A disaster warning model for a spacecraft launch based on information fusion and network inference

Abstract

The launch of spacecraft should synthesise various incomplete and uncertain symptoms of extreme events to dynamically assess the possibility of disaster during the launch readiness process. However, there are still many technical problems in disaster assessment of spacecraft launch engineering. For example, the problem of how to effectively use the disaster symptoms is not addressed well yet and the fact that group decision-making can be easily lead to the minority opinion being ignored. Based on the extreme event symptom obtaining technique, a disaster warning model combining both virtual and real evidence is built through information fusion and network inference, using evidence theory and Bayesian theory. Using information fusion, the proposed model shows good performance with regard to complementary evidence, A disaster warning model for a spacecraft launch 143 elimination of data redundancy and reduction of uncertainty. Using symptom information-based network inference, this model guarantees the identification of the correct minority. Finally, the effectiveness, correctness and feasibility of the model are verified by analysis of the 'Challenger' space shuttle explosion.Keywords: risk assessment management; disaster warning model; spacecraft launch engineering; information fusion; network inference; extreme events symptoms.Reference to this paper should be made as follows: Xuejun, D., Yingwu, C., Jianwei, M. and Ming, L. (2012) 'A disaster warning model for a spacecraft launch based on information fusion and network inference', Int.

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

“…The use of various features extracted from images leads to uncertainty related to human knowledge. Another area where evidence theory finds application is in the safety assessment of spaceship landing sites [38]. The method combines information from several sensors to estimate the safety degree of landing site candidates while managing uncertainty.…”