著者

前川 陽平 錦澤 滋雄 長岡 篤 村山 武彦 竹島 喜芳 安本 晋也

出版者

一般社団法人 日本計画行政学会

雑誌

計画行政 (ISSN:03872513)

巻号頁・発行日

vol.46, no.1, pp.29-36, 2023-02-15 (Released:2023-03-10)

参考文献数

16

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In recent years, there have been conflicts with local residents in Japan over issues such as landscape and disaster risk caused by the construction of solar-PV facilities. In order to promote the suitable installation of solar-PV facilities in the future, it is necessary to clarify resident attitudes toward such facilities, not only in the planning stage but also in operation. In this research, a questionnaire survey was conducted among local residents living near a solar-PV facility in operation (that had had conflicts in the planning stage) at Mt. Tsukuba, in Tsukuba city. In addition, a model of resident attitude formation toward the facility was constructed and its determinants were clarified. As a result, it was found that the felt annoyance due to landscape change had a statistically significant relationship with the negative attitude to the project. Moreover, it was also demonstrated that the installation in Mt. Tsukuba was a significant correlated factor with the felt annoyance due to landscape change. The analysis confirms that it is important to avoid installation in mountainous forests of special value to residents, such as Mt. Tsukuba, in order to improve the acceptance from local communities toward solar-PV projects.

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

3

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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.