Hanlin Li’s research while affiliated with Nanjing University of Posts and Telecommunications and other places

Maritime route planning under minimal-risk conditions plays an important part in the development and utilization of marine resources. High-resolution weather forecasting data places higher demands on the algorithms’ ability to optimize and compute, and existing algorithms are significantly deficient in these aspects. Therefore, we propose a parallel computing-based planning method, segment parallel A* (SPA*), which splits the path into small segments and runs A* separately on CPU cores through a control algorithm. In segment planning, we propose an adaptive heuristic function on A*. It automatically balances the order of magnitude difference between the risk assessment value and the estimated distance, thus significantly reducing the A* expansion useless grid to improve the performance and running speed of the algorithm. Finally, the complete route is obtained by splicing the above segments. In the static planning experiments, the time of SPA* is reduced by about 5~12,425 times compared with 6 traditional and swarm intelligence-based algorithms, i.e., Dijkstra, A*, bidirectional A* (BA*), ant colony optimization (ACO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA). And the abilities to control the risk caused by wind and waves and the comprehensive risk are improved by 7.68%~25.14% and 8.44%~14.38%, respectively; in the dynamic planning experiments, the above results are 4.8~1262.9 times, 3.87%~9.47% and 7.21%~10.36%, respectively. By setting the recommended range of the number of segments for each case, SPA* shows stable performance in terms of the calculation and risk control. SPA* demonstrates a unique structure for using parallel computing in route planning, which is representative and general in both reducing time and improving efficiency.

With the rise of unmanned combat in naval battles, military powers are paying increasing attention to unmanned underwater platforms. However, there are few studies on the interaction between unmanned underwater platforms and naval battlefield environments, and most of them provide qualitative descriptions, which are of little help in future combat deployment and the development and utilization of marine resources. To solve this problem, we constructed a natural environmental risk index system for unmanned underwater platform operations in a sea area using the marine environmental factor data of the South China Sea region. We used a two-dimensional density-weighted operator to obtain the weights of different impact factors and conducted a risk assessment of the South China Sea region leveraging the cloud barycenter-Bayesian network method. The overall risk probability of unmanned underwater platforms was 14.7%, with a higher risk of 19.0%, a medium risk of 26.0%, a lower risk of 21.2%, and a low risk of 19.1%. Additionally, the risks in the west and north of the South China Sea were larger than those in the south and east. Therefore, the risk in offshore areas was greater than that in the open sea. Consequently, the open sea is more suitable for operating the relevant unmanned underwater platforms. Overall, this study provides technical support for the risk assessment of unmanned underwater vehicles operating in the South China Sea and the development and utilization of marine resources.

It is crucial yet challenging to estimate the parameters of hydrological distribution for hydrological frequency analysis when small samples are available. This paper proposes an improved Bootstrap and combines it with three commonly used parameter estimation methods, i.e., improved Bootstrap with method of moments (IBMOM), maximum likelihood estimation (IBMLE) and maximum entropy principle (IBMEP). A series of numerical experiments with different small sized (10, 20, and 30) of samples generated from the three commonly used probability distributions, i.e., Pearson Type III, Weibull, and Beta distributions, are conducted to evaluate the performance of the proposed three methods compared with the cases of conventional Bootstrap and without-Bootstrap. The proposed methods are then applied to the estimation of distribution parameters for the average annual precipitations of 8 counties in Qingyang City, China with assumption of Pearson Type III distribution for the average annual precipitations. The resulting absolute deviation (AD) box plots and Root Mean Square Error (RMSE) and bias estimators from both the numerical experiments and the case study show that the estimated parameters obtained by the improved Bootstrap methods have less deviation and are more accurate than those obtained through conventional Bootstrap and without-Bootstrap for the three distributions. It is also interestingly found that the improved Bootstrap provides more relative improvement on the parameter estimation when smaller size of sample is used. The method based on improved Bootstrap paves a new way forward to alleviating the need of large size of sample for quality hydrological frequency analysis.

Multivariate hydrological frequency analysis is important when designing hydraulic and civil infrastructures. However, hydrologic data scarcity and insufficiency are common. By studying the relationship between copula entropy and total correlation estimated by the matrix-based Renyi's α-order entropy functional, a new estimation method (total correlation estimation, TCE) for parameters of the Gumbel-Hougaard copula and Clayton copula was proposed when the sample size was equal to or less than 30. A total of 11,802 simulations were performed to evaluate the performance of TCE for sample sizes ranging from 30 to 5, and were compared with traditional estimation methods that require a large amount of data. As for the Gumbel-Hougaard copula, the performance of TCE is satisfactory regardless of sample size, while the traditional methods perform poorly when the sample size is equal to or less than 20. For the Clayton copula, TCE is reliable and robust and performs well if the sample size is greater than 10, while the traditional methods are unreliable when the sample size is less than 25. Also, TCE is applied to construct the joint distributions of annual runoff and sediment discharge in the Xiliugou River, China. The method based on Renyi's α-order entropy functional provides a new way for multivariate hydrological frequency analysis with small sample sizes.