Using deep learning approach in flight exceedance event analysis
學年 108
學期 2
出版（發表）日期 2020-07-01
作品名稱 Using deep learning approach in flight exceedance event analysis
作品名稱（其他語言）
著者 H.-J. Shyur; C.-B. Cheng; Y. Hsiao
單位
出版者
著錄名稱、卷期、頁數 Journal of Information Science and Engineering, 37, 1405-1418.
摘要 Causal analysis of flight exceedance events, e.g. hard-landing, is a key task for modern airlines performing Flight Operation Quality Assurance (FOQA) programs. The main objective of the program is to learn from experience: detect early signs of major problems and correct them before accidents occur. It has been found that flare operation would greatly influence the landing performance. According to the finding, we proposed a deep learning approach to assist airlines performing causal analysis for hard landing events. Experimental results confirm that compared with the other stateof-the-art techniques, the proposed approach provides a more reliable results. The technique can be the basis of developing advanced models for further revealing the relationships between pilot operations and flight exceedance events.
關鍵字
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI Scopus
產學合作 國內
通訊作者 H.-J. Shyur
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/119988 )

SDGS 產業創新與基礎設施