Development and prospects of initial alignment method for strap-down inertial navigation system北大核心CSCD

Initial alignment technology is directly related to the navigation accuracy and startup time of the strap-down inertial navigation system (SINS), and has always been regarded as a challenging focal point in the research field of inertial navigation. This paper makes a comprehensive survey of SINS initial alignment technology, briefly introduces the basic principles of initial alignment without latitude, coarse alignment with known latitude and precise alignment, and points out their advantages, disadvantages and applicable conditions. The research and effects of existing initial alignment error suppression techniques are then analyzed and discussed. Finally, according to the problems of existing initial alignment methods and the development requirements of the carrier, the future research direction of SINS initial alignment technology is predicted. © 2022 Journal of Clinical Hepatology. All rights reserved.     

基于自适应迭代学习控制的列车自动驾驶算法

针对高速列车自动驾驶系统受到时变外部扰动和受限状态的情况，提出一种基于迭代学习控制的自适应控制算法. 基于Lyapunov 函数，利用列车运行过程中的状态偏差，推导出自适应迭代学习控制律和参数学习更新律. 构造类Lyapunov 函数的复合能量函数，通过迭代域的差分，证明其差分负定性和收敛性. 采用所提控制算法对列车跟踪性能进行计算机仿真和实例仿真验证，结果表明，所提出的自适应迭代学习控制算法对列车期望曲线跟踪具有较高的精度和较快的收敛速度，能够在较短的迭代次数实现对期望曲线的精确跟踪.     

基于模糊层次的铁路混装货物装运安全评价研究

随着客户运输需求的多样化升级,采用铁路混装货物装运模式在提升铁路运输效率、吸引多样货源等方面相较于单一品名装运显示出一定的优越性,同时混装货物装运面临的安全压力相较于单一品名装运出现骤增。为保障铁路混装货物装运安全,在阐述混装货物装运特点及安全控制要点的基础上,从"人防""物防""技防"3方面构建混装货物装运安全评价指标体系,采用层次分析法确定各指标权重,并利用模糊综合评价法对铁路混装货物装运安全进行评价,通过实例验证该评价方法的实用性及有效性,为铁路混装货物装运安全评价提供依据。     

隧道洞口边坡变形的多阶段递进预测研究

为及时掌握隧道洞口边坡的变形规律,保证隧道进洞过程的安全,采用小波变换剔除变形序列中的误差信息,将原始序列分解为趋势项和误差项序列,并采用PSO-LSSVM模型和ARMA模型分别对趋势项和误差项进行预测,将两者叠加即得到边坡的综合变形预测值,再利用马尔科夫链建立预测误差的修正模型,进一步提高预测精度。对预测模型进行实例分析,结果表明： sym9小波函数、启发式阈值标准、硬阈值选取标准及10层小波分解的去噪效果较优,且通过综合预测,得到边坡变形预测结果的相对误差均值为1.03%,方差值为0.042 6,预测精度和稳定性较高,验证了预测模型的有效性。     

Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices – average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations – are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs.     

载人潜器人因工程研究

对载人潜器人因工程的研究现状、研究范畴、技术手段和发展趋势进行梳理和总结,旨在为指导载人潜器设计总体集成优化、提高潜器综合效能、节省全寿命周期费用及缩减潜器人员编制提供参考。     

轨道安全监测中传感器优化配置研究

为了研究轨道结构安全监测中传感器的合理配置方法,确保城市轨道交通安全运营,通过建立桥上交叉渡线道岔模型和钢轨应变传递误差最小的目标函数,提出传感器测点选择依据和监测方案评价标准,保证监测方案的经济性、合理性。结论表明:模型数据曲线可以指导监测系统的测点位置选择;随着传感器测点数量增加,监测误差逐渐减小,但在误差比率的"拐点"后增加单位个数传感器带来的收益降低;通过建立误差最小准则的目标函数,结合误差指标可以评价和优化传感器布设方案;钢轨位移、附加力等指标的监测方案也可以通过本文方法确定。     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.     

基于层次分析法的区间估计的车站能力查定误差因子分析研究

本文归纳总结了车站能力查定计算过程中存在的多种影响因素,深入分析了其产生原因和不可避免性。然后利用层次分析法的区间估计方法计算得出各种误差因子对计算结果的影响权重大小,并对比不同误差因子的权重区间,筛选出强误差因子,剔除弱误差因子,为精确计算车站能力值提供依据。     

Incorporating human-factors in car-following models: A review of recent developments and research needs

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams.It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models.This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.