Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments

ガーネット電解質を用いたリチウム金属電池の界面工学に関する研究

京都大学新制・課程博士博士(工学)甲第24632号工博第5138号新制||工||1982(附属図書館)京都大学大学院工学研究科物質エネルギー化学専攻(主査)教授 安部 武志, 教授 作花 哲夫, 教授 陰山 洋学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Vibrational spectra and structure of ZrF4-FeF3-PbF2-YF3 fluoride glasses

Results of the investigation of infrared and Raman spectra in heavy metal fluoride glasses based on the ZrF4-FeF3-PbF2-YF3 system are presented. Infrared and Raman bands in the spectra of these glasses are discussed. The coordination environments of both Zr4+ and Fe3+ ions have been extracted from the vibrational modes. Α structural model for these glasses is proposed. It is composed of the basic network formers ZrF7 polyhedra and FeF6 octahedra, which are randomly linked by corners and/or edges to construct a disordered three-dimensional network, whereas the Pb2+ and Y3+ ions are assumed to play the role of network modifier and stabilizer, respectively