雷达辐射源信号识别研究综述

介绍雷达辐射源信号识别的过程,概述和分析国内外雷达辐射源识别研究的现状,阐述基于粗糙集理论的雷达辐射源信号识别方法,指出雷达辐射源信号识别的发展趋势,为今后相关研究提供了有益的思路。     

基于地质条件下的公路选线设计探讨

公路选线要受自然条件、技术标准、国家政策、社会环境、道路美学、民族风俗习惯等因素的制约,其中自然条件主要表现为地形、地质等.公路选线时应进行深入调查研究,查明特殊地区和不良地质地段的分布范围、类型、规模及发生、发展的原因和规律,灵活运用选线原则和掌握选线要点.     

国道210线树林召至东胜段公路改建工程地质问题分析

以国道210线树林召-东胜段公路改建工程为研究对象,经过现场地质调查,从地形地貌、区域构造、地层特点及路基填料适应性等方面对其进行宏观地质评价。本研究对附近地质背景相近的公路建设项目评价有一定借鉴意义。     

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。     

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.     

长大隧道施工地质超前预测预报技术应用研究

结合渝怀铁路金洞隧道施工地质超前预测预报技术的实施,着重介绍长大隧道施工地质超前预测预报技术应用研究的目的、思路、方法、手段、工作原理、实施步骤与判别方法、主要预测预报成果及体会。     

铁路工程地质钻机的现状及发展方向

铁路工程地质钻探用钻机结构性能几十年未变,已远远不能适应目前钻掘工程和岩土工程的市场要求,通过对国内外钻机生产现状的分析,探讨铁路钻探设备的发展方向。     

埠阳庄大桥复杂地质条件下的桩基施工

埠阳庄大桥实际地质状况复杂,重点介绍该桥桩基施工中的相关准备工作以及对问题的分析和处理措施,为同类地质条件下梁桩基施工提供借鉴经验。     

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。