Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

Abstract

With the development of aviation, the air traffic density in the terminal area is high and the traffic situation is relatively complex, which brings challenges to the flight deployment. In order to fully understand the air flight situation and provide decision-making basis for controllers, this paper proposes a key conflict aircraft identification method based on complex network theory and node deletion method. First, an aircraft state network is constructed with an aircraft as nodes and airborne collision avoidance system (ACAS) communication relations as edges. Network efficiency, network robustness, connection density, and largest component were used as the indexes of network performance. The weight of each index is determined by using AHP-entropy weight method. A multi-attribute decision-making method was introduced to quantify network performance. Then we used a node deletion method to determine key conflict aircrafts. The simulation and experiment are respectively carried out on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport. The results show that the method proposed in this paper can identify the key conflict points in the aircraft state network. The deployment of selected nodes can not only effectively reduce the complexity of the flight state network, but also provide a reference for air traffic control services and reduce the control difficulty of the controller. INDEX TERMS Aircraft state network, complex network, node deletion, air traffic control (ATC).

Assessment of safety risk in airline operations based on constant-sum game

“…There are also preconditions of mutual opposition and the ultimate goal of win-win cooperation in the weights distribution of evaluation indicators. [25][26][27] In a constant-sum game, also called a non-zero sum game, the sum of all players' payoffs is a non-zero constant. Similarly, the sum of the proportions between the subjective and objective weights is always 1.…”

“…Similarly, the sum of the proportions between the subjective and objective weights is always 1. 26,27 In this paper, the calculation methods of subjective and objective weights are set as the two sides of the game. The weight vectors to be optimized obtained by each are used as the strategy set of the two sides of the game.…”

SEE Fault Sensitivity Analysis and Security Reinforcement Design for FPGA Circuits Based on Complex Network

“…A network can be viewed as a graph consisting of nodes connected by edges according to a certain rule or form, so the nodes and edges do not necessarily have physical meanings in the graphs. Complex network analysis has been applied to many fields for key node identification and efficiency analysis, such as wireless communication system [16]- [18], aviation transportation system [19], [20], community network [21], underwater acoustic sensor network [22], urban transportation system [23], [24], power transmission system [25], [26], etc. Complex network analysis models have been proposed for different systems, and structural characteristics are studied based on these models, such as node degree, betweenness centrality, and clustering coefficient.…”