A Study on the Intelligent Platform for Cultural Property Management

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2021 Vol.6, Issue 1 Preview Page Next Page

Article

A Study on the Intelligent Platform for Cultural Property Management 문화재 관리를 위한 지능형 플랫폼 방안 연구: 최종운¹, 김미숙², 윤영철³^†
Jong-un Choi¹, Misuk Kim², Young-chul Yoon³^†; ¹하이테크 대표
²하이테크 선임연구원
³^†가톨릭관동대학교 전자공학과 교수

¹Head of HighTech
²Senior Research Engineer of HighTech
³^†Professor, Department of Electrical Engineering, Catholic Kwandong University

30 April 2021. pp. 13-20

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Abstract

Recently, with the rapid development of IoT technology, the issue of Intelligent Platforms for managing Cultural Properties is increasing. In order to develop an Intelligent Platform for Cultural Property management, Sensors can be installed to collect and analyze internal and external state data of Cultural Properties, and predict with Big Data analysis using Artificial Intelligence algorithms. This Study proposes an external and internal intelligent integrated platform research method based on IoT technology for maintenance and safety management of Cultural Properties. In the proposed Intelligent Platform, a Deep Neural Network (DNN) learning algorithm was proposed to analyze and predict the tilt Sensor data and the Meteorological Agency data considering changes in the external environment as external data of Cultural Properties. In addition, we propose a Convolutional Neural Network (CNN) learning algorithm to analyze and predict image data for detecting pests as internal data of Cultural Properties.

Keywords

Cultural Property

IoT

Big Data

Deep Neural Network

Convolution Neural Network

최근 IoT 기술의 급격한 발전과 더불어 문화재 관리를 위한 지능형 플랫폼에 대한 이슈가 높아지고 있다. 문화재 관리를 위한 지능형 플랫폼을 개발하기 위해서는 센서를 장착하여 문화재의 내부적, 외부적 상태 데이터를 수집, 분석하고 인공지능 알고리즘을 활용한 빅데이터 분석으로 예측할 수 있다. 본 연구에서는 문화재의 유지관리 및 안전관리 위해 IoT 기술을 기반으로 한 외부적, 내부적 지능형 통합 플랫폼 연구 방안에 대하여 제안한다. 제안하는 지능형 플랫폼에서는 문화재의 외부적 데이터로 기울기 센서 데이터와 외부환경 변화를 고려한 기상청 데이터를 분석, 예측하기 위해 심층신경망(DNN) 학습 알고리즘을 제안하였다. 또한 문화재의 내부적 데이터로는 해충을 감지하기 위한 영상 데이터를 분석, 예측하기 위해 합성곱신경망(CNN) 학습 알고리즘을 제안한다.

키워드

문화재

IoT

빅데이터

심층신경망(DNN)

합성곱신경망(CNN)

References

Wooden Cultural Property Pest Management Manual, the Cultural Heritage Administration (2013.01).
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Biology of Termites: A modern synthesis, springer (2011).
Noh Yeongsuk. (2016). “A Study on Improvement of Disaster Prevention and Management for Cultural Property: With a Focus on Gyeonggi-do,” Legislation and Policy Studies, Vol.8, No.2, pp.117-139.
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Information

Publisher :The Society of Cultural Heritage Disaster Prevention
Publisher(Ko) :문화재방재학회
Journal Title :Journal of the Society of Cultural Heritage Disaster Prevention
Journal Title(Ko) :문화재방재학회논문집
Volume : 6
No :1
Pages :13-20

[1] Wooden Cultural Property Pest Management Manual, the Cultural Heritage Administration (2013.01).

[2] Choi, Jongun, Yu, Myunghan, Yoon, Youngchu, A Study on the Installation and Operation of Cultural Property Monitoring System based on Low Power IoT Sensors, The Society of Cultural Heritage Disaster Prevention, Vol.3 No.2 (2018.12).

[3] Takahashi.M, Current and future aspects of termite-controlling techniques, wood research rev, vol.28 (1992).

[4] Su N.Y, A review of subterranean termite control practices and prospects for integrated pest management programs, Integrated pest Management Reviews, vol.3 (1998).

[5] Takahashi.M, Recent development in the control of Japanese subterranean termites, sociobiology, vol.40 (2002).

[6] Su N.Y, overview of the global distribution and control of the Formosan subterranean termite, sociobiology, vol. 41 (2003).

[7] David Pinniger and Peter Winsor, Integrated pest management - A guide for museums, libraries and archives, MLA (2004).

[8] Biology of Termites: A modern synthesis, springer (2011).

[9] Noh Yeongsuk. (2016). “A Study on Improvement of Disaster Prevention and Management for Cultural Property: With a Focus on Gyeonggi-do,” Legislation and Policy Studies, Vol.8, No.2, pp.117-139.

[10] Vangelista, Lorenzo, Andrea Zanella, and Michele Zorzi. (2015). “Long-range IoT technologies: The dawn of LoRa™.” Future Access Enablers of Ubiquitous and Intelligent Infrastructures. Springer, Cham. 10.1007/978-3-319-27072-2_7

[11] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” In Proceedings of the IEEE, Vol.86, No.11, pp.2278-2324, 1998. 10.1109/5.726791

[12] A. Karpathy, et al., “Large-scale video classification with convolutional neural networks,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1725-1732, 2014. 10.1109/CVPR.2014.223

[13] S. Ji, W. Xu, and M. Yang, “3D convolutional neural networks for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.1, pp.221-231, 2013. 10.1109/TPAMI.2012.59 22392705

[14] A. Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,” Advances in Neural Information Processing Systems, pp.2643-2651, 2013.

[15] X. Shen, B. Yi, Z. Zhang, J. Shu, and H. Liu, “Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network,” International Symposium on Educational Technology (ISET), pp.30-34, 2016. 10.1109/ISET.2016.12

Journal of the Society of Cultural Heritage Disaster Prevention ISSN:2508-2825(Print) 문화재방재학회논문집

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