基于工况双向压力法的商用车空调检漏技术

为解决汽车空调的冷媒泄漏问题,从全系列商用车空调系统的结构和原理研究入手,通过对空调系统的泄漏问题进行调查和分析,建立了空调泄漏失效模式,利用真空度和工况压力差值关系判断空调泄漏质量,探索工况双向压力法空调检漏技术,提高了空调检漏的可靠性、准确率及效率,使空调泄漏的售后赔偿下降60%以上,提升其自身品质质量。     

海底介质模型及其声能透入机理研究

海底建模是海洋声探测及海洋混响研究的重要课题.文章用Biot多孔介质、粘弹性介质和液态介质三种海底模型对声波在海底的反射和透射进行了理论计算分析,具体讨论了纵波、快纵波、剪切波以及慢纵波对声透射的贡献,并对三种海底模型中纵波、剪切波的反射、透射进行了比较,得到对声学问题海底建模有实际意义的结果.     

浅析影响轮对超声波探伤的因素

介绍了影响轮对超声波探伤的主要因素及相应对策。     

痕量爆炸物探测仪研制

介绍了离子迁移谱仪的基本原理及在爆炸物监测方面的应用.提供的自制离子迁移谱仪可定量分析TNT、黑索金等样品.采用液体和固体2种方式进样,快速有效,定量准确,适用性广,对TNT和黑索金的检测限达到10 ng.对离子迁移谱仪的离子门、迁移电场设计进行了详细探讨.     

FFT在机车故障检测记录系统中的应用

针对当前机车故障诊断记录系统故障诊断能力提高和通用性扩展的需要,结合FFT算法特点,采用TMS320VC33实现快速傅立叶变换,使系统具备了频谱分析的能力,可以应用到谐波分析和振动分析等相关领域。     

汽车技术检测的规范与发展

在分析我国汽车技术检测存在的问题基础上,提出了规范和发展我国汽车技术检测的方法和途径。     

基于序列互相关特性和SVM的入侵检测研究

通过刻画特权进程的系统调用来进行入侵检测已被广泛研究，采用的大多是人工智能方法，如支持向量机、隐马尔可夫模型和神经网络等。针对这种基于人工智能的入侵检测系统，本文提出应用序列互相关特性选择训练数据的方法。序列的互相关特性是刻画序列之间的相互关系的重要手段，使用具有一定特性的序列来训练人工智能模型，可以提高入侵检测的效率。在本文中，使用了支持向量机（SVM）来评价序列的互相关特性在模型训练中的作用。实验仿真说明，这种方法可以有效的提高入侵检测率。     

在役预应力混凝土结构损伤检测与评定技术综述

从混凝土材料和预应力钢筋的检测与评定以及预应力混凝土结构损伤检测三个方面,总结了近些年来在役预应力混凝土结构损伤检测技术的发展成果,指出了存在的主要问题和缺陷,并揭示了该研究领域今后的发展趋势。     

Effective arterial road incident detection: A Bayesian network based algorithm

Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.     

HDJ-I型货车运行故障动态检测系统

介绍了HDJ—I型货车运行故障动态检测系统(TFDS)的主要功能、特点、组成及工作原理。