| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2020: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2019: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2018: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
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| Outline of Final Research Achievements |
ML-based regression may result in a rather low R2 score when the target objects are not uniformly modelled. We proposed a method to improve the regression R2 score. It uses itemset mining for segmenting the data set, and applies ML to each segment to obtain a regression function with a higher R2 score. We extended our exploratory visual analytics framework by exploiting this method. Its application to the experimental data set of ferromagnets improved the R2 of the regression estimation of the Curie temperature. For another application to the urban-scale CPS, we exploited LPWA, and developed a trajectory reporting node. At every minute, it reports its last 1 minute trajectory with 10 sample locations, each within 8m error. This is guaranteed at any speed up to 125 km/h. We developed another node for the fixed-point measurement of snow accumulation. These nodes’ data are used with the probe car data for the exploratory visual analysis to improve snow removing operations.
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