Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical bus...Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.展开更多
The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decisi...The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.展开更多
目的:研究一种改进的视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)回环检测方法,以提高算法的定位与建图精度。方法:在ORB-SLAM2回环检测算法基础上,增加图像深度信息利用率,通过词袋模型加速匹配当前帧与每一...目的:研究一种改进的视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)回环检测方法,以提高算法的定位与建图精度。方法:在ORB-SLAM2回环检测算法基础上,增加图像深度信息利用率,通过词袋模型加速匹配当前帧与每一个回环候选帧,分别索引当前帧与该回环候选帧地图匹配点的深度信息,将两者的比值与设定的阈值进行比较,满足阈值条件则证明该回环候选帧为相似场景图像帧,将其剔除。通过相似场景布置,使用Kinect1.0相机采集场景图片,制作相似场景数据集,结合TUM RGBD标准数据集中fr1-room序列对ORB-SLAM2算法和改进算法的性能进行测试,使用evo评价工具比较2种算法的定位精度。结果:在相似场景数据集中,改进算法能够有效剔除错误回环,准确率较高的同时还具有很好的召回率。在TUM RGBD数据集中,改进算法相较于ORBSLAM2算法,均方根误差减小了24.1%,最大误差减小了14.0%。结论:提出的改进算法在相似场景中具有较好的鲁棒性,相较于ORB-SLAM2算法具有更高的定位与建图精度,对于提高搜救机器人的自主行动能力和搜救效率具有重要意义。展开更多
In order to improve the recognition accuracy of similar weather scenarios(SWSs)in terminal area,a recognition model for SWS based on contrastive learning(SWS-CL)is proposed.Firstly,a data augmentation method is design...In order to improve the recognition accuracy of similar weather scenarios(SWSs)in terminal area,a recognition model for SWS based on contrastive learning(SWS-CL)is proposed.Firstly,a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images.Secondly,in the pre-trained recognition model of SWS-CL,a loss function is formulated to minimize the distance between the anchor and positive samples,and maximize the distance between the anchor and the negative samples in the latent space.Finally,the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS.The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset,and the proposed SWS-CL model can achieve satisfactory recognition accuracy.It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.展开更多
基金the National Natural Science Foundation of China(Nos.71731001,61573181,71971114)the Fundamental Research Funds for the Central Universities(No.NS2020045)。
文摘Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.
基金supported by the National Natural Science Foundation of China(No.61501229)the Fundamental Research Funds for the Central Universities(Nos.2019054,2020045)。
文摘The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.
基金supported by the Fundamental Research Funds for the Central Universities(NOS.NS2019054,NS2020045)。
文摘In order to improve the recognition accuracy of similar weather scenarios(SWSs)in terminal area,a recognition model for SWS based on contrastive learning(SWS-CL)is proposed.Firstly,a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images.Secondly,in the pre-trained recognition model of SWS-CL,a loss function is formulated to minimize the distance between the anchor and positive samples,and maximize the distance between the anchor and the negative samples in the latent space.Finally,the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS.The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset,and the proposed SWS-CL model can achieve satisfactory recognition accuracy.It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.
