Kernel Factor Analysis Algorithm with Varimax

Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.     

移动模架制梁施工技术

移动模架施工技术是世界桥梁施工的先进技术，成功应用于石太客运专线，克服了大吨位箱梁预制、运输和架设的困难。本工程的移动模架采用整体液压系统驱动，工效高，详细了介绍移动模架的工作原理和主要施工工艺。     

A knowledge-transfer-based learning framework for airspace operation complexity evaluation

A sector is a component airspace whose operation is allocated to an air traffic controller. The operation complexity of a sector plays a critical role in the current Air Traffic Management system, e.g. it determines the workload volume of air traffic controllers and serves as a reliable index for airspace configuration and traffic flow management. Therefore, accurately evaluating the sector operation complexity is a problem of paramount importance in both practice and research. Due to numerous interacting factors, traditional methods based on only one single complexity indicator fail to accurately reflect the true complexity, especially when these factors are nonlinearly correlated. In light of these, the attempt to use machine learning models to mine the complex factor-complexity relationship has prevailed recently. The performance of these models however relies heavily on sufficient samples. The high cost of collecting ample data often results in a small training set, adversely impacting on the performance that these machine learning models can achieve. To overcome this problem, this paper for the first time proposes a new sector operation complexity evaluation framework based on knowledge transfer specifically for small-training-sample environment. The proposed framework is able to effectively mine knowledge hidden within the samples of the target sector, i.e. the sector undergoes evaluation, as well as other sectors, i.e. non-target sectors. Moreover, the framework can properly handle the integration between the knowledge derived from different sectors. Extensive experiments on real data of 6 sectors in China illustrate that our proposed framework can achieve promising performance on complexity evaluation when only a small training set of the target sector is available.     

Prediction of aircraft performances based on data collected by air traffic control centers

Accurate prediction of aircraft position is becoming more and more important for the future of air traffic. Currently, the lack of information about flights prevents us to fulfill future demands for the needed accuracy in 4D trajectory prediction. Until we get the necessary information from aircraft and until new more accurate methods are implemented and used, we propose an alternative method for predicting aircraft performances using machine learning from historical data about past flights collected in a multidimensional database. In that way, we can improve existing applications by providing them better inputs for their trajectory calculations. Our method uses flight plan data to predict performance values, which are suited individually for each flight. The results show that based on recorded past aircraft performances and related flight data we can effectively predict performances for future flights based on how similar flights behaved in the past.     

先张法U形梁快速施工与机械设备配套技术

为满足合同工期紧迫的要求,使1232片U形梁达到快速预制、运输和架设的目的,从施工方案和资源配置着手,紧紧围绕快速施工,介绍梁场的选址、梁场的布置、张拉台座的布置、模板设计、混凝土浇筑、运梁车配备以及大吨位吊车配备等技术研究等内容,采用上述技术方案,在10个月内成功完成了1232片U形梁的预制和架设施工任务,确保了合同工期。     

敞开式全断面隧道掘进机通过不良地质地段的措施和施工方法

研究目的:铁道部1997年引进的两台TB880E型TBM由德国Wirth公司生产,该机型的结构及技术性能参数以西安安康铁路秦岭特长隧道为设计对象,主要适用于硬岩隧道的掘进施工,属敞开式硬岩TBM。TBM如何通过软弱围岩是一个世界性的难题。研究方法:结合西安南京铁路桃花铺1号隧道的工程实践,研究预防TBM施工事故的措施和施工方法。研究结论:提出了“超前探测,支护紧跟,调整参数,适速推进,减少扰动”20字方针。对于施工过程中出现的各种坍方如何处理进行了说明,总的处理措施是“及时喷锚,加密钢架,搭焊钢管,网板结合,封闭坍腔,回填密实”。TBM顺利、安全通过不良地质地段的措施和方法在桃花铺1号隧道的掘进后期得到很好的验证。     

铁路分局装车计划智能决策支持系统

在分析了铁路分局日间装车计划工作的特点后,指出掌握车流信息只是装车计划的基础,应建立装车计划智能决策支持系统(IDSS).铁路分局每日的装车计划要考虑装运货物的经济效益,文中提出了装车计划优化数学模型,据此来审批承认车,提高运输经济效益.     

铁路自动检票机的设计与实现

以进站检票机为研究对象,介绍铁路自动检票机的设计与功能实现.内容涉及系统的总体结构及其各主要功能的实现方式.     

Detection and Classification of Vehicles by Measurement of Road-Pavement Vibration and by Means of Supervised Machine Learning

Road vehicle detection and, to a lesser extent, classification have received considerable attention, in particular for the purpose of traffic monitoring by transportation authorities. A multitude of sensors and systems have been developed to assist people in traffic monitoring. Camera-based systems have enjoyed wide adoption over the last decade, partially substituting for more traditional techniques. Methods based on road-pavement vibration are not as common as camera-based systems. However, vibration sensors may be of interest when sensors must be out of sight and insensitive to environmental conditions, such as fog. We present and discuss our work on detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning. We describe the entire processing chain from sensor data acquisition to vehicle classification and discuss our results for the task of vehicle detection and the task of vehicle classification separately. Using data for a single vibration sensor, our results show a performance ranging between 94% and near 100% for the detection task (1340 samples) and between 43% and 86% for the classification task (experiment specific, between 454 and 1243 samples).     

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.