中文网页的自动分类

提出了一种中文网页自动分类的方法，主要包括中文网页的自动抓取、中文分词、特征选取、贝叶斯机器学习与分类等功能模块。该系统可以很好地实现一个中文网页的自动分类，且系统中的分类器具有较高的分类质量。     

学习动机和学习策略对英语学习的影响

本文通过对中学生和大学生英语学习的动机和策略的调查 ,揭示了动机、策略和学习成效的关系。     

基于组合深度学习的快速路车道级速度预测研究

随着物联网、云计算和大数据在智能交通领域的普及应用,传统的以道路断面为研究对象的预测方法已经无法满足智能网联技术发展的需求.本文以车道断面为研究对象,提出一种基于组合深度学习(Combined Deep Learning,CDL)的城市快速路车道级速度预测模型.该模型利用基于信息熵的灰色关联分析提取空间特征变量,采用长短期记忆神经网络提取空间特征变量的时间特征,并利用门限递归单元神经网络得到预测结果.通过北京市东二环路车道断面实测微波数据验证发现,提取车道交通流的时空特征,CDL模型能够很好地拟合不同车道不同时段的速度变化趋势,可有效地实现车道速度的单步及多步预测,且该模型的预测精度和稳定性均优于传统预测模型.     

基于BP神经网络的货车车号识别方法研究

针对货车车号的字体不规则、单字有断裂的特点,提出基于图像预处理的动态字符分割和提取算法,准确地对车号图像进行车号区域提取和车号单字分割.利用Hilditch细化算法对单字进行细化,提取出能表征数字的结构特征向量.设计出BP人工神经网络,数字的结构特征向量作为BP网络的输入,用经过训练的BP网络进行货车车号的识别.     

对外汉语专业的英美文学选读课教学初探

英美文学选读课有助于对外汉语专业的学生全面掌握语言文字的魅力,领略不同文化的精神内涵,肩负起自身的文化使命。笔者通过两年多的教学实践,以教学活动过程为背景,从预热、选材、学习、反思四个阶段,总结出了几点经验。     

Real time location prediction with taxi-GPS data streams

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS-enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a damped window model namely, Multivariate multiple regression, Spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. We found that the Multivariate multiple regression method has the best performance in terms of prediction accuracy but the Spherical-spherical regression method is the best performer when we take into account the accuracy time trade-off criterion. The next pickup location problem, where we are interested in predicting the next pickup location for a taxi given the dropoff location coordinates of the previous trip as input is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method gives better performance than the rest when we consider prediction accuracy but the Spherical-spherical regression method is the best performer when the accuracy-time trade-off criterion is taken into account.     

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.     

A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Fully automated vehicles could have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles.This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem.In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a “short-sighted” user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered.     

Statistical and machine learning approach for planning dial-a-ride systems

Door-to-door transportation service for elderly and persons with disabilities is often called dial-a-ride (DAR), and is usually provided by transit agencies through private contractors. Growth in DAR ridership is reported across the United States and this tendency will likely continue due to aging population. Such trends encourage development of models that can provide decision support in planning new DAR systems or expanding existing ones. Several statistical models were previously developed to predict the required DAR system capacity, given various characteristics of the service region, level-of-service requirements and operator constraints. Our work contributes to this line of research by proposing statistical and machine learning approaches that provide more accurate predictions over a wider range of scenarios. This is accomplished through transformation of variables and application of generalized linear model and support vector regression. Proposed models are built into an online tool that can help transit planners and policy makers: (a) estimate the capacity and operating cost of a DAR system needed to provide the desired level of service, (b) explore tradeoffs between system costs and levels of service, and (c) compare the cost of providing DAR service with other transportation alternatives (e.g., taxi, conventional transit).     

工学结合模式下高职院校思想政治理论课教学改革探析

思政课一直以来受到中央和教育部高度的重视,同时也肩负育人重任。工学结合模式对思政课的教学提出了更高的要求,有了更多的期待,但是目前高职院校的思政课存在着教学内容的独特性有待提高、教学形式的多样性有待加强、教学目的的针对性有待明确等问题,须改进教学模式,才能适应不断发展的教学环境。高职院校应在遵循理论结合实际、素质教育、明确教育理念等原则的基础上进行教学改革,使思政课成为学生喜爱并终身受益的课程。