With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation...With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.展开更多
Complex networks are everywhere. A typical example is software network. How to measure and control coupling interactions of software components is a largely explored research problem in software network. In terms of g...Complex networks are everywhere. A typical example is software network. How to measure and control coupling interactions of software components is a largely explored research problem in software network. In terms of graph theory and linear algebra, this paper investigates a pair of coupling metrics to evaluate coupling interactions between the classes of object-oriented systems. These metrics differ from the majority of existing metrics in three aspects： Taking into account the strength that one class depends on other ones, reflecting indirect coupling, and distinguishing various coupling interaction. An empirical comparison of the novel measures with one of the most widely used coupling metrics is described. Specifically, an experiment about the relationships of this pair metrics is conducted. The result shows that software complexity derived from coupling interaction could not be accurately reflected by one dimension of coupling metric for negative correlation.展开更多
基金supported by Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20200710).
文摘With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.
基金This research is supported by the National Key Basic Research and Development 973 Program of China under Grant No. 2007CB310805, Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant No. 2007B4, the National Natural Science Foundation of China under Grant Nos. 60873083, 60803025, and the National High Technology Research and Development Program of China under Grant No. 2006AA04Z156.
文摘Complex networks are everywhere. A typical example is software network. How to measure and control coupling interactions of software components is a largely explored research problem in software network. In terms of graph theory and linear algebra, this paper investigates a pair of coupling metrics to evaluate coupling interactions between the classes of object-oriented systems. These metrics differ from the majority of existing metrics in three aspects： Taking into account the strength that one class depends on other ones, reflecting indirect coupling, and distinguishing various coupling interaction. An empirical comparison of the novel measures with one of the most widely used coupling metrics is described. Specifically, an experiment about the relationships of this pair metrics is conducted. The result shows that software complexity derived from coupling interaction could not be accurately reflected by one dimension of coupling metric for negative correlation.
