Vulnerability analysis of the Chinese coupled aviation and high-speed railway network

The integrated aviation and High-Speed Railway (HSR) transportation system plays a vital role for today’s inter-city transportation services. However, an increasing number of unexpected disruptions (such as operation failures, natural disasters, or intentional attacks) pose a considerable threat to the normal operation of the system, especially on ground transfer, leading to the extensive research on its vulnerability. Previous approaches mainly focus on interruptions within a single transportation mode, neglecting the role of ground transfer which serves as a coupled connection between aviation and High-Speed Railway. This paper proposes a network-based framework for evaluating the vulnerability of the Chinese Coupled Aviation and High-Speed Railway (CAHSR) network from the viewpoint of ground transfer interruption. Taking the end-to-end travel time and passenger flow information into consideration as an evaluation measure and analyzing from the perspective of urban agglomerations, an adaptive method is developed to identify the critical cities and further investigate their failure impacts on the geographic distribution of vulnerability. In addition, the proposed model explores variations of vulnerability under different failure time intervals. Based on the empirical study, some major conclusions are highlighted as follows: (A) Only a few cities show significant impacts on the network’s vulnerability when ground transfer interruptions occurred. (B) The distribution of vulnerability is not proportional to the distance between failure city and influenced city. (C) The vulnerability is more serious in the morning and evening when the ground transfer is disconnected. Our findings may provide new insights for maintenance and optimization of the CAHSR network and other real-world transportation networks.     

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M...     

Formation control for networked multiagent systems with a minimum energy constraint

Minimum-energy formation achievement problems for networked multiagent systems are investigated, where information networks with leaderless and leader-follower structures are respectively addressed and information networks are randomly switching. The critical feature of this work is that the energy constraint is minimum in the sense of the linear matrix inequality, but limited-budget control and guaranteed-cost control cannot realize a minimum-energy formation. Firstly, the leaderless minimum-energy formation control problem is converted into an asymptotic stability one via a nonsingular transformation and state space decomposition, and based on linear matrix inequality techniques, sufficient conditions for analysis and design of leaderless minimum-energy formation achievement are proposed, respectively, which can be solved by the generalized eigenvalue method. Then, main results of minimum-energy formation achievement of leaderless networked multiagent systems are extended leader-follower networked multiagent systems, where the asymmetric property of the leader-follower information network is well dealt with by two nonsingular transformations. Finally, two simulation examples are shown to verify the main results for minimum-energy formation achievements of leaderless and leader-follower networked multiagent systems, respectively.     

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.