Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships,a high-frequency method by using graphics processing unit(GPU)parallel acceleration...Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships,a high-frequency method by using graphics processing unit(GPU)parallel acceleration technique is proposed.For the implementation of different electromagnetic methods of physical optics(PO),shooting and bouncing ray(SBR),and physical theory of diffraction(PTD),a parallel computing scheme based on the CPU-GPU parallel computing scheme is realized to balance computing tasks.Finally,a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing process.By using the established simulation platform,signals of ships at different seas are simulated and their images are achieved as well.It is shown that the higher sea states degrade the averaged peak signal-to-noise ratio(PSNR)of radar image.展开更多
为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of a...为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。展开更多
为在保证货主满意度的同时尽可能地控制航运成本,在考虑北极东北航线气象条件和自愿速度损失的基础上,建立北极东北航线航速优化模型,通过带精英策略的非支配排序遗传算法(elitist non-dominated sorting genetic algorithm,NSGA-Ⅱ)和...为在保证货主满意度的同时尽可能地控制航运成本,在考虑北极东北航线气象条件和自愿速度损失的基础上,建立北极东北航线航速优化模型,通过带精英策略的非支配排序遗传算法(elitist non-dominated sorting genetic algorithm,NSGA-Ⅱ)和逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)筛选出最佳折中解。选取北极东北航线沿线重要港口进行案例分析,结果表明:当航运成本降低时,货主总体满意度会下降,船公司可以根据各货主的重要性调整航速,以平衡航运成本和货主满意度;对比考虑自愿速度损失与无自愿速度损失的北极东北航线航速优化模型发现,两者的优化结果存在显著差异,自愿速度损失直接影响航速优化模型的准确性;航速优化对时间窗较为敏感,较大的时间窗可以为减速和提高船舶能效提供更大的机会空间。展开更多
基金supported by the Opening Foundation of the Agile and Intelligence Computing Key Laboratory of Sichuan Province under Grant No.H23004the Chengdu Municipal Science and Technology Bureau Technological Innovation R&D Project(Key Project)under Grant No.2024-YF08-00106-GX.
文摘Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships,a high-frequency method by using graphics processing unit(GPU)parallel acceleration technique is proposed.For the implementation of different electromagnetic methods of physical optics(PO),shooting and bouncing ray(SBR),and physical theory of diffraction(PTD),a parallel computing scheme based on the CPU-GPU parallel computing scheme is realized to balance computing tasks.Finally,a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing process.By using the established simulation platform,signals of ships at different seas are simulated and their images are achieved as well.It is shown that the higher sea states degrade the averaged peak signal-to-noise ratio(PSNR)of radar image.
文摘为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。
文摘为在保证货主满意度的同时尽可能地控制航运成本,在考虑北极东北航线气象条件和自愿速度损失的基础上,建立北极东北航线航速优化模型,通过带精英策略的非支配排序遗传算法(elitist non-dominated sorting genetic algorithm,NSGA-Ⅱ)和逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)筛选出最佳折中解。选取北极东北航线沿线重要港口进行案例分析,结果表明:当航运成本降低时,货主总体满意度会下降,船公司可以根据各货主的重要性调整航速,以平衡航运成本和货主满意度;对比考虑自愿速度损失与无自愿速度损失的北极东北航线航速优化模型发现,两者的优化结果存在显著差异,自愿速度损失直接影响航速优化模型的准确性;航速优化对时间窗较为敏感,较大的时间窗可以为减速和提高船舶能效提供更大的机会空间。
