磁悬浮列车绝对定位系统的研究

针对磁悬浮列车运行时与轨道不接触的特殊性,利用电磁感应原理,提出一种采用位置标志板形式的绝对定位系统。对利用位置标志板进行检测的原理进行了介绍与分析,给出了求解接收线圈磁感应强度的计算公式,详细分析了在位置信息解码单元中对列车位置信息进行逻辑解码的过程。试验结果验证了系统的可行性。     

工长用轨检小车的研制

主要提出养路工长用轨检小车的研制方案,包括设计 要求、测量原理、机械机构形式、电气设计要点、数采软硬件功 能等。     

电力机车制动机车载运行记录诊断系统

介绍一种用组态软件实现的机车车载运行检测记录系统的结构,阐述系统协议文件的设定以及怎样利用协议文件来画出电气线路和空气管路图,描述了系统对故障的判断功能。仿真表明:系统能够用很直观的方式显示机车运行时制动系统的运行状态和故障的产生时间、位置和次数。     

钢管混凝土拱桥钢结构焊接检测方法探讨

介绍了汾河大桥钢结构焊接检测分类、检测标准、检测方法和焊缝质量等级评定。     

斜拉桥结构损伤识别的概率可靠度法

基于静力响应测试数据,结合正则化技术和假设检验,提出了测试数据不完整且存在观测噪声条件下斜拉桥结构损伤识别的概率可靠度法。采用正则化技术降低斜拉桥结构损伤识别问题的不适定性和求解困难,综合利用参数估计和数据摄动技术获得结构参数的统计特征,在此基础上根据假设检验识别损伤位置和损伤程度。以一斜拉桥结构的损伤识别仿真分析验证了方法的正确性和有效性。研究表明,所提出的方法能够在测试数据存在观测噪声的情况下准确识别损伤。     

人工神经网络在舰船火灾探测中的应用

舰船火灾探测系统的目的是能及时、准确地探测到舰船上发生的火灾。提出一种利用人工神经网络的舰船火灾探测方法，通过设计三层反向传播（BP）人工神经网络模型，使用改进的BP算法——LM算法，对多传感器（温度传感器、烟雾传感器和CO传感器）同时探测到的数据进行智能化处理。仿真结果表明：基于人工神经网络的舰船火灾探测系统能及时、准确地识别各种火灾信号。     

提速客车盘形制动轮对轮座镶入部超声波探伤工艺探讨

介绍了提速客车盘形制动轮对轮座镶入部超声波探伤工艺,分析了探伤过程中存在的实际问题,并提出了相应的改进措施与建议。     

铁路车辆轮对自动检测系统的研制

介绍一种可自动测量铁路货车轮对参数并实现计算机自动化管理的系统，对其结构和一些关键技术作了较详细的分析。分析表明，该系统采用工业控制计算机、精密传感器、摄像机以及各种先进的测试和控制技术，对包括轮对外形尺寸、轮对踏面擦伤、剥离以及车轴、轮对和标志板的标记等20多个参数实现动态在线测量，大大提高了工作效率和检测精度，解决了目前轮对测量手段落后、测量误差大、劳动强度高等问题。     

荧光磁粉探伤法在船舶检验中的应用

阐述了荧光磁粉探伤法在船舶检验中的应用经验，介绍了该探伤法的特点、常用设备和材料、以及操作中应注意的事项。对荧光磁粉探伤法及普通磁粉探伤法进行了对比试验，介绍了其试验结果，证明前述探伤法的灵敏度优于后者。并列举了一些应用实例，以证明在船舶检验中应用荧光磁粉法探伤的有效性。     

Multimode trip information detection using personal trajectory data

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.