Probability-based clustering and its application to WLAN location estimation

Wireless local area networks (WLAN) localization based on received signal strength is becoming an important enabler of location based services. Limited efficiency and accuracy are disadvantages to the deterministic location estimation techniques. The probabilistic techniques show their good accuracy but cost more computation overhead. A Gaussian mixture model based on clustering technique was presented to improve location determination efficiency. The proposed clustering algorithm reduces the number of candidate locations from the whole area to a cluster. Within a cluster, an improved nearest neighbor algorithm was used to estimate user location using signal strength from more access points. Experiments show that the location estimation time is greatly decreased while high accuracy can still be achieved.     

抗疏力固化土的室内击实试验研究

文章通过分析抗疏力固化土室内击实试验和无侧限抗压强度试验,探讨抗疏力固化材料的不同掺量对抗疏力固化土最大干密度和最佳含水量的影响,及其对抗疏力固化土无侧限抗压强度的影响.     

广义回归神经网络在交通运输量预测中的应用

因为交通运输、地方经济及企业发展的紧密联系,交通运输量预测成为运输市场和经济发展研究中的一个重要问题。以影响交通运输量的若干重要参数作为样本数据,构建一个广义回归神经网络,经过学习训练后使之达到误差最小,再以此网络外推出未来的交通运输量。结果证明广义回归神经网络用于交通运输量预测的有效性。     

Generalization of Results of Computations and Natural Experiments at Steel Parts Quenching

IntroductionThepaPerpresentsthegenera1izationofresultsofnumericalcomputationsandnaturalexperimentsbasedonthesimilaritytheoryandtheoryofregularthermalconditions.Thenumericalcomputationshasbeenmadeonthebasisofsolvingequationsofnon-stationarythermalconductivityandthermoplaSticity.Thegeneralizationofresultsliesinthefactthatwefoundadimensionlessformulaforthedeterminationofthetimeofreachingthemaximumcompressionstressesonthesurfaceofp8rtstobequenched.ThepatstobequenchedaredividedintothreegrouPs:pla…     

成层软土路基极限填筑高度研究

文章依托某线路工程,分析软土路基的失稳机理,探讨线路工程穿越软土区时,成层软土路基可行的极限填高计算方法。通过填高与稳定性的横向和纵向分析得出路基的极限填筑高度,再用数值法计算由填高产生的路堤和路基内位移分布。研究表明,用整体稳定性确定成层软土的极限填筑高度是可行的。     

利用方向自适应投影统一运营铁路坐标系

为满足TB 10601-2009《高速铁路工程测量规范》中控制网设计时边长投影变形值的要求,设计院通常把1条几百公里的高速铁路划分为多个几十公里的工程投影独立坐标系。相邻坐标系之间的搭接段线路设计中线,可采用前、后投影带坐标系的设计参数来推算,由于投影变形导致相邻坐标系推算出的线路设计中线存在横向偏差,轨道精调时若处理不当,会降低轨道的平顺性,甚至影响运营铁路的行车安全。为解决换带搭接段设计中线的横向偏差,提高轨道精调的效率及精度,本文提出利用方向自适应投影将运营铁路多个坐标系统一为1个坐标系的方法,同时建立精确的线路参数转换模型,实现了原设计参数与新设计参数的精确转换。通过工程实例验证,坐标系统一后的线路参数与原设计参数较差较小,不会影响轨道平顺性,该方法可行。研究成果可为提升运营铁路精调效率提供借鉴。     

Development of a maximum likelihood regression tree-based model for predicting subway incident delay

This study aims to develop a maximum likelihood regression tree-based model to predict subway incident delays, which are major negative impacts caused by subway incidents from the commuter’s perspective. Using the Hong Kong subway incident data from 2005 and 2009, a tree comprising 10 terminal nodes is selected to predict subway incident delays in a case study. An accelerated failure time (AFT) analysis is conducted separately for each terminal node. The goodness-of-fit results show that our developed model outperforms the traditional AFT models with fixed and random effects because it can overcome the heterogeneity problem and over-fitting effects. The developed model is beneficial for subway engineers looking to propose effective strategies for reducing subway incident delays, especially in super-large-sized cities with huge public travel demand.     

Minimum sample size for extreme value statistics of flow-induced response

Extreme value statistics are one way to determine the maximum design loads for systems in extreme conditions, such as operational loads experienced by ships. Accurate predictions typically require large sample sizes, which are not always possible to obtain. Conversely, small sample sizes lead to more variation in the predictions. Increasing the sample size improves the variance to a desired range. The proposed method aimed to estimate a minimum sample size for an extreme value process by specifying and obtaining an acceptable variance. Minimum sample sizes for extreme value statistics depend on the distribution's behavior, so the method proposed here was designed for use before and during measurements. To test the proposed method, the response of a cantilever fin with a varying angle of attack was measured. The proposed method was able to estimate minimum sample sizes for several distributions. Accuracy was demonstrated by randomly drawing measured and simulated samples.     

Optimization of mid-block pedestrian crossing network with discrete demands

In many cases, pedestrian crossing demands are distributed discretely along an arterial segment. Demand origins, destinations and crosswalks comprise a pedestrian crossing network. An integrated model for optimizing the quantity, locations and signal settings of mid-block crosswalks simultaneously is proposed to best trade-off the operational performances between pedestrians and vehicles. Pedestrian behavior of choosing crosswalks is captured under a discrete demand distribution. Detour distance and delay at signalized crosswalks are formulated as a measure of pedestrian crossing cost. Maximum bandwidths are modeled in analytical expressions as a measure of vehicular cost. To solve the proposed model, the Non-dominated Sorting Genetic Algorithm II (NSGA II) based algorithm is designed and employed to obtain the Pareto frontier efficiently. From the numerical study, it is found that there exists an optimal number of mid-block crosswalks. Excess available crosswalks may make no contributions to improvement in pedestrian cost when the constraint of the minimum interval between crosswalks and vehicular cost are taken into account. Two-stage crosswalks are more favorable than one-stage ones for the benefits of both pedestrian and vehicles. The study results show promising properties of the proposed method to assist transportation engineers in properly designing mid-block crosswalks along a road segment.     

Investigating the nature and impact of reporting bias in road crash data

This paper investigates the nature, and impact of the reporting bias associated with the police-reported crash data on inferences made using this data. In doing so, we merge a detailed emergency room data and police-reported crash data for a specific region in Denmark. To disentangle potentially common observable and unobservable factors that affect drivers’ injury severity risk and their crash reporting behavior, we formulate a bivariate ordered-response probit model of injury severity risk and crash reporting propensity. To empirically identify the reporting bias in this joint model, we exploit an exogenous police reform that particularly affects some specific municipalities of the region under consideration. The empirical analysis reveals substantial reporting bias in the commonly used police-reported road crash data. This non-random sample selection associated with the police-reported crash data leads to biased estimates on the effect of some of the explanatory variables in injury severity analysis. For instance, estimates based on the police-reported crash data substantially underestimate the effectiveness of seat belt use in reducing drivers’ injury severity risk.