A proposal of extended function-structure analysis method based on local information structuring

2021 ◽  
Vol 2021 (0) ◽  
pp. 401
Author(s):  
Jumpei Kuboyama ◽  
Hidenori Murata ◽  
Hideki Kobayashi
2017 ◽  
Vol 20 (2) ◽  
pp. 1387-1399 ◽  
Author(s):  
Tie-Jun Cui ◽  
Pei-Zhuang Wang ◽  
Sha-Sha Li

2008 ◽  
Vol 35 (7Part1) ◽  
pp. 3259-3277 ◽  
Author(s):  
Wenli Cai ◽  
Michael E. Zalis ◽  
Janne Näppi ◽  
Gordon J. Harris ◽  
Hiroyuki Yoshida

2015 ◽  
Vol 27 (1) ◽  
pp. 241-248 ◽  
Author(s):  
B. C. C. Khoo ◽  
J. R. Lewis ◽  
K. Brown ◽  
R. L. Prince

2009 ◽  
Vol 03 (03) ◽  
pp. 121-141 ◽  
Author(s):  
MUNEO HORI ◽  
KENJI OGUNI ◽  
TSUYOSHI ICHIMURA

This paper presents the integrated simulation for earthquake hazard and disaster prediction. The earthquake hazard simulation takes advantage of the macro-micro analysis method which estimates strong ground motion with high spatial and temporal resolution. The earthquake disaster simulation calculates seismic responses for all structures in a target area by inputting synthesized strong ground motion to a structure analysis method which is plugged into the system; a suitable analysis method, linear or non-linear, is chosen depending on the type of the structure. The results of all simulations are visualized so that government officials and residents can share common recognition of possible earthquake hazard and disaster. Two examples of this integrated earthquake simulations are presented; one is made by plugging nonlinear structure analysis methods into the system, and the other is made for an actual city, the computer model of which is constructed with the help of available geographical information systems.


2021 ◽  
Vol 267 ◽  
pp. 01054
Author(s):  
Weizheng Kong ◽  
Yaohua Wang ◽  
Hongcai Dai ◽  
Liujun Zhao ◽  
Chunming Wang

In order to solve the problem of huge and messy data in the process of analyzing energy consumption structure in different regions, an energy consumption structure analysis method based on K-means clustering algorithm is proposed, and the elbow method and contour coefficient method are used to analyze the data in Qinghai Province. The consumption structure was analyzed and the algorithm was verified. The results show that the algorithm can efficiently and quickly perform data mining and clustering based on local economic and environmental characteristics, which greatly improve the convenience of energy consumption structure analysis.


2020 ◽  
Vol 2020 (0) ◽  
pp. 304
Author(s):  
Yuichi KITAURA ◽  
Hidenori MURATA ◽  
Shinichi FUKUSHIGE ◽  
Hideki KOBAYASHI

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