横浪作用下系泊船舶撞击能量的分析和研究

收集了国内5万~30万t系泊船舶在横浪作用下撞击能量物理模型试验的研究成果,采用理论结合模型试验的分析方法,研究码头系泊船舶在横浪作用下的撞击能量规律。通过分析系泊船舶横向撞击速度随波高、水深、装载度等要素的变化规律,并结合物理模型试验进行数据拟合,给出完整的系泊船舶撞击能量估算公式。     

The Cooperative Multi-agent Learning with Random Reward Values

This paper investigated how to learn the optimal action policies in cooperative multi-agent systems if the agents‘ rewards are random variables, and proposed a general two-stage learning algorithm for cooperative multiagent decision processes. The algorithm first calculates the averaged immediate rewards, and considers these learned rewards as the agents‘ immediate action rewards to learn the optimal action policies. It is proved that the learning algorithm can find the optimal policies in stochastic environment. Extending the algorithm to stochastic Markov decision processes was also discussed.     

小型拖网渔船主要参数统计分析

主要参数分析是渔船设计过程中最重要的工作之一.通过对收集到的45艘小型钢质拖网渔船和38艘小型木质拖网渔船的主要参数进行回归分析,得到了各种参数与船长的数学关系,并得出了渔船主要参数的估算公式,可供相关部门在渔船设计和船型论证工作中参考.     

线性回归预测模型在供应链牛鞭效应控制中的应用

探讨了供应链牛鞭效应的成因及其量化描述、供应链库存模型等问题,在此基础上,提出了一种控制牛鞭效应的方法———线性回归预测法,并通过算例进行仿真验证。结果表明,该方法能够有效地降低供应链节点企业的库存水平,抑制牛鞭效应的影响,从而提高供应链的稳定性与竞争力。     

智能船舶动力系统设备的自适应阈值方法

[目的]针对智能船舶动力系统设备的状态监控报警不及时、阈值带宽过大、状态评估参数不准确等问题,提出自适应阈值的确定方法,用以对动力系统设备进行监控报警和状态评估.[方法]首先,采用模拟退火算法优化回归支持向量机(SVR)预测模型,对动力系统设备的常规状态特征参数进行建模;然后,对建模残差进行正态转化,并结合滑动时间窗来...     

A model for vehicle–track random interactions on effects of crosswinds and track irregularities

A stochastic mathematical model is developed to evaluate the dynamic behaviours and statistical responses of vehicle–track systems when random system excitations including crosswinds and track irregularities are imposed. In this model, the railway vehicle is regarded as a multi-rigid-body system, the track system is modelled by finite element theory. These two systems are spatially coupled by the nonlinear wheel–rail contact forces and unsteady aerodynamic forces. The high efficiency and accuracy of this stochastic model are validated by comparing to the robust Monte-Carlo method. Numerical studies show that crosswinds have a great influence on the dynamic performance of vehicle–track systems, especially on transverse vibrations. When the railway vehicle initially runs into the wind field, it will experience a severe vibration stage, and then stepping into a relatively steady state where the fluctuating winds and track irregularities will play deterministic roles in the deviations of system responses. Moreover, it is found that track irregularities should be properly considered in the safety assessment of the vehicle even in strong crosswinds.     

中小城市城乡公交站点的筛选方法研究

为探寻中心城市城乡公交站点的筛选方法,判断当前站点的设置合理性,提出出行兴趣点 的概念,并以显著度指标来表征站点满足居民出行需求情况,结合出行调查数据和网络公开平台 所得数据,提取出行时效性和土地混合利用两个因素,且引入路网通达度、综合服务值两个指 标,建立站点显著度与二者的回归模型,并依据聚类分析将站点进行分级,通过分级结果判断得 到出行兴趣点集合,将其作为城市端城乡公交的备选站点集合。研究结果显示：所建二元回归模 型精度为0.85,拟合效果良好,表明采用显著度指标表征站点需求可行,其与路网通达度、综合 服务值具有很强的相关性;聚类分析将站点分为5 个等级,且4 级及以上站点能满足90%的农村 居民城乡出行需求,可作为出行兴趣点,构成中小城市城乡公交站点集合。     

数学回归法在压实度试验中的应用

结合工程实践,对压实度检测中灌砂法与核子密度仪法之间的回归方程进行推导,并建立起两者之间的回归关系.由此所得的方法既能有效地控制工程质量,又有利于减轻检测人员的劳动强度,加快工程进度.     

驾驶行为非参数微观仿真模型

运用信息挖掘技术最大限度地榨取实测数据所携带的驾驶行为个体有用信息,通过数据修正和精简形成样本数据库,采用K最近邻概率密度函数法对样本数据库进行数据过滤和冗余信息清洗,利用预处理后的数据和非参数回归法构建了驾驶行为非参数仿真模型。模拟得到的后车多元信息与其实际值有很好的拟合性,且实际值以模拟平均值为轴小幅度摆动。仿真结果表明,合适的光滑参数能提高模型精度,使模型避免大样本标定数据的限制,很好地反映和预测跟驰过程中的驾驶员行为。     

A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

The Highway Safety Manual (HSM) recommends using the empirical Bayes method with locally derived calibration factors to predict an agency's safety performance. The data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of these data variables are currently unavailable in most of the agencies' databases. Furthermore, it is not economically feasible to collect and maintain all the HSM data variables. This study aims to prioritize the HSM calibration variables based on their impact on crash predictions. Prioritization would help to identify influential variables for which data could be collected and maintained for continued updates, and thereby reduce intensive data collection efforts. Data were first collected for all the HSM variables from over 2400 miles of urban and suburban arterial road networks in Florida. Using 5 years (2008–2012) of crash data, a random forests data mining approach was then applied to measure the importance of each variable in crash frequency predictions for five different urban and suburban arterial facilities including two‐lane undivided, three‐lane with a two‐way left‐turn lane, four‐lane undivided, four‐lane divided, and five‐lane with a two‐way left‐turn lane. Two heuristic approaches were adopted to prioritize the variables: (i) simple ranking based on individual relative influence of variables; and (ii) clustering based on relative influence of variables within a specific range. Traffic volume was found as the most influential variable. Roadside object density, minor commercial driveway density, and minor residential driveway density variables were the other variables with significant influence on crash predictions. Copyright © 2015 John Wiley & Sons, Ltd.