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學年
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110 |
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學期
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1 |
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出版（發表）日期
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2021-11-01 |
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作品名稱
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Using Deep Learning Approach in Flight Exceedance Event Analysis |
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作品名稱（其他語言）
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著者
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HUAN-JYH SHYUR; CHI-BIN CHENG; AND YU-LIN HSIAO |
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單位
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出版者
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著錄名稱、卷期、頁數
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Journal of Information Science and Engineering 37(6), p.1405-1418 |
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摘要
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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. |
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關鍵字
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Hard Landing; Quick Access Recorder; Deep Learning; Bidirectional Long Short-Term Memory |
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語言
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en_US |
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ISSN
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1016-2364 |
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期刊性質
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國外 |
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收錄於
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SCI
Scopus
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產學合作
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國內
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通訊作者
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Huan-Jyh Shyur |
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審稿制度
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否 |
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國別
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TWN |
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公開徵稿
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出版型式
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,紙本 |