基于机器学习的船舶机舱设备状态监测方法

[目的]为实现船舶机舱设备的智能状态监测,引入机器学习算法,提出一种结合流形学习和孤立森林的船舶机舱设备状态监测方法.[方法]由于船舶机舱设备的状态监测数据是多维度数据,基于该监测系统,通过流形学习来提取有效的数据特征,实现对原始数据的降维,减少数据复杂度.基于孤立森林算法,在仅利用正常工况数据集的情况下,训练并构建多...     

酸雨湿干循环作用下百色膨胀土裂隙发育规律及其微观机制

为弄清酸雨与湿干循环共同作用对膨胀土裂隙发育的影响,以广西酸雨重灾区的百色原状膨胀土为对象,室内设置高清照相机观测记录经不同酸雨与湿干循环作用后试验土样的裂隙发展,用Image-Pro Plus(IPP)图像处理软件获取土体裂隙特征参数,并采用扫描电镜(SEM)和X射线衍射法研究试验过程中土样的微结构及化学成分演变.据...     

基于自然驾驶跟车数据的驾驶人差异性分析与辨识

为研究跟车工况下个体驾驶行为特性及其辨识,以驾驶人自然驾驶数据为基础,通过统计分析,频域分析及时频分析,多尺度对比驾驶人加速度、碰撞时间倒数、跟车时距等跟车轨迹特征参数分布的差异性;利用统计方法和离散小波变换提取能够表征驾驶人跟车习性差异的特征参数,分析不同参数输入结果,确定最优参数组合,建立基于随机森林的驾驶人差异性...     

人口老龄化背景下香港地铁服务满意度分析与改进策略研究

我国的人口老龄化趋势日益凸显,受到政府和社会的高度关注。实施积极应对人口老龄化国家战略,满足老年人多层次、多样化需求成为国家的中心工作之一。地铁是老年人重要的出行方式和移动性保障,本研究基于270名老年人对香港地铁(MTR)服务满意度的问卷调查数据,建立随机森林模型关联整体满意度与属性满意度,并构建“重要性-满意度”指数识别地铁属性适老化改进的优先级。研究发现：老年人对地铁服务基本满意;老年人重点关注的地铁服务属性是候车空间、优先座位和行车稳定性;老年人较少关注的属性是发车频率和准时性;改进优先级最高的地铁服务属性是优先座位、车厢温度和站点可达性。据此,针对性提出地铁适老化改进策略。     

港口大型煤堆场内含煤雨水的出路

港口大型煤堆场煤炭露天堆存存在的主要问题是环境污染,主要包括煤尘对大气环境的影响和产生大量的含煤雨水。以天津港为例,从其降雨特点入手,分析港口大型煤堆场内含煤雨水的主要去向,明确含煤雨水的治理规模,说明雨污水回用技术中需要解决的主要问题。     

Forecasting road traffic conditions using a context-based random forest algorithm

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimise congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately contexts such as public holidays, sporting events and school term dates. This paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.     

深水悬链复合锚泊线疲劳损伤计算

以某座Spar平台的锚泊系统为研究对象,首先利用三维绕射理论计算Spar平台主体波浪力,得到平台的总体运动响应时程。再建立复合锚泊线的二维非线性有限元动力分析模型,基于DelVecchio(1992)提出的经验公式,采用迭代的方法计算复合锚泊线的刚度。锚泊线和海床之间的接触作用基于刚性海床假定,基于Morrison公式计算锚泊线的惯性力和拖曳力荷载,根据计算得到的平台主体运动响应时程作为锚泊线顶端输入条件,在时域范围内进行复合锚泊线的动力分析。计算得到中国南海某海域各短期海况条件下复合锚泊线应力的时间历程曲线,采用雨流法对其计数得到对应于各短期海况条件下的疲劳载荷谱。最后根据Miner线性累积损伤模型,对复合锚泊线在长期海况条件下的疲劳损伤进行比较计算。     

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.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

俄罗斯原木关税调整的影响及辽宁省的对策

本文在分析俄原木的出口关税率提高对我国的影响和两国森林资源合作开发的基础上,就我省面临的问题提出对策和建议。