船摇前馈控制补偿效果及优化方法研究

雷达隔离船摇的主要手段之一.针对船摇前馈在实际工程应用中补偿效果不理想的问题,通过对前馈复合控制原理、船摇前馈实现方法和效果影响因素的研究,提出了工程实现的优化方法.采用时间序列分析方法进行船摇数据预报可以提高近一个数量级的预报精度,从而直接提高船摇前馈补偿量的计算精度和补偿效果.通过在前馈补偿环节中加入自适应设计以实现船摇前馈信息有效平稳地加入控制环路.此外,选择适宜的时机加入船摇前馈也是工程应用的一个重要因素.     

AB组填料在高铁路基填筑施工中的质量控制与检测

本文主要介绍哈尔滨至大连客运专线路基AB组填料施工工艺。     

FAT文件系统下硬盘分区表损坏的解决方案

针对目前广泛采用多分区的硬盘出现分区表故障的情况,从数据恢复的原理和可行性上进行分析,提出采用普通恢复软件和磁盘编辑工具Winhex手工恢复数据相结合的解决方法,达到数据恢复的最优效果。     

DEA方法在电子对抗装备软件综合评价中的应用研究

针对电子对抗装备软件综合评价中四种不同的群体,建立装备软件综合评价的指标体系,并根据其特点,将模糊综合评价方法和DEA方法结合起来,建立了电子对抗装备软件综合评价和装备软件组织与设计开发方案选择模型,最后给出一个实例验证了模型的正确性。     

A fuzzy Delphi-AHP-TOPSIS framework to identify barriers in big data analytics adoption: case of maritime organizations

ABSTRACT

Big data analytics has prospered in recent years and has triggered revolutionary changes in various industries. However, its adoption in maritime organizations is relatively lagged and there is no study addressing this phenomenon so far. This paper develops a fuzzy Delphi-AHP-TOPSIS framework to identify barriers in emerging technology adoption. A case study employs this framework to investigate the hurdles for big data analytics to be adopted in the maritime industry. The case study shows that the framework is useful in identifying and ranking barriers. The results unveil the most serious managerial, cultural, and technical barriers that impede the adoption of big data analytics in maritime organizations.     

铁路微观路网基础数据的组织方法研究

主要研究了应用于运行图编制、调度及相关研究等工作的铁路微观路网的数据组织方法,提出了采用数据分层模型进行数据组织的思想,据此将基础数据分为图形显示层、拓朴层、电气层、逻辑层四个层次,各层次具有相对独立性并相互关联以利于数据的组织,在此基础上对各个层次中的主要数据的属性进行了详细的分析,并针对不同的数据特点提出了相应的数据组织方法,最后介绍了基础数据的录入方法及其实现．     

基于手机信令数据的旅客联程出行时空特征分析方法

为高效准确地识别旅客联程出行方式,基于旅客联程出行行为特征,引入不同运输方式场站地理位置、场站数据取样最佳半径、旅客行驶速度等关键参数,对旅客手机信令数据进行筛选、校核和计算,提出了基于手机信令数据的旅客联程出行方式识别方法,同时测算了不同运输方式场站数据取样最佳半径。以2018年国庆假期广东省内旅客出行为例进行分析,剔除了约98%的无关信令数据。分析结果显示,广东省内公铁联运出行比例最高,广州、深圳两大枢纽城市的客流集疏运效应突出,广佛城际出行联系较为密切,佛山机场的潜力较大。研究表明,识别方法大幅降低了信令数据分析量和运算成本,方法原理和技术路线清晰,分析结果准确、符合实际。     

Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China

To improve predictive accuracy, new hybrid models are proposed for container throughput forecasting based on wavelet transforms and data characteristic analysis (DCA) within a decomposition-ensemble methodology. Because of the complexity and nonlinearity of the time series of container throughputs at ports, the methodology decomposes the original time series into several components, which are rather simpler sub-sequences. Consequently, difficult forecasting tasks are simplified into a number of relatively easier subtasks. In this way, the proposed hybrid models can improve the accuracy of forecasting significantly. In the methodology, four main steps are involved: data decomposition, component reconstruction based on the DCA, individual prediction for each reconstructed component, and ensemble prediction as the final output. An empirical analysis was conducted for illustration and verification purposes by using time series of container throughputs at three main ports in Bohai Rim, China. The results suggest that the proposed hybrid models are able to forecast better than do other benchmark models. Forecasting may facilitate effective real-time decision making for strategic management and policy drafting. Predictions of container throughput can help port managers make tactical and operational decisions, such as operations planning in ports, the scheduling of port equipment, and route optimization.     

一种改进的基于密度和网格的高维聚类算法

提出了一种改进的基于密度和网格的高维聚类算法,并对算法有效性进行了验证.该算法通过减少样本点数量的方法达到减少稠密子空间数量.在发现高维稠密子空间时,对样本库进行精简.这些样本点的求得能有效减少求解最小聚类的时间复杂度.     

改善209000吨Newcastlemax散货船总纵强度的探究

通过对209,000吨Newcastlemax散货船货舱纵向划分、下墩用途及压载舱舱容的调整,探讨了各种措施下该船型总纵弯矩的变化情况,以此来改善其总纵强度,为优化船体结构、降低空船重量提供了有利条件。