DOI QR코드

DOI QR Code

머신러닝 기술의 광업 분야 도입을 위한 활용사례 분석

Case Analysis for Introduction of Machine Learning Technology to the Mining Industry

  • 이채영 (부경대학교 에너지자원공학과) ;
  • 김성민 (서울대학교 BK21플러스 기반에너지 지속가능화 인력양성 사업단) ;
  • 최요순 (부경대학교 에너지자원공학과)
  • Lee, Chaeyoung (Dept. of Energy Resources Engineering, Pukyong National University) ;
  • Kim, Sung-Min (Division of Graduate Education for Sustainability of Foundation Energy, Seoul National University) ;
  • Choi, Yosoon (Dept. of Energy Resources Engineering, Pukyong National University)
  • 투고 : 2019.02.07
  • 심사 : 2019.02.16
  • 발행 : 2019.02.28

초록

본 연구에서는 국내 의료, 제조, 금융, 자동차, 도시 분야와 해외 광업 분야에서 머신러닝 기술이 활용된 사례를 조사하였다. 문헌 조사를 통해 머신러닝 기술이 의학영상 정보시스템 개발, 실시간 모니터링 및 이상 진단 시스템 개발, 정보시스템의 보안 수준 개선, 자율주행차 개발, 도시 통합관리 시스템 개발 등에 광범위하게 활용되어왔음을 알 수 있었다. 현재까지 국내 광업 분야에서는 머신러닝 기술의 활용사례를 찾을 수 없었으나, 해외에서는 광상 탐사나 광산 개발의 생산성 및 안전성을 개선을 위해 머신러닝 기술을 도입한 프로젝트들을 찾을 수 있었다. 향후 머신러닝 기술의 광업 분야 도입은 점차 확산될 것으로 예상된다.

This study investigated use cases of machine learning technology in domestic medical, manufacturing, finance, automobile, urban sectors and those in overseas mining industry. Through a literature survey, it was found that the machine learning technology has been widely utilized for developing medical image information system, real-time monitoring and fault diagnosis system, security level of information system, autonomous vehicle and integrated city management system. Until now, the use cases have not found in the domestic mining industry, however, several overseas projects have found that introduce the machine learning technology to the mining industry for improving the productivity and safety of mineral exploration or mine development. In the future, the introduction of the machine learning technology to the mining industry is expected to spread gradually.

키워드

OBGHBQ_2019_v29n1_1_f0001.png 이미지

Fig. 1. Comparison of supervised learning, unsupervised learning and reinforcement learning methods (modified from Jung, 2018)

OBGHBQ_2019_v29n1_1_f0002.png 이미지

Fig. 2. Relationship between artificial intelligence, machine learning and deep learning (modified from NVIDIA, 2016)

OBGHBQ_2019_v29n1_1_f0003.png 이미지

Fig. 3. PACS viewer for supporting medical image analysis (INFINITT, 2018)

OBGHBQ_2019_v29n1_1_f0004.png 이미지

Fig. 4. Overview of the ECMinerTM and ECMinerIMSTM systems (modified from ECMiner, 2017)

OBGHBQ_2019_v29n1_1_f0005.png 이미지

Fig. 5. Smart City System architecture (modified from IFEZ, 2019)

OBGHBQ_2019_v29n1_1_f0006.png 이미지

Fig. 6. Real-time monitoring and analysis of construction and mining sites using vision sensors mounted on equipments and artificial intelligence (EQUIPMENTWORLD, 2017)

OBGHBQ_2019_v29n1_1_f0007.png 이미지

Fig. 7. Process to apply machine leaning in mineral exploration (Goldspot Discoveries, 2018)

OBGHBQ_2019_v29n1_1_f0008.png 이미지

Fig. 8. Ore fragmentation assessment using machine learning – FRAGx (PETRA, 2019)

