Given the national goal of “emission peaking and carbon neutralization”, China has become the largest country in the world for offshore wind farm construction. At the same time, navigational safety problems in offshore wind farm waters have become increasingly frequent. Owing to the complexity of offshore wind farm waters and the small number of accident data samples available for reference, the system theory method is more suitable for selection than the traditional method. Based on causal analysis based on system theory (CAST) and a complex network (CN), in this study, a qualitative and quantitative accident analysis model, CAST-CN, is constructed to analyze a complete case of vessel and wind turbine allision in offshore wind farm waters. The results show that, at the micro level, in addition to the master, crew, shipping company, and typhoon Hato, the maritime safety administration and the wind farm operation management department have a certain impact on the development of the accident discussed in this study. At the macro level, internal and external factors leading to the lack of system safety are identified, and measures and suggestions for system safety improvement are proposed based on analysis. This study can fill the research gap in the systematic analysis of traffic accidents in offshore wind farm waters and provide support for the safety assessment and decision-making of government management departments and research institutes.

The interdependent network is particularly vulnerable to attacks on high degree nodes; therefore, the improvement of its robustness under intentional attacks has become an important topic. In this paper, we put forward a new metric to quantify the robustness of interdependent networks against intentional attacks and develop an improved simulated annealing algorithm (ISAA) to maximize this metric by optimizing the allocation of intra-links in subnetworks. Based on the comparison between the ISAA and existing algorithms, it is found that the algorithm presented in this paper is more effective to enhance the robustness of an interdependent scale-free network (ISFN). By applying the ISAA to ISFNs with different coupling preferences, there is a key finding that the robustness of the optimized ISFN is significantly stronger than that of the original ISFN. In particular, for cases of disassortative and random couplings, no sudden collapse occurs in optimized ISFNs. According to the analysis of the degree and the clustering coefficient, we find that the subnetwork of the optimized ISFN exhibits an onion-like structure. In addition, the ISFN whose robustness is enhanced to resist the attacks on high degree nodes is still robust to the intentional attacks concerning the betweenness and PageRank.

The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46 %, 1.33%, and 0.36% respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%.

Existing works on the interdependent system have come to fruition based on the percolation theory and revealed that it possesses the great vulnerability due to the essential interdependency. However, how to effectively recover the performance of the interdependent system after cascading failures is still under research. In this article, we define an interdependent mechatronic system as an interdependent machine-electricity-communication network. By considering the behavior of the real-world system, we put forward an extended cascading failure model in which the non-giant component is also functional when its size proportion is not smaller than the proportion threshold $\delta $ and it has the interdependent links from the other two subnetworks. Then, according to the measures of a node (i.e., single measures), the interdependent measures are proposed and the repair strategies are obtained by these measures to determine the order of repaired nodes. In order to accurately reflect the resilience in the interdependent mechatronic system, we adopt three metrics to quantify it, i.e., the change of the robustness, the recovery ability, and the critical number of repaired nodes. Finally, we study the relationship between $\delta $ and the robustness, and apply different repair strategies to the analysis of the resilience in a real mechatronic system. The experiments show that the non-giant component plays a key role in the robustness and the resilience is affected by $\delta $ when a few nodes fail to work. In addition, we obtain the optimal repair strategy from different aspects of the resilience. A striking finding is that in most instances, the repair strategies concerning the interdependent measures lead to the higher resilience compared with the ones concerning the single measures. Our work may provide insights to make a plan for repairing equipment so as to enhance the resilience of the interdependent mechatronic system.