超大型油船(very large crude carrier,VLCC)目的港预测对海运原油流向预测以及货源地未来运力估计具有重要作用。针对VLCC的AIS目的港信息存在缺失、更新不及时、不准确等现象,提出一种基于隐马尔科夫模型的VLCC目的港预测方法。分析船...超大型油船(very large crude carrier,VLCC)目的港预测对海运原油流向预测以及货源地未来运力估计具有重要作用。针对VLCC的AIS目的港信息存在缺失、更新不及时、不准确等现象,提出一种基于隐马尔科夫模型的VLCC目的港预测方法。分析船舶AIS轨迹数据,得到油船历史停靠港口序列;根据VLCC轨迹提取习惯航路,以航路中的交叉点为依据设置观测线;利用船舶航行轨迹数据判断船舶是否经过观测线以及经过观测线的方向,对不同方向分别计算船舶在挂靠港间的转移概率矩阵和船舶挂靠港与观测线间的输出概率矩阵,建立VLCC目的港预测模型并进行预测。研究结果表明:在大多数情况下VLCC目的港预测的准确率可以达到70%以上;航线越固定、运行越规律的船舶,预测准确率越高;船舶越靠近目的港,预测越准确;重载状态下的船舶目的港预测更准确。展开更多
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.展开更多
针对船舶压载水管理系统(Ballast Water Management System,BWMS)设备选型的多样性和复杂性问题,以运营的超大型油船(Very Large Crude Carrier,VLCC)为研究对象,分别采用层次分析-逼近理想解排序法和模糊层次分析法对BWMS进行设备选型...针对船舶压载水管理系统(Ballast Water Management System,BWMS)设备选型的多样性和复杂性问题,以运营的超大型油船(Very Large Crude Carrier,VLCC)为研究对象,分别采用层次分析-逼近理想解排序法和模糊层次分析法对BWMS进行设备选型研究。结果表明,采用这2种方法得到的最优方案结果一致,2种方法都适于对VLCC进行BWMS设备选型,但对于没有实际数据,只能通过专家评判得到数据的这类设备的选型而言,采用模糊层次分析法的效果优于层次分析-逼近理想解排序法。展开更多
文摘超大型油船(very large crude carrier,VLCC)目的港预测对海运原油流向预测以及货源地未来运力估计具有重要作用。针对VLCC的AIS目的港信息存在缺失、更新不及时、不准确等现象,提出一种基于隐马尔科夫模型的VLCC目的港预测方法。分析船舶AIS轨迹数据,得到油船历史停靠港口序列;根据VLCC轨迹提取习惯航路,以航路中的交叉点为依据设置观测线;利用船舶航行轨迹数据判断船舶是否经过观测线以及经过观测线的方向,对不同方向分别计算船舶在挂靠港间的转移概率矩阵和船舶挂靠港与观测线间的输出概率矩阵,建立VLCC目的港预测模型并进行预测。研究结果表明:在大多数情况下VLCC目的港预测的准确率可以达到70%以上;航线越固定、运行越规律的船舶,预测准确率越高;船舶越靠近目的港,预测越准确;重载状态下的船舶目的港预测更准确。
基金Supported by the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
文摘针对船舶压载水管理系统(Ballast Water Management System,BWMS)设备选型的多样性和复杂性问题,以运营的超大型油船(Very Large Crude Carrier,VLCC)为研究对象,分别采用层次分析-逼近理想解排序法和模糊层次分析法对BWMS进行设备选型研究。结果表明,采用这2种方法得到的最优方案结果一致,2种方法都适于对VLCC进行BWMS设备选型,但对于没有实际数据,只能通过专家评判得到数据的这类设备的选型而言,采用模糊层次分析法的效果优于层次分析-逼近理想解排序法。
