公路工程投标项目风险分析的人工神经网络模型

对投标项目进行风险分析是投标前期决策的重要环节.本文通过投标项目风险因素辨识,建立了基于BP神经网络的风险分析模型,用MATLAB5.3开发了分析软件,为投标人在进行投标决策时提供了一个有效的风险分析工具.     

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.     

Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.     

基于BP神经网络的砂泥岩互层地质爆破参数优化研究

砂岩、泥岩互层地质在黄土地区路基工程中比较常见,其软硬相间的特性使得爆破参数设计困难,工程实践中难以达到理想的爆破效果。本文提出一种基于BP神经网络的爆破参数优化方法,该方法以影响爆破效果的主要因素作为网络输入参数、以爆破效果作为输出参数、采用搜集的数据样本作为训练和检测样本建立BP神经网络。以通过理论计算得出的爆破参数为基础,利用正交试验法筛选出16种试验方案。基于训练好的神经网络预测爆破效果,从中选择爆破效果较好的5组方案进行试爆,并根据试爆效果确定最佳爆破方案。该方法应用于横山车站路堑工程施工,效果良好。     

浅谈氢燃料电池电动汽车布置设计

本文结合作者实际工作经验,介绍了某氢燃料电池电动汽车的电堆及其附件系统的布置设计,包括空气供应系统、氢气供应系统和电堆热管理系统,积累经验,供后来者参考。     

Vehicle classification from low-frequency GPS data with recurrent neural networks

The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results.     

基于RBF神经网络的全船结构载荷平衡调整方法研究

在全船结构有限元直接计算过程中,各计算工况下施加在模型中的外载荷必须处于平衡状态,否则会得到错误的计算结果。本文基于RBF神经网络提出一种全船结构载荷平衡调整计算方法,通过不同载况下重力的合力与合力矩计算船舶平衡状态下的吃水、纵倾角和横倾角;在PATRAN中使用PCL（PATRAN Command Language）编制了辅助计算插件,使用MATLAB神经网络工具箱实现平衡浮态的自动计算。并以3条典型船舶在静水、迎浪中拱、迎浪中垂等工况下的载荷平衡调整为例,验证本方法的正确性。     

基于DSC的欠驱动船舶路径跟踪神经滑模控制

为进一步提高船舶路径跟踪的准确性和稳定性,针对欠驱动船舶易受外界环境的干扰和模型不确定等问题,提出一种基于动态面技术的神经网络滑模控制策略。在虚拟船逻辑制导算法的基础上,结合反步法对虚拟控制律进行设计,引入动态面技术对虚拟控制律的导数进行估计,避免因直接求导而增加计算负担。在动力学回路设计中,将径向基神经网络与滑模控制相结合,设计神经网络权重的自适应律,实现对系统非线性项的在线估计和对实际控制律的设计。采用Lyapunov直接法证明闭环系统的稳定性,并对一艘长为32m的单体船进行仿真试验。结果表明,该控制策略可行,能实现对欠驱动船舶路径的有效跟踪。     

PEM燃料电池透氢电流密度测试的误差分析

质子交换膜燃料电池(PEMFC)的寿命和耐久性是制约其商业化发展的主要因素。准确表征氢气对质子交换膜(PEM)的渗透能力有助于电池的设计安全和运行安全,提高电池的寿命和耐久性。本文主要对PEMFC透氢电流密度的测试误差进行了分析,发现膜厚度、膜穿孔、膜短路以及测试气体压力和湿度,均会对该测试结果带来误差。     

EXPERIMENTAL STUDY OF COCHLEAR HAIR CELL LOSS IN THE GUINEA PIG

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