Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
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Case Analysis for Introduction of Machine Learning Technology to the Mining Industry

머신러닝 기술의 광업 분야 도입을 위한 활용사례 분석

  • 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)
  • 이채영 (부경대학교 에너지자원공학과) ;
  • 김성민 (서울대학교 BK21플러스 기반에너지 지속가능화 인력양성 사업단) ;
  • 최요순 (부경대학교 에너지자원공학과)
  • Received : 2019.02.07
  • Accepted : 2019.02.16
  • Published : 2019.02.28
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Abstract

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.

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.

Keywords

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

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

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

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

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

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

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

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

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