과제정보
본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제입니다(과제번호 : 20224B10100030).
참고문헌
- Yun. H., Moon. S.J., and Oh. Y.J., "Development of wall-thinning evaluation procedure for nuclear power plant piping - Part 2: Local wall-thinning estimation method", Nuclear Engineering and Technology, Vol.52, No.9, pp.2119-2129, 2020. doi:https://doi.org/10.1016/j.net.2020.03.001
- Oh. Y.J., Jee K.K. and Park. H.B., "Machine Learning Approach for Thinned Pipe Classification using Thickness Measurement Data of Nuclear Secondary Piping Systems", Trans. of the Korean Nuclear Society Spring Meeting, Jeju, 2019.05.23. - 24.
- Oh. Y.J., Yun. H., Moon. S.J., Han K.H. and Park. B.U., 2015, "Development of Numerical Algorithm of Total Point Method for Thinning Evaluation of Nuclear Secondary Pipes", Trans. of the KPVP, Vol.11, No.2, pp. 31-39.
- Oh. Y.J., Yun. H. and Park. H.B., 2018, "Preprocessing Method of Measurement Data for Machine-Learning Application to Thinning Location Prediction", Proc. of 2018 KPVP, Gwangju, 2018.11.22 -23.
- Yun. H., Moon. S.J, and Oh. Y.J., "Development of Wall-Thinning Evaluation Procedure for Nuclear Power Plant Piping Part 1: Quantification of Thickness Measurement Deviation", Nuclear Engineering and Technology, Vol.48, No.3, pp.820-830, 2016. doi:http://dx.doi.org/10.1016/j.net.2016.01.020
- Aurelien Geron, 2020, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd, Hanbit Media Inc., pp.216-218.
- Jang. H.Y., Hwang. H.R., Park. H.B., Yun. H., Oh. Y.J., Kim. K.S., Lee. D.Y., 2020, "Development of improved management method for nuclear secondary pipe thinning using machine learning and artificial intelligence technique", KEPCO-ENC Technical development report.