著者

岡田 夏男 前川 陽平 大和田 済熙 芳賀 一寿 柴山 敦 川村 洋平

出版者

一般社団法人 資源・素材学会

雑誌

Journal of MMIJ (ISSN:18816118)

巻号頁・発行日

vol.137, no.1, pp.1-9, 2021-01-31 (Released:2021-01-29)

参考文献数

10

被引用文献数

2

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Currently, there have been issues concerning the depletion and scarcity of mineral resources. This is mostly due to the excavation of high grade minerals having already occurred years and years ago, hence forcing the mining industry to opt for the production and optimization of lower grade minerals. This however brings about a plethora of problems, many of which economic, stemming from the purification of those low grade minerals in various stages required for mineral processing. In order to reduce costs and aid in the optimization of the mining stream, this study, introduces an automatic mineral identification system which combines the predictive abilities of deep learning with the excellent resolution of hyperspectral imaging, for pre-stage of mineral processing. These technologies were used to identify and classify high grade arsenic (As) bearing minerals from their low grade mineral counterparts non-destructively. Most of this ability to perform such tasks comes from the highly versatile machine learning model which employs deep learning as a means to classify minerals for mineral processing. Experimental results supported this statement as the model was able to achieve an over 90% accuracy in the prediction of As-bearing minerals, hence, one could conclude that this system has the potential to be employed in the mining industry as it achieves modern day system requirements such as high accuracy, speed, economic, userfriendly and automatic mineral identification.

著者

伊藤 豊 竹内 誠人 見上 柊人 川村 洋平

出版者

一般社団法人 資源・素材学会

雑誌

Journal of MMIJ (ISSN:18816118)

巻号頁・発行日

vol.136, no.5, pp.33-39, 2020-05-31 (Released:2020-05-27)

参考文献数

34

被引用文献数

1

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Productivity, safety and its improvement is also an integral part of a good mining operation. In recent times, due to constraints on time and cost, it has become increasingly harder to conduct training and safety inductions at mine sites. For the purpose of overcoming these limitations, the use of virtual reality (VR) is proposed for mining education and training. VR has already been introduced in the education and training of miners overseas, and quantitative studies on the effects of using VR for miner's education and training have been made. However, Japan has only one such application of VR for mining education, namely, “Virtual Mining Practice System” which was produced by Akita University, and there are relatively few cases where VR has been introduced in the Japanese mining industry. Furthermore, there has been no quantitative study to date on the effects of education using VR for mining education. Therefore, the objective of this study is to investigate the effects of a class that utilizes a VR application developed for mining education (Mining VR), as well as evaluate its learning outcomes. In this study, a method called randomized controlled trial (RCT) is used for evaluating Mining VR's effectiveness. Study participants are divided two groups randomly where one class makes use of Mining VR and another class using other non-VR material. After the classes are completed, a test is conducted and the average results of each group are compared by T-test. The results of this experiment showed that there were no statistically significant differences in skill of “understanding” and “knowledge retention” comparing two groups. On the other hand, results suggested that Mining VR has improved students'“ motivation” for class when using Mining VR.