Deep Learning and Internet of Things (IoT) Based Monitoring System for Miners


Cetinkaya T. S., Senan S., ORMAN Z.

JOURNAL OF MINING SCIENCE, vol.58, no.2, pp.325-337, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 58 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1134/s1062739122020156
  • Journal Name: JOURNAL OF MINING SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.325-337
  • Keywords: Internet of Things (IoT), miner monitoring, artificial neural networks, deep learning, LSTM model, PRE-ALARM SYSTEM, NEURAL-NETWORKS, TAILINGS DAM, MODEL, PREDICTION, COAL
  • Istanbul University Affiliated: No

Abstract

In this study, a miner monitoring system is designed using the Deep Learning (DL) approach and the IoT technology together. It is aimed to determine the area where the miners are located while a possible accident occurs by the proposed system. Experiments were carried out to analyze the effectiveness of the proposed system and the performance evaluations were made. The best result was obtained with an accuracy rate of 97.14%. This rate indicates that the designed miner monitoring system can be used effectively in practice.