城市道路指路标志标准化设计实践——以宁波市为例

为了向交通参与者提供准确、及时的路径引导信息,城市道路指路标志在版面设计、点位设置、信息选取与表达等方面应形成一个标准化的设计体系。宁波市在城市道路指路标志设计实践中,通过解读现有国家规范,结合宁波市实际路网、交通特性,对指路标志的版面形式、点位设置、标志套用等作了细化规定。在指路信息选取上,通过统筹道路、桥梁、交通枢纽、主要地区、旅游景区等各类交通信息,构建了指路标志的信息层级体系,并设定了信息的分层表达规则,以保障交通信息传递的有效性、连贯性和层次性。     

A hybrid Bayesian Network approach to detect driver cognitive distraction

Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.     

基于模糊聚类理论的TBM施工围岩可掘进性分级预测模型

文章针对硬岩掘进机(TBM)在复杂地质条件下的可掘进性,进行了系统及定量的研究;基于模糊聚类理论和施工样本数据分析,建立了以掘进速率为分级指标,包括岩石单轴抗压强度、岩石完整性系数、围岩结构面与隧道轴线夹角和渗水量四项性质指标的可掘进性分级预测模型,将TBM施工围岩可掘进性分为好、一般和差三个性能等级;并在此基础上进一步细化模型粒度,以提高模型的精度和地质适用性。将所建模型应用于西秦岭隧道和大伙房水库输水隧洞工程实际工程效果表明,TBM掘进速率与由模型预测的掘进速率基本相吻合,验证了掘进性分级预测模型的可行性、科学性和有效性,进而对TBM的选型、设计和施工提供了重要的理论依据。     

新型城镇化建设中的县域公路网规划

基于新型城镇化对县域公路网的新要求,深入分析了县域公路网规划的关键问题,提出从对外通道和内部网络两方面布局县域公路网的方法,对外建立主通道、内部运用层次布局法构建了干路层-联网层-社区层的层级路网,并给出了对外主通道与干路层、联网层、社区层统筹规划的具体方法,可为县域公路网科学规划与建设提供一定的依据,支撑县域科学发展和新型城镇化建设.     

边坡监测中FCM聚类算法的应用研究

利用模糊聚类分析方法将边坡进行安全等级分类,在提高变形监测效率的基础上可降低工作量。FCM聚类分析算法更具科学性,是理论最为完善且应用较多的算法。利用边坡的坡角、高度、边坡所处的水文地质条件作为影响边坡稳定的影响指标,来对边坡监测点的安全等级分类,从而降低工作量,提高工作效率。     

基于节点兴趣的混合P2P网络

针对P2P网络在搜索资源时往往无法快速准确的定位目标,提出一种节点兴趣分组以及分层结构的P2P网络结构.网络拓扑在分布时将兴趣相似度高的节点放到一个兴趣组中,节点在搜索资源时就会更准确的定位在离自己距离近的同时也是兴趣最相似的节点上,减少了查询消息的路由转发.同时应用分层的管理,对整个网络中的节点功能细化,使得网络的稳定性和可扩展性得到优化.实验结果表明,采用兴趣分组和结构分层的网络访问延时得到有效控制,搜索成功率也得到提高.该算法在缩短路由跳数的同时还提高了搜索的精准率.     

Outlier mining based abnormal machine detection in intelligent maintenance

Assessing machine’s performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine. In this paper, an outlier mining based abnormal machine detection algorithm is proposed for this purpose. Firstly, the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor (CBGOF) is presented. Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed. The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines. Finally, a comparison of mobile soccer robots’ performance proves the algorithm is feasible and effective.     

基于居民出行需求的城市公共交通运营水平综合评价

分析影响城市公共交通运营水平的因素,在充分考虑居民出行需求的基础上,遵循科学性、合理性、客观性、完备性的原则,结合兰州市城市交通实际,建立城市公共交通运营水平的评价指标体系。运用灰色聚类分析法,构建城市公共交通运营水平评价的层次结构,建立综合评价数学模型,对兰州市公共交通运营水平纵向进行全面评价。     

HTN规划中面向多计划生成的顺序任务分解算法

顺序任务分解算法（OTD）是层次任务网规划（HTN）中的一种高效求解算法．由于算法中的计划生成采用一次性回溯机制,每次求解过程只能产生一个可行计划．文中提出了一种能够快速生成多个可行计划的回溯算法．该算法采用分段回溯的计划生成机制,充分利用了求解过程中生成的局部解序列,从而能够一次性地快速生成多个可行计划,为寻求优化的计划和进行计划的评估提供更为有效、灵活的支持．     

使用分类和聚类提高搜索引擎的可用性

由于很多Web用户面对搜索引擎搜索出来的成千上万条结果记录没有耐心一页一页地翻找,搜索出来的内容就不能得到有效的利用.自动分类聚类技术将是搜索引擎进一步改善搜索效率的一个很重要的方面.介绍联合使用分类聚类技术来改善搜索结果的表示形式,提高搜索引擎的可用性.