基于二维主分量分析的人耳身份识别研究

有效的特征提取方法是解决人耳身份识别任务的关键之一。以主分量分析（PCA）为代表的线性子空间方法在特征提取工作中得到了广泛应用。为了更有效地提取人耳图像特征并减少运算量,将基于二维图像矩阵的2D-PCA方法应用于人耳身份识别。针对三个USTB人耳图像库,采用最近邻分类器,研究了选用不同的特征维数、贡献率,及不同的相似性测度时,2D-PCA方法与传统的PCA方法的识别性能。交叉验证的实验结果表明：2D-PCA方法较PCA方法获得了更短的训练时间和更高的识别率,说明基于图像矩阵的2D—PCA方法是一种效率更高,鲁棒性更强的人耳身份识别方法。     

考虑多维动态特征交互的高速公路实时事故风险建模

为探究天气和道路等特征,以及交通流、天气、道路及时间等多维动态特征之间的交互作用对实时事故风险预测模型精度的影响,本文基于京哈高速公路北京段的事故数据,以及匹配的交通传感器数据、天气数据和道路特征等,构建4个数据集,分别为只包含交通流变量,包含交通流变量、天气及时间特征变量,包含交通流变量、道路及时间特征变量,包含交通流变量、天气、道路及时间特征变量。从考虑多维动态特征的交互效应出发,基于深度交叉网络,提出一种新的实时事故风险预测模型。结果显示,本文所构建的深度交叉网络模型比其他几种实时事故风险预 测方法显示出更高的精度。模型的AUC值(Area Under Curve)可达0.8562,在0.2的概率阈值下, 可以正确分类84.26%的非事故数据和77.55%事故数据。结论表明,本文采用的多维动态特征交互样本条件下的深度交叉网络模型能够有效地预测高速公路交通事故,可为我国高速公路安全管理部门提供理论与技术支持。     

行人过街风格多参数融合识别

行人作为城市慢行交通系统中的弱势群体,探究其过街行为特征,对减少人车冲突,提升行人过街的本质安全具有重要的现实意义。本文针对特定交叉口,设计自由过街及心理临界过街两种场景状态下的行人过街试验。分析过街速度、心理临界时窗及视觉特性等参数之间的耦合关系,选取风险感知因子与临界安全系数作为心理临界过街状态下行人风格的表征指标,构建过街风格识别模型,透析不同风格行人过街行为可靠性。结果表明：年龄对过街速度和心理临界时窗有显著影响;心理临界时窗与过街速度和信号灯区域注视概率呈负相关关系,与车行区域注视概率呈正相关;心理临界过街状态下,依据风格识别结果,男性谨慎程度高于女性,冒险型行人过街失败率为65.22%;冒险型老年人对自身运动机能的评估能力以及视觉搜索特征存在重要缺憾。研究结果可为交叉口基础设施设计与优化,行人过街教育与培训等提供参考。     

A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

基于多维信息特征分析的驾驶人认知负荷研究

准确评估驾驶人认知负荷水平，对于深入研究驾驶人行为特性，改善驾驶安全性具有重要意义。现有的驾驶人认知负荷分类方法，大多基于心电、脑电等生理信息和车辆信息，由于特征选择上的单一性，导致驾驶人认知负荷分类模型的分类精度不高。设计基于跟驰场景的不同认知负荷N-back次任务试验，通过采集受试者的生理信号和车辆信号，结合NASA_TLX主观评分和机器学习算法，提出了基于多维信息特征融合的驾驶人认知负荷分类方法。研究表明：基于生理信息和车辆信息的多维信息特征认知负荷分类方法，其精度显著高于传统的基于生理信息的认知负荷分类方法，以多维信息特征为输入，随机森林法以其稳定性好、抗过拟合能力强的特点，表现出优异的分类效果，相比神经网络和支持向量机，具有最高的平均分类精度。     

碱矿渣胶凝材料的开裂性能研究

碱矿渣胶凝材料是具有高强、高耐久性的胶凝材料,但其收缩大,易开裂,是阻碍其广泛应用的主要原因.通过圆环法研究了MgO膨胀剂、聚丙烯纤维和减缩剂单掺或复掺对碱矿渣胶凝材料开裂性能的影响.研究结果表明:单掺MgO膨胀剂对碱胶凝体系抗裂不利,减缩剂和聚丙烯纤维的掺入能够有效延缓裂缝的形成,减小开裂面积.     

Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

T91与钢102异种钢焊接接头氢致裂纹研究

用甘油法分别测定了CM-9Cb、R347和国内新研制的T91专用焊条熔敷金属中的护散氢含量，通过阴极电解渗氢和定载延迟开裂试验，研究了选用不同焊条焊接时、T91与钢102异种钢接头的氢致裂纹倾向，确定了临界启裂应力。     

新的汽车牌照识别系统及其成像技术

介绍了一种高性能的汽车牌照识别系统及其成像技术。系统采用了新的成像控制和闪光灯补光技术，使其成像特别适合牌照区域分割和字符识别，成像质量完全不受实际应用环境中强烈的光线变化的影响，从而达到较高的全天候识别率和实际应用的要求。给出了系统在全天候条件下的成像效果和牌照识别效果．结果显示系统可在全天候条件下获得高质量的图像。