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5th International Conference on Transportation Information and safety, ICTIS 2019

作者： Zhuang, Zibo Dai, Zheng Ling, Ruohong Chen, Xing Xing, Zhiwei Song, Delong College of Flight Technology Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China Tianjin Key Laboratory of Air Traffic Management Operation Planning and Safety Technology Civil Aviation University of China Tianjin China

ISBN: (纸本)9781728104898

Low-altitude wind shear has become a serious factor impacting for taking off and landing, which has characteristics like sudden appearance, short duration, small scale and high intensity. So, the actively research for detecting and distinguishing it is very important for the aviation safety. In this paper, a gradient detection and four-neighbor labeling algorithm is used to segment the wind shear of the lidar scanning, and the parameters such as the area and azimuth of each shear region are obtained,and then the second segmentation is performed according to the se parameters. Finally, based on PCA (principal component analysis) theory, the segmented area is identified by the type of downburst. The results show that the correct recognition rate and rejection rate can reach 96%, which has good robustness. © 2019 IEEE.

关键词： Principal component analysis

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Automated early warning for ATM safety risks based on fuzzy reasoning

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Information technology Journal 2013年第17期12卷 4188-4191页

作者： Heng, Xiang Man, Liang Sino-European Institution of Aviation Engineering Civil Aviation University of China Tianjin 300300 China Tianj in Key Laboratory of Operation Programming and Safety Technology of Air Traffic Management Civil Aviation University of China Tianjin 300300 China

The establishment of an automated risk early warning mechanism closely linked to the air traffic flow management system and information management system plays a positive role in ATM risk management. In this study, a fuzzy reasoning-based approach was proposed to effectively extract the key ATM risk factors for establishing a rapid early warning mechanism. The fuzzy IF-THEN rules were used to build up a fuzzy rule base on the base of experts' knowledge and then through a multi-input fuzzy logic, the different kinds of the unsafe models were ranked. Finally, an extraction of key attributes for ATM safety risks early warning was realized. The result is that the most important attribute is weather conditions, followed by controller's ability in handling large flows and the influences from military activities rank in the third place. The use of this approach is an exploration in the intelligent warning technology for ATM safety risks. © 2013 Asian Network for Scientific Information.

关键词： Extraction

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