An Improved FN Algorithm for Community Division of Air Route Network

Community division is an important method to study the characteristics of complex networks.The widely used fast-Newman(FN)algorithm only considers the topology division of the network at the static layer,and dynamic traffic flow demand is ignored.The result of the division is only structurally optimal.To improve the accuracy of community division,based on the static topology of air route network,the concept of network traffic contribution degree is put forward.The concept of operational research is introduced to optimize the network adjacency matrix to form an improved community division algorithm.The air route network in East China is selected as the object of algorithm comparison experiment,including 352 waypoints and 928 segments.The results show that the improved algorithm has a more ideal effect on the division of the community structure.The proportion of the number of nodes included in the large community has increased by 21.3%,and the modularity value has increased from 0.756 to 0.806,in which the modularity value is in the range of[-0.5,1).The research results can provide theoretical and technical support for the optimization of flight schedules and the rational use of air route resources.

The Design of Small Micro-UAV Formation of ＂Sub-mother＂ type

This paper aims to provide a design about a kind of low-cost ＂sub-mother＂ UAV small formation by using the low-cost micro-UAV as the platform. Combined with the current international situation in the UAV formation research, the modeling is based on the distributed control method and the behavior-based strategy. In this paper, some research to a few key issues has been carried out.

Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Autoencoder and Its Application in Trajectory Clustering

Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis.Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control(ATC)actions.However,the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging.To alleviate these two challenges,this paper proposes a novel framework which combines robust deep auto-encoder(RDAE)model and density peak(DP)clustering algorithm.Specifically,the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations.Then,the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows.An experiment on a terminal airspace at Guangzhou Baiyun Airport(ZGGG)with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement.The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.

A Novel Aircraft Air Conditioning System with a Sterilization Unit by Ultra-High-Temperature Air Stream

An aircraft cabin is a narrow,closed-space environment.To keep the air quality in cabin healthy for passengers,especially during an epidemic such as SARS-CoV-2(or 2019-nCoV)in 2020,a novel aircraft air conditioning system,called the ultra-high-temperature instantaneous sterilization air conditioning system(UHTACS),is proposed.Based on the proposed system,a simulation of the UHT-ACS is analysed in various flight states.In the UHT-ACS,the mixing air temperature of return and bleed air can reach temperature up to 148.8°C,which is high enough to kill bacilli and viruses in 2一8 s.The supply air temperature of the UHT-ACS in a mixing cavity is about 12 C in cooling mode both on the ground and in the air.The supply air temperature is about 42 C in heating mode.Compared with the air conditioning systems(ACS)of traditional aircraft the supply air temperatures of the UHT-ACS in the mixing cavity are in good agreement with those of a traditional ACS with 60%fresh air and 40%return air.Furthermore the air temperature at the turbine outlet of the UHT-ACS is higher than that of a traditional ACS which will help to reduce the risk of icing at the outlet.Therefore the UHT-ACS can operate normally in various flight states.

Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

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.