A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data
A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data
复制标题
基于云传感器数据的飞机着陆速度预测的新型深度学习方法
DOI：
10.1016/j.future.2018.06.023
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发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Zhigao Zheng
中科院分区：
文献类型：
--
作者：
Chao Tong;Xiang Yin;Shili Wang;Zhigao Zheng
人工智能方法和基于物联网的传感器数据的结合将在各种环境中发挥关键作用。飞机着陆安全问题一直是航空界的研究热点。准确预测着陆速度有利于减少着陆事故。提出了一种基于长短期记忆（LSTM）的飞机着陆速度预测模型。首先，对数据进行统计分析和预处理，包括随机性检验和平稳性检验。其次，采用随机森林算法设计特征，并利用主成分分析对特征进行降维。第三，提出了一种基于长短期记忆的飞机着陆速度预测的深度结构。实验结果表明，与现有的预测模型相比，该模型具有更好的性能和更高的预测精度，表明该模型是准确有效的。研究结果可应用于实际飞行中，以进一步预防着陆事故，提高空中交通管制员的空中管理水平。
The combination of artificial intelligence methods and IoT based sensor data will play a critical and crucial role in various environments. Flight landing safety is a research hotspot of aviation field for a long time. Accurately predicting the landing speed is conducive to reducing the landing accidents. In this paper, we proposed an accurate aircraft landing speed prediction model based on the long-short term memory (LSTM) with flight sensor data. Firstly, we analyze and pre-process the dataset with statistical method including randomness tests and stationary tests. Secondly, we design the features by random forest algorithm and reduce the dimensionality of features with principal component analysis. Thirdly, we develop a deep architecture based on long-short term memory to predict the aircraft landing speed. Experiment results prove that it has better performance with higher prediction accuracy compared with the state of the art, indicating that the proposed model is accurate and effective. The findings are expected to be applied into flight operation practice for further preventing of landing accidents and improving the air management for air traffic controllers.
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影响因子：
6.3
作者：
Wollschlaeger, Martin;Sauter, Thilo;Jasperneite, Juergen
通讯作者：
Jasperneite, Juergen
DOI：
10.1016/j.ress.2014.03.013
发表时间：
2014-07
期刊：
Reliab. Eng. Syst. Saf.
影响因子：
--
作者：
Lei Wang;Changxu Wu;Ruishan Sun
通讯作者：
Lei Wang;Changxu Wu;Ruishan Sun
DOI：
10.1109/apsipa.2014.7041743
发表时间：
2014-12
期刊：
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific
影响因子：
--
作者：
Jiamei Wei;Ercheng Pei;D. Jiang;H. Sahli;Lei Xie;Zhonghua Fu
通讯作者：
Jiamei Wei;Ercheng Pei;D. Jiang;H. Sahli;Lei Xie;Zhonghua Fu
DOI：
10.1016/j.future.2015.07.010
发表时间：
2016
期刊：
Future Gener. Comput. Syst.
影响因子：
--
作者：
Eugenio Cesario;C. Mastroianni;D. Talia
通讯作者：
Eugenio Cesario;C. Mastroianni;D. Talia
DOI：
10.1007/978-1-4419-9326-7_5
发表时间：
2012-01-01
期刊：
ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS
影响因子：
--
作者：
Cutler, Adele;Cutler, D. Richard;Stevens, John R.
通讯作者：
Stevens, John R.