Visualization of Spacecraft Data Based on Interdependency Between Changing Points in Time Series

Author

Sato, Yuichi ; Kawahara, Yoshinobu ; Yairi, Takehisa ; Machida, Kazuo

Author_Institution

Dept. of Aeronaut. & Astronaut., Tokyo Univ.

fYear

2006

Firstpage

3414

Lastpage

3418

Abstract

A support technology for spacecraft operators is one of the important themes for reliable operation. We suggest a framework for visualization of relations among sequences based on "changing points". First, we employ auto-regression model for detecting changing points from data. And next, we apply a structure learning of dynamic Bayesian net to the change-detected data for getting the graph structure, which stands for dependency among sequences. We applied this approach to two kinds of actual telemetry data of a communication satellite, and verified graph structures rightly showed the relation among sequences

Keywords

artificial satellites; belief networks; data visualisation; learning (artificial intelligence); regression analysis; time series; auto-regression model; changing point detection; dynamic Bayesian network; graph structure; spacecraft data visualization; structure learning; time series; Aerodynamics; Artificial satellites; Bayesian methods; Data mining; Data visualization; Downlink; Satellite communication; Space technology; Space vehicles; Telemetry; operators support; visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE-ICASE, 2006. International Joint Conference

Conference_Location

Busan

Print_ISBN

89-950038-4-7

Electronic_ISBN

89-950038-5-5

Type

conf

DOI

10.1109/SICE.2006.315124

Filename

4108350