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

Chaeyoung Lee; Sung-Min Kim; Yosoon Choi

doi:10.7474/TUS.2019.29.1.001

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2019 Vol.29, Issue 1 Next Page

Technical Note

Case Analysis for Introduction of Machine Learning Technology to the Mining Industry 머신러닝 기술의 광업 분야 도입을 위한 활용사례 분석

28 February 2019. pp. 1-11

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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.

Keywords

Machine learning

Mineral exploration

Mine development

Productivity

Safety

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.

키워드

머신러닝

광상 탐사

광산 개발

생산성

안전성

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Information

Publisher :Korean Society for Rock Mechanics and Rock Engineering
Publisher(Ko) :한국암반공학회
Journal Title :Tunnel and Underground Space
Journal Title(Ko) :터널과 지하공간
Volume : 29
No :1
Pages :1-11
Received Date : 2019-02-07
Revised Date : 2019-02-14
Accepted Date : 2019-02-16
DOI :https://doi.org/10.7474/TUS.2019.29.1.001

Tunnel and Underground Space ISSN:1225-1275(Print) 2287-1748(Online) 터널과 지하공간

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