首页|Civil Aviation Flight University of China Researchers Update Knowledge of Support Vector Machines (Modeling and detection of lowaltitude flight conflict network based on SVM)
Civil Aviation Flight University of China Researchers Update Knowledge of Support Vector Machines (Modeling and detection of lowaltitude flight conflict network based on SVM)
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Investigators discuss new findings in . According to news reporting originating from Sichuan, People’s Republic of China, by NewsRx correspondents, research stated, “With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention.” Our news editors obtained a quote from the research from Civil Aviation Flight University of China: “Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping.”
Civil Aviation Flight University of ChinaSichuanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
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