estimating regression, Theory of Probability & Its Applications, 9 (1) (1964) 141-142. 

  3. G S Watson 359 1964 Sankhyā: The Indian Journal of Statistics G. S. Watson, Smooth regression analysis, Sankhyā: The Indian Journal of Statistics, Series A (1964) 359-372. 

  4. M P Wand 1994 Kernel Smoothing 10.1201/b14876 M. P. Wand and M. C. Jones, Kernel Smoothing, Chapman and Hall/CRC (1994). 

  5. J W Hines 2008 Theoretical Issues J. W. Hines, D. Garvey, R. Seibert and A. Usynin, Technical review of on-line monitoring techniques for performance assessment, Theoretical Issues, The University of Tennessee-Knoxville, 2 (2008). 

  6. C K Williams 4 2006 Gaussian Processes for Machine Learning C. K. Williams and C. E. Rasmussen, Gaussian Processes for Machine Learning, MIT Press, 2 (3) (2006) 4. 

  7. IEEE Transactions on Reliability F Di Maio 62 4 833 2013 10.1109/TR.2013.2285033 F. Di Maio, P. Baraldi, E. Zio and R. Seraoui, Fault detection in nuclear power plants components by a combination of statistical methods, IEEE Transactions on Reliability, 62 (4) (2013) 833-845. 

  8. N Sairam 2016 Computer, Electrical & Communication Engineering (ICCECE) International Conference on. IEEE N. Sairam and S. Mandal, Thermocouple sensor fault detection using auto-associative Kernel regression and generalized likelihood ratio test, Computer, Electrical & Communication Engineering (ICCECE) International Conference on. IEEE (2016). 

  9. S AI-Dahidi 2014 Second European Conference of the Prognostics and Health Management Society S. AI-Dahidi, P. Baraldi, F. Di Maio and E. Zio, Quantification of signal reconstruction uncertainty in fault detection systems, Second European Conference of the Prognostics and Health Management Society (2014). 

  10. M Alamaniotis 2010 European Safety and Reliability Conference M. Alamaniotis, A. Ikonomopoulos and L. H. Tsoukalas, Gaussian processes for failure prediction of slow degradation components in nuclear power plants, European Safety and Reliability Conference, Prague, Czech Republic (2010). 

  11. Chemical Engineering Transactions V Vitelli 33 907 2013 10.3333/CET1333152 V. Vitelli and E. Zio, Approximate Gaussian process regression with sparse functional learning of inducing points for components condition monitoring, Chemical Engineering Transactions, 33 (2013) 907-912, Doi: https://doi.org/10.3333/CET1333152 . 

  12. Progress in Nuclear Energy P Baraldi 78 141 2015 10.1016/j.pnucene.2014.08.006 P. Baraldi, F. Mangili and E. Zio, A prognostics approach to nuclear component degradation modeling based on Gaussian process regression, Progress in Nuclear Energy, 78 (2015) 141-154. 

  13. R Tipireddy 2017 ANS 10th International Topical Meeting on NPIC-HMIT R. Tipireddy, M. Lerchen and P. Ramuhalli, Virtual sensors for robust on-line monitoring (OLM) and diagnostics, ANS 10th International Topical Meeting on NPIC-HMIT (2017). 

  14. A Nair 2017 ANS 10th International Topical Meeting on NPIC-HMIT A. Nair and J. Coble, Bayesian inference for high confidence signal validation and sensor calibration assessment, ANS 10th International Topical Meeting on NPIC-HMIT (2017). 

  15. P Ramuhalli 2017 NPIC & HMIT 2017 P. Ramuhalli, R. Tipireddy, M. Lerchen, B. Shumaker, J. Coble, A. Nair and S. Boring, Robust online monitoring for calibration assessment of transmitters and instrumentation, NPIC & HMIT 2017, Pacific Northwest National Lab.(PNNL), Richland, WA (United States) (2017). 

  16. P Ramuhalli 2014 No. PNNL-22847 Rev. 1 P. Ramuhalli, G. Lin, S. L. Crawford, B. A. Konomi, J. B. Coble, B. Shumaker and H. Hashemian, Uncertainty quantification techniques for sensor calibration monitoring in nuclear power plants, No. PNNL-22847 Rev. 1, Pacific Northwest National Laboratory (PNNL), Richland, WA (US) (2014). 

  17. R N Neal 1994 Technical Report No. CRG-TR-94-1 R. N. Neal, Priors for infinite networks, Technical Report No. CRG-TR-94-1, University of Toronto (1994). 

  18. R N Neal 1994 Ph.D. Thesis R. N. Neal, Bayesian learning for neural networks, Ph.D. Thesis, University of Toronto (1994). 

  19. C K Williams 295 1997 Advances in Neural Information Processing Systems C. K. Williams, Computing with infinite networks, Advances in Neural Information Processing Systems (1997) 295-301. 

  20. J Lee 2017 Deep neural networks as Gaussian processes J. Lee, Y. Bahri, R. Novak, S. S. Schoenholz, J. Pennington and J. Sohl-Dickstein, Deep neural networks as Gaussian processes, Arxiv Preprint Arxiv: 1711.00165 (2017). 

LOADING...
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