Aviation Accident Causation Analysis Based on Complex Network Theory

Accident causation analysis is of great importance for accident prevention.In order to improve the aviation safety,a new analysis method of aviation accident causation based on complex network theory is proposed in this paper.Through selecting 257 accident investigation reports,45 causative factors and nine accident types are obtained by the three-level coding process of the grounded theory,and the interaction of these factors is analyzed based on the“2-4”model.Accordingly,the aviation accident causation network is constructed based on complex network theory which has scale-free characteristics and small-world properties,the characteristics of causative factors are analyzed by the topology of the network,and the key causative factors of the accidents are identified by the technique for order of preference by similarity to ideal solution(TOPSIS)method.The comparison results show that the method proposed in this paper has the advantages of independent of expert experience,quantitative analysis of accident causative factors and statistical analysis of a lot of accident data,and it has better applicability and advancement.

Dependency-based importance measures of components in mechatronic systems with complex network theory

To compensate for the limitations of previous studies,a complex network-based method is developed for determining importance measures,which combines the functional roles of the components of a mechatronic system and their topological positions.First,the dependencies among the components are well-represented and well-calculated.Second,a mechatronic system is modeled as a weighted and directional functional dependency network(FDN),in which the node weights are determined by the functional roles of components in the system and their topological positions in the complex network whereas the edge weights are represented by dependency strengths.Third,given that the PageRank algorithm cannot calculate the dependency strengths among components,an improved PageRank importance measure(IPIM)algorithm is proposed,which combines the node weights and edge weights of complex networks.IPIM also considers the importance of neighboring components.Finally,a case study is conducted to investigate the accuracy of the proposed method.Results show that the method can effectively determine the importance measures of components.

RESEARCH ON KEY NODES OF WIRELESS SENSOR NETWORK BASED ON COMPLEX NETWORK THEORY

On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.

Cascading failure analysis of power flow on wind power based on complex network theory

Cascading failure is a potential threat in power systems with the scale development of wind power,especially for the large-scale grid-connected and long distance transmission wind power base in China.This introduces a complex network theory(CNT)for cascading failure analysis considering wind farm integration.A cascading failure power flow analysis model for complex power networks is established with improved network topology principles and methods.The network load and boundary conditions are determined to reflect the operational states of power systems.Three typical network evaluation indicators are used to evaluate the topology characteristics of power network before and after malfunction including connectivity level,global effective performance and percentage of load loss(PLL).The impacts of node removal,grid current tolerance capability,wind power instantaneous penetrations,and wind farm coupling points on the power grid are analyzed based on the IEEE 30 bus system.Through the simulation analysis,the occurrence mechanism and main influence factors of cascading failure are determined.Finally,corresponding defense strategies are proposed to reduce the hazards of cascading failure in power systems.

Multiple scenario simulation and optimization of an urban green infrastructure network based on complex network theory:a case study in Harbin City,China

Background:Urban green infrastructure(GI)networks play a significant role in ensuring regional ecological security;however,they are highly vulnerable to the influence of urban development,and the optimization of GI networks with better connectivity and resilience under different development scenarios has become a practical problem that urgently needs to be solved.Taking Harbin,a megacity in Northeast China,as the case study,we set five simulation scenarios by adjusting the economic growth rate and extracted the GI network in multiple scenarios by integrating the minimal cumulative resistance model and the gravity model.The low‑degree‑first(LDF)strategy of complex network theory was introduced to optimize the GI network,and the optimization effect was verified by robustness analysis.Results:The results showed that in the 5%economic growth scenario,the GI network structure was more complex,and the connectivity of the network was better,while in the other scenarios,the network structure gradually degraded with economic growth.After optimization by the LDF strategy,the average degree of the GI network in multiple scenarios increased from 2.368,2.651,2.189,1.972,and 1.847 to 2.783,3.125,2.643,2.414,and 2.322,respectively,and the GI network structure connectivity and resilience were significantly enhanced in all scenarios.Conclusions:Economic growth did not necessarily lead to degradation of the GI network;there was still room for economic development in the study area,but it was limited under existing GI conditions,and the LDF strategy was an effective method to optimize the GI network.The research results provide a new perspective for the study of GI network protection with urban economic growth and serve as a methodological reference for urban GI network optimization.

GGC:Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train

Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.

Geodesic Vulnerability Approach for Identification of Critical Buses in Power Systems

One of the most critical issues in the evaluation of power systems is the identification of critical buses. For this purpose, this paper proposes a new methodology that evaluates the substitution of the power flow technique by the geodesic vulnerability index to identify critical nodes in power systems.Both methods are applied comparatively to demonstrate the scope of the proposed approach. The applicability of the methodology is illustrated using the IEEE 118-bus test system as a case study. To identify the critical components, a node is initially disconnected, and the performance of the resulting topology is evaluated in the face of simulations for multiple cascading faults. Cascading events are simulated by randomly removing assets on a system that continually changes its structure with the elimination of each component. Thus, the classification of the critical nodes is determined by evaluating the resulting performance of 118 different topologies and calculating the damaged area for each of the disintegration curves of cascading failures. In summary, the feasibility and suitability of complex network theory are justified to identify critical nodes in power systems.