참고문헌

  1. CC&I RESEARCH, 2019, COPD, ccnires.com/bbs/content.php?co_id=sub7_1, (Accessed at 15 February 2019).
  2. CIM MAGAZINE, 2018, Revving up, magazine.cim.org/en/technology/revving-up-en/, (Accessed at 15 February 2019).
  3. Digital trend, 2017, 커넥티드카(Connected Car)에 관한 쉬운 이해, easydigital.co.kr/?p=626, (Accessed at 15 February 2019).
  4. ECMiner, 2017, 빅데이터 활용을 위한 방법론 및 사례소개, www.softwarecatalog.co.kr/paper/ecminer.pdf, (Accessed at 15 February 2019).
  5. ECMiner, 2018, Blog, ecminer.com/?p=2181, (Accessed at 15 February 2019).
  6. ECMiner, 2019, ECMinerIMS, ecminer.com/?page_id=177, (Accessed at 15 February 2019).
  7. Emerj, 2019, AI in Mining - Mineral Exploration, Autonomous Drills, and More, emerj.com/ai-sector-overviews/ai-in-miningmineral-exploration-autonomous-drills/, (Accessed at 15 February 2019).
  8. EQUIPMENTWORLD, 2017, Komatsu brings artificial intelligence to heavy equipment with NVIDIA-powered cameras, www.equipmentworld.com/komatsu-brings-artificial-intelligence-to-heavy-equipment-with-nvidia-powered-cameras/, (Accessed at 15 February 2019).
  9. EXCELACOM, 2016, The 5 Vs of Big Data: Predictions for 2016, www.excelacom.com/resources/blog/the-5-vs-of-bigdata-predictions-for-2016, (Accessed at 15 February 2019).
  10. Forbes, 2017, NVIDIA And Komatsu Partner on AI-Based Intelligent Equipment For Improved Safety And Efficiency, www.forbes.com/sites/tiriasresearch/2017/12/12/nvidia-and-komatsu-partner-on-ai-based-intelligent-equipment/#63ad3365665b, (Accessed at 15 February 2019).
  11. GATENEWS, 2017, PACS (picture archiving and communication system), searchhealthit.techtarget.com/definition/picturearchiving-and-communication-system-PACS, (Accessed at 15 February 2019).
  12. Goldspot Discoveries, 2018, The application of machine learning in mineral exploration, goldspot.ca/wp-content/uploads/2018/05/goldspot_presentation.pdf, (Accessed at 15 February 2019).
  13. IFEZ, 2019, 스마트시티서비스, http://www.ifez.go.kr/ivt110, (Accessed at 15 February 2019).
  14. INFINITT, 2018, www.mountainsidehosp.com/assets/39/7/Infinitt_-_Clinician_PACS_Guide.pdf, (Accessed at 23 July 2018).
  15. INFINITT, 2019, INFINITT PACS, https://www.infinitt.com/kr/radiology#PACS, (Accessed at 15 February 2019).
  16. Jung, D.H., 2018, Business Strategy in the age of Artificial Intelligence, Thequest, Korea, p.7-81.
  17. KTNexR, 2019, Big Data for Business Action, www.nexr.co.kr/resources/case_view.html?id=76C0296BB7014430B956254A768F9C94, (Accessed at 15 February 2019).
  18. Lim, H. J., 2017, Development direction of fraud detection system technology, J. The Korean Institute of Communication Sciences, 34(3), 37-46.
  19. MEDIGATE, 2017, 의료/산업, www.medigatenews.com/news/2334703587, (Accessed at 15 February 2019).
  20. Mining Magazine, 2016, Machine-learning prediction, www.miningmagazine.com/innovation/opinion/1263928/machine-learningenters-mines, (Accessed at 15 February 2019).
  21. Mining Magazine, 2018, NEWTRAX, www.miningmagazine.com/partners/partner-content/1332132/the-future-of-mining-isunderground, (Accessed at 15 February 2019).
  22. MINING, 2017, Goldcorp partners with IBM to hunt for exploration targets at Red Lake, www.mining.com/goldcorppartners-ibm-hunt-exploration-targets-red-lake/, (Accessed at 15 February 2019).
  23. Miyake, Y and Moricawa Y., 2017, Artificial Intelligence 70, Jpub, Korea, 252p.
  24. Newtrax, 2018, Newtrax Makes the Cover of Mining Magazine's 'The Future of Mining' Issue!, www.newtrax.com/miningmagazine-cover-future-of-mining/, (Accessed at 15 February 2019).
  25. NVIDIA, 2016, What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?, blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/, (Accessed at 15 February 2019).
  26. PETRA, 2017, Algorithms - Mining's crystal ball www.petradatascience.com/casestudy/algorithms-minings-crystal-ball/, (Accessed at 15 February 2019).
  27. PETRA, 2019, Machine learning AI enters underground mines, www.petradatascience.com/casestudy/machine-learning-aienters-underground-mining/, (Accessed at 15 February 2019).
  28. ShinhanCardBlog, 2017, ShinhanCardBlog, www.shinhancardblog.com/461, (Accessed at 15 February 2019).
  29. TechTarget, 2018a, big data analytics, searchbusinessanalytics.techtarget.com/definition/big-data-analytics, (Accessed at 15 February 2019).
  30. TechTarget, 2018b, PACS (picture archiving and communication system), searchhealthit.techtarget.com/definition/picturearchiving-and-communication-system-PACS, (Accessed at 15 February 2019).