Variable guidance for pedestrian evacuation considering congestion,hazard, and compliance behavior

A methodology for optimizing variable pedestrian evacuation guidance in buildings with convex polygonal interior spaces is proposed. The optimization of variable guidance is a bi-level problem. The calculation of variable guidance based on the prediction of congestion and hazards is the upper-level problem. The prediction of congestion provided the variable guidance is the lower-level problem. A local search procedure is developed to solve the problem. The proposed methodology has three major contributions. First, a logistic regression model for guidance compliance behavior is calibrated using a virtual reality experiment and the critical factors for the behavior are identified. Second, the guidance compliance and following behaviors are considered in the lower-level problem. Third, benchmarks are calculated to evaluate the performance of optimized variable guidance, including the lower bound of the maximum evacuation time and the maximum evacuation time under a fixed guidance. Finally, the proposed methodology is validated with numerical examples. Results show that the method has the potential to reduce evacuation time in emergencies.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

船舶大型低速柴油机VIT机构研究

在船舶大型低速柴油机的管理维护过程中,其VIT机构较之于柴油机其他部件更易于出现复杂的管理状况,甚至发生设备故障,针对此现象,文章研究分析了船舶大型低速主动力柴油机的VIT机构功能调整、故障处理和维护管理技术等三个方面的难点问题,提出了相应的预防措施和解决方案,为广大同行提供借鉴。     

基于变流阻系数的船舶海水冷却管网水力特性研究

准确计算管网摩阻系数,对具有复杂流态船舶流体管网的优化设计、运行、管理及工作特性实时调节和准确计算都有重要意义。本文提出采用“变流阻系数”方法,在计算中实时更新任意迭代时步的各管网分支流阻系数,基于图论理论,在MATLAB软件中编程实现。理论计算与实验结果进行了对比,两者具有较好的一致性。研究发现,相比定流阻系数方法,利用变流阻系数网络解算算法对船舶海水冷却管网进行水力分析,可以大大减小解算误差,提高解算精度。     

岩溶隧道围岩位移场解析解研究

利用复变函数并结合Schwarz交替法推导了半无限平面内隧道与岩溶之间相互影响的位移和应力解析解。结合实际工程计算得到如下结论：随着岩溶与隧道的净距不断增大,岩溶对隧道的影响越来越小,当溶洞半径是净距的5倍时,可认为岩溶对隧道不产生影响。利用本文得到的岩溶隧道围岩的解析解,可计算得到岩溶影响下隧道围岩的应力和位移值,并且计算结果有较高的精度。     

Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

柴油机曲轴主轴承模糊随机优化设计研究

柴油机曲轴主轴承是一种变载荷流体动压滑动轴承，由于在运转中其工作性能随负载的变化而变化，现在还没有一个完全合理的优化设计方法。本文根据其中存在有随机性和模糊性的特点，给出了模糊随机优化设计的数学模型，提出了一种决策方法。通过一船用柴油机实例的检验表明，此模型和方法是适宜的，效果良好，优于其他方法。     

猎雷艇声纳基阵俯仰角姿态伺服系统变结构控制

本文对猎雷艇声纳基阵俯仰角姿态伺服系统分别进行了频域法控制规律的设计和变结构控制律的设计，对两种设计由于基阵附加惯性力矩的变化而引起系统参数变化的工况进行了数字仿真，并给出了仿真结果。     

基于变权综合原理的斜拉桥索力、线形状态评估

基于变权综合原理,建立斜拉桥索力、线形状态评估模型。以约束优化索力值为基准值,引入索力上下限;以成桥线形作为基准值,以控制点的竖向允许位移高次插值形成线形评估的上下限。编制状态评估算法,利用变权综合原理和斜率关联度,以适度指标模型进行线性插值,并根据一组实测数据,对索力、线形状态进行评估。计算结果表明,随着均衡系数的减小,桥梁构件评估结果是一个加速退化的过程;变权模式作用下的变权权重越发散,评估结果的退化过程越剧烈,均衡系数-评分曲线的曲率越大。对于每个测点评分状态完全一致的桥梁构件,其均衡系数-评分曲线的曲率恒等于0,即评估结果将与均衡系数的取值无关,数值模拟结果与预测基本吻合。     

张力腿平台尺度优化设计

提出了一种张力腿平台（ＴＬＰ）的尺度优化模型。以平台造价为目标函数,考虑尺寸约束,运动约束和强度约束,用约束变尺度法求解了尺度优化这个非线性规划问题,为张力腿平台的概念设计提供了一种有效的工具。