Using deep learning approach in flight exceedance event analysis

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 期刊論文 > Item 987654321/119988

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题名:	Using deep learning approach in flight exceedance event analysis
作者:	Shyur, H.-J.;Cheng, C.-B.;Hsiao, Y.
关键词:	hard landing;quick access recorder;deep learning;BLSTM;RNN
日期:	2020-07
上传时间:	2021-03-03 12:11:44 (UTC+8)
出版者:	中華民國計算語言學學會
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.
關聯:	Journal of Information Science and Engineering 37(6), p.1405-1418
显示于类别:	[資訊管理學系暨研究所] 期刊論文

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