重庆与纽约两种轨道交通互联互通CBTC系统安全评估的对比研究

针对由城市轨道交通互联互通CBTC(基于通信的列车控制)系统推广带来的风险,安全评估作为重要的监督环节显得尤为重要.在对重庆与纽约2个典型城市的互联互通CBTC系统架构分析的基础上,从安全评估标准、风险评估方法、安全认证过程及活动等3个方面对二者的安全评估进行详细对比.结合二者自身互联互通CBTC系统的特点和应用场景,制定了相适应的安全评估规则,并通过不同方法和途径实现了系统既定的安全目标.     

冰区核电平台系泊设计及影响研究

本文根据冰区浮式核电平台的主要功能及外部环境条件,确定平台的基本形式,并在此基础上进行了系泊系统的优化设计.与以往研究不同的是,考虑到冰区海洋的特殊性,除计算风、浪、流作用情况外,还运用Sdem软件构造了海冰离散单元模型,对海冰与海洋系泊结构物的相互作用过程进行了数值模拟.在此基础上,结合系泊分析软件ANSYS-AQWA的二次开发功能,对平台运动及其系泊系统进行了时域耦合分析,研究了平台在不同环境载荷作用下的运动响应;进行了系泊参数研究,分析一般规律,最终提出了一种满足规范要求且性能优良的系泊方案.研究结果表明:本文提出的冰区核电平台系泊系统可抵抗恶劣的环境影响;通过合理选择系泊缆数量、夹角、预张力大小及顶端倾角等系泊参数,可减小平台运动响应.     

基于改进BP-GA方法的FPSO舷侧结构耐撞性能优化设计

基于遗传算法和ABAQUS参数化有限元仿真技术,对传统的BP-GA优化方法进行改进,并采用改进的BP-GA方法对浮式生产储油卸油装置(FPSO)舷侧结构的耐撞性能进行优化,以验证其可行性和准确性。结果表明,与传统的BP神经网络相比,经遗传算法优化的BP神经网络具有更高的预测精度和更强的泛化能力;改进的BP-GA优化方法可在结构减重的基础上进一步提高结构的耐撞性能,能较好地适用于复杂的FPSO舷侧结构耐撞性优化设计。采用的优化方法具有通用性,可为抗爆性能的优化设计提供参考。     

自动化集装箱码头综合管沟结构设计

综合管沟是近10 a来我国城市地下管线铺设的新型设计思路，在港口工程中的应用较为少见。港口工程具有独特的平面布置，场区内管线类型、地基条件、荷载、地面设施、交互节点等均与其他管沟大不相同，结构设计条件非常复杂。针对上述问题，在广州港南沙四期工程综合管沟结构设计中，结合自动化集装箱码头的特点，对荷载条件、沉降要求等方面进行了研究，提出了多功能盖板段、渐变式复合地基等方面的优化设计思路，完成了适用于自动化集装箱码头的综合管沟结构设计，有效解决了管线综合的问题。本文所阐述的结构设计经验对后续的自动化集装箱码头综合管沟结构设计具有一定的参考意义。     

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.     

Some insights in novel risk modeling of liquefied natural gas carrier maintenance operations

This study discusses the analysis of various modeling approaches and maintenance techniques applicable to the Liquefied Natural Gas (LNG) carrier operations in the maritime environment. Various novel modeling techniques are discussed; including genetic algorithms, fuzzy logic and evidential reasoning. We also identify the usefulness of these algorithms in the LNG carrier industry in the areas of risk assessment and maintenance modeling.     

Enabling a viable technique for the optimization of LNG carrier cargo operations

In this study, we optimize the loading and discharging operations of the Liquefied Natural Gas (LNG) carrier. First, we identify the required precautions for LNG carrier cargo operations. Next, we prioritize these precautions using the analytic hierarchy process (AHP) and experts’ judgments, in order to optimize the operational loading and discharging exercises of the LNG carrier, prevent system failure and human error, and reduce the risk of marine accidents. Thus, the objective of our study is to increase the level of safety during cargo operations.     

CT reconstruction with priori MRI images through multi-group datasets expansion

Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.     

Visualisation of uncertainty in probabilistic traffic models for policy and operations

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.