Using Deep Learning Approach in Flight Exceedance Event Analysis
學年 110
學期 1
出版（發表）日期 2021-11-01
作品名稱 Using Deep Learning Approach in Flight Exceedance Event Analysis
作品名稱（其他語言）
著者 HUAN-JYH SHYUR; CHI-BIN CHENG; AND YU-LIN HSIAO
單位
出版者
著錄名稱、卷期、頁數 Journal of Information Science and Engineering 37(6), p.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 state-of-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 Hard Landing; Quick Access Recorder; Deep Learning; Bidirectional Long Short-Term Memory
語言 en_US
ISSN 1016-2364
期刊性質 國外
收錄於 SCI Scopus
產學合作 國內
通訊作者 Huan-Jyh Shyur
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,紙本
SDGS 產業創新與基礎設施,負責任的消費與生產