Zilong Guo’s research while affiliated with National University of Defense Technology and other places

Since the development of unmanned systems, autonomous underwater unmanned platforms have become an important facility for countries to explore the ocean field which play an important role in both military and civilian. In this paper, the route planning of underwater unmanned platform is carried out from three constraints: the risk that affects the operational effectiveness of the unmanned underwater platform, the navigation resistance and the navigation path length. The simulation results show that the model can deal with three constraints reasonably and plan a suitable route.

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.

Because of its special location and structure, the Western Pacific Subtropical High (WPSH) influences greatly the climate and weather in East Asia, especially the summer precipitation. To clarify how the interannual variability (IAV) of the WPSH is related to anomalies in the tropical sea surface temperature (SST) and atmospheric circulation, time series of the intensity index of the WPSH are subjected to wavelet analysis, showing IAV in the index. Characteristic indexes are defined for three key sea areas and the equatorial-latitude westerly region. After a continuous wavelet transform, the oscillation period of them is similar to that of the WPSH. The cross-wavelet transform of the four regional and two WPSH indexes is used to obtain the corresponding time-delay correlation. Regarding the potential correlation, WPSH weakening leads to strengthening of the westerly wind and then affects the rise of SST in the eastern equatorial Pacific Ocean. At the same time, warm water moves eastward. This gradually increases the SST in the equatorial central Pacific and warm pool area and then strengthens the WPSH under the action of the Hadley circulation. From the above analysis, a model for predicting the IAV of the WPSH intensity index is established based on the information diffusion model improved by a genetic algorithm. An experiment is conducted to predict the IAV of the WPSH intensity index, and the results show that the prediction model is accurate in predicting the IAV trend, with good prediction for 84 months. The mean absolute percentage error is 14.44% and the correlation coefficient is 0.8507. Also, the normal and abnormal years of the WPSH are used as different starting points for different prediction experiments. However, the different starting points have little influence on the predictions, showing the stability of the model. Studying the IAV of the WPSH provides a strong theoretical and scientific basis for predicting its abnormal interannual behavior and offers the prospect of socially important disaster prevention and mitigation.

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.

Internal solitary waves (ISWs) with features such as large amplitude, short period, and fast speed have great influence on underwater thermohaline structure, nutrient transport, and acoustic signal propagation. The characteristics of ISWs in hotspot areas have been revealed by satellite images combined with mooring observation. However, the ISWs in the Timor Sea, which is located in the outflow of the ITF, have not been studied yet and the characteristics are unrevealed. In this study, by employing the Synthetic Aperture Radar (SAR) images taken by the Sentinel-1 satellite from 2017 to 2022, the temporal and spatial distribution characteristics of ISWs in the Timor Sea are analyzed. The results show that most of ISWs appear in Bonaparte basin and its vicinity. The average wavelength of the ISWs is 248 m, and most of the wave lengths are less than 400 m. The peak line of ISWs is longer in deeper water. The underwater structures of two typical ISWs are reconstructed based on the Korteweg–de Vries (KdV) equation combined with mooring observation. This shows that, compared with the two-layer model, the continuous layered model is more suitable for reconstructing the underwater structures of ISWs. Further analysis shows that both the rough topography and the spring-neap tides contribute to the generation of ISWs in the Timor Sea. This study fills a gap in knowledge of ISWs in regional seas, such as the Timor Sea.