Introduction of our studies so far
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In Japanese version(日本語ページ)

This study proposed the disaster warning map using the keywords of premonitory symptoms from SNS.

This study proposed about the Big Data analysis method for fashion coordination data.
This study focuses on the patterns of similarities that are found between coordinated fashion items, so as to define a new set of score value rows for calculating similarity indexes from the patterns. These similarity indexes are then inverted into distances, based on which the clustering method proposed in this study is achieved.
The each clusters in the above figure are composed simirar fashion coordination nodes.

本研究では,①地震がいつどこで発生しそうで,②地震が発生した場合にその場所はどの程度揺れそうなのか,③揺れた場合にその場所で火災・倒壊などの災害はどの程度発生しそうなのか,の3つを総合的にハザード評価する係数(ODGC-Hazard Coefficient)を開発した.
さらに,ODGC-HCを日本地図にヒートマップとして可視化するODGC-Hazard Map (ODGC-HM)を開発した.
We proposed a hazard coefficient. This is called "ODGC Hazard coefficient". This coefficient is comprehensive hazard assessment: ①When and where the earthquake will likely to occur (Occurrence probability of earthquake)? ② When the earthquake occurs, what levels of seismic intensity are anticipated (Ground conditions)? ③ What levels of disaster severity for fire/collapse due to the earthquake are anticipated (Density of wooden houses)? In addition, we proposed the ODGC Hazard Map, a heat map of Japan based on ODGC-HC.

We proposed a new index to evaluate research contribution in a way that qualitatively improves on the addition, we proposed citation map like above figure (Each node are the academic article, each edge are the cited relationship in the right side of the screen).

This study proposed the clustering method to social setwork sipartite graph.The above figure is the image of similar regions analyzed by this clustering method.

This study proposed the method of extraction of key papers from graph network of an academic an academic article.

 Copyright © 2013 Akira Otsuki. All rights reserved.

inserted by FC2 system inserted by FC2 system