自适应神经网络用于船舶动力定位系统

船舶动力定位（DP）采用一种控制系统驱动装在船上的侧推器和尾部推进器或者几个全回转的推进器，使船定位在海平面的要求位置上。船受到风、浪及流等海洋环境力的作用时会漂移离开原位置，传统的控制方法是采用PID（比例、积分、微分）反馈控制。顾和李（1994）提出了一种新的基于人工神经网络的控制方法，它具有许多优越性：①一个可随意调节的目标函数以适合不同的需求──定位精度高或节约定位能量后前馈控制并能自适应于不同的环境力变化包括非线性的波浪漂力；本文在目标函数中引入了与速度相关的项，从而提高控制质量。这一方法也可用于自动驾驶船舶沿设定的轨迹航行。计算机模拟结果表明本方法能达到很好的控制效果。     

Second-order perturbation added mass fluctuation on vertical vibration of tension leg platforms

Total motion in waves can be considered as a superposition of the motion of the body in still water and the forces on the restrained body. In this study the effect of added mass fluctuation on vertical vibration of tension leg platforms (TLP) in the case of vibration in still water for both free and forced vibration subjected to axial load at the top of the leg is presented. This effect is more important when the amplitude of vibration is large. Also this is important in fatigue life study of tethers. The structural model used here is very simple. Perturbation method is used to formulate and solve the problem. First- and second-order perturbations are used to solve the free and forced vibrations, respectively.     

基于马尔可夫链的极值波高预测

本文研究了由日最大波高系列估算设计极值波高时，相邻日最大波高间的相关性对极值预测的影响，从日最大波高系列遵从马尔可夫链的假定出发，考虑到国内外经常采用对数一正态分布的韦布尔分布拟合波高长期分布的现实，本文用解析法求解了对数一正态分布情况下的极值预测。同时，对解析法难以求解的非正态随机变量情况（如韦布尔分布），用计算机随机模拟方法求解其极值预测，用上述两种方法对北大西洋和北海有关日最大波高系列的预测     

高速铁路应答器报文接收质量测试及分析

主要介绍了京沪高铁信号综合试验期间,在不同列车速度条件下完成的应答器报文接收质量测试情况,并分析了不同安装方式下应答器的作用范围。     

铁路基桩反射波法完整性检测应用探讨

对使用反射波法检测铁路大直径基桩完整性时,其反射波曲线中浅部会产生1个正向缺陷信号的问题作了详细探讨。经分析,它是由于基桩在冲击成孔或人工挖孔施工中,其桩顶砂浆没有清理干净和人工挖孔桩护壁等原因引起的。所以,在采用反射波法检测基桩完整性时,其桩顶砂浆要清理干净,且露出含有粗细骨料均匀致密呈青色的混凝土新表面,而对于人工挖孔桩的护壁影响因素可在桩体混凝土强度达到设计强度95%时进行检测。这样,就能够消除反射波曲线中浅部的正向缺陷信号,以便正确判定基桩完整性。     

发动机点火波形与故障分析

点火波形是发动机点火系工作时点火线圈初级、次级电流及电压随时间（或曲轴转角）变化的关系在专用示波器上的显示，它实时地反映了点火系的工作状况。因此，点火系的故障也必然反映在点火波形上。分析了点火波形的形成、影响点火波形的因素     

Tuning of transfer functions for analysis of wave–ship interactions

Transfer functions are often used together with a wave spectrum for analysis of wave–ship interactions, where one application addresses the prediction of wave-induced motions or other types of global responses. This paper presents a simple and practical method which can be used to tune the transfer function of such responses to facilitate improved prediction capability. The input to the method consists of a measured response, i.e. time series sequences from a given sensor, the 2D wave spectrum characterising the seaway in which the measurements are taken, and an initial estimate of the transfer function for the response in study. The paper presents results obtained using data from an in-service container ship. The 2D wave spectra are taken from the ERA5 database, while the transfer function is computed by a simple closed-form expression. The paper shows that the application of the tuned transfer function leads to predictions which are significantly improved compared to using the transfer function without tuning.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

Delivering improved alerts,warnings, and control assistance using basic safety messages transmitted between connected vehicles

When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.     

The influence of fully nonlinear wave forces on aero-hydro-elastic calculations of monopile wind turbines

The response of an offshore wind turbine tower and its monopile foundation has been investigated when exposed to linear and fully nonlinear irregular waves on four different water depths. The investigation focuses on the consequences of including full nonlinearity in the wave kinematics. The linear and nonlinear irregular wave realizations are calculated using the fully nonlinear potential flow wave model OceanWave3D [1]. The linear and nonlinear wave realizations are compared using both a static analysis on a fixed monopile and dynamic calculations with the aeroelastic code Flex5 [2]. The conclusion from this analysis is that linear wave theory is generally sufficient for estimating the fatigue loading, but wave nonlinearity is important in determining the ultimate design loads.