Acknowledgement
본 연구는 2022년도 산업통상자원부의 재원으로 한국에너지기술평가원(KETEP)의 지원과 한국연구재단의 지원을 받아 수행한 을 받아 수행한 연구과제입니다((No. 20224B10200050; No. RS-2022-00154571).
References
- Bordeleau F, Combemale B, Eramo R, van den Brand M, Wimmer M. Towards model-driven digital twin engineering: Current opportunities and future challenges. In: Systems Modelling and Management: First International Conference, ICSMM 2020, Bergen, Norway, June 25-26. 2020 Jun;43-54.
- Lim S, Choi K, Cho G. A study on 3D model building of drones-based urban digital twin. Journal of Cadastre & Land InformatiX [Internet]. 2020 Jun;50(1):163-180.
- Booyse W, Wilke DN, Heyns S. Deep digital twins for detection, diagnostics and prognostics. Mechanical Systems and Signal Processing [Internet]. 2020 Jun;140:106612.
- Kim DC, Kim GG, Kwag SY, Eem SH. Constructing a digital twin system for estimating the response of a piping system subjected to arbitrary loads. Korea Society of Civil Engineers [Internet]. 2022: 190-191.
- Kim DC, Kim GG, Kwag SY, Eem SH. Constructing a digital twin for estimating the response and load of a piping system subjected to seismic and arbitrary loads. Smart Structures and Systems [Internet]. 2023 Mar;31(3):275-281.
- Lo CK, Chen CH, Zhong RY. A review of digital twin in product design and development. Advanced Engineering Informatics [Internet]. 2021 Apr;48:101297.
- Kim J, Choi W, Song M, Lee S. Design and implementation of IoT platform-based digital twin prototype. Journal of Broadcast Engineering [Internet]. 2021 Jul;26(4):356-367.
- Lee SH, Yun BD. Industry 4.0 and the direction of failure prediction and health management technology (PHM). Journal of KSNVE [Internet]. 2015 Feb;25(1):22-28.
- Han SJ, Oh SI, Choi JH, Kim JG. Real-time virtual sensor technology using model-driven digital twin. Noise․ Vibration [Internet]. 2021 Jan;31(1):4-11.
- Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology [Internet]. 2018 Mar;94:3563-3576. https://doi.org/10.1007/s00170-017-0233-1
- Wang B, Zhang G, Wang H, Xuan J, Jiao K. Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model. Energy and AI [Internet]. 2020 Aug;1:100004.
- Jang S, Kim B, Yoon H, Seo Y, Lee H, Lee J, Lee T. Implementation of virtual reality model for offshore gas field platform and evaluation of gas hydrate formation for subsea production pipeline using AI. Journal of The Korean Society of Mineral and Energy Resources Engineers [Internet]. 2021 Apr;58(2):150-160. https://doi.org/10.32390/ksmer.2021.58.2.150
- Kang J, Chung K, Hong EJ. Multimedia knowledge-based bridge health monitoring using digital twin. Multimedia Tools Appl [Internet]. 2021 Feb;80(26-27):34609-34624. https://doi.org/10.1007/s11042-021-10649-x
- Ellingwood BR, Mori Y. Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants. Nucl Eng Des [Internet]. 1993 Aug;142(2-3):155-166. https://doi.org/10.1016/0029-5493(93)90199-J
- Shim C, Dang N, Lon S, Jeon C. Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Structure and Infrastructure Engineering [Internet]. 2019 Jun;15(10):1319-1332. https://doi.org/10.1080/15732479.2019.1620789
- Kim HY. [Terminology and architecture] structural health monitoring. Architecture [Internet]. 2018 Nov;62(11):78.
- Sony S, Laventure S, Sadhu A. A literature review of next-generation smart sensing technology in structural health monitoring. Structural Control and Health Monitoring [Internet]. 2019 Jan; 26(3):e2321. https://doi.org/10.1002/stc.2321
- Oh S, Park D, Baek HW, Kim SH, Lee JK, Kim JG. Virtual sensing system of structural vibration using digital twin. Trans. Korean Soc. Noise & Vib. Eng [Internet]. 2020 Feb;30(2):149-160. https://doi.org/10.5050/KSNVE.2020.30.2.149
- Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput [Internet]. 1997 Jan;9(8):1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Kim HW, Park SH. LSTM Based LKAS yaw rate prediction model using lane information and steering angle [Internet]. 2018 Mar; 26(2):279-287. https://doi.org/10.7467/KSAE.2018.26.2.279
- OECD N. Integrity of structures, systems and components under design and beyond design loads in nuclear power p lants: Final report of the project on metallic component margins under high seismic loads (MECOS). [Internet]. 2018
- Lagarias JC, Reeds JA, Wright MH, Wright PE. Convergence properties of the nelder--mead simplex method in low dimensions. SIAM Journal on optimization [Internet]. 1998;9(1):112-147. https://doi.org/10.1137/S1052623496303470
- Jeon BK, Lee KH, Kim E J. Development of a prediction model of solar irradiances using LSTM for use in building predictive control. Journal of the Korean Solar Energy Society[Internet]. 2019 Oct; 39(5):41-52. https://doi.org/10.7836/kses.2019.39.5.041