车辆排放污染的综合控制

随着我国汽车保有量急剧的增加,车辆排放污染已成为亟待解决的重要课题,控制排放污染是一个大型系统工程,文中从车辆、道路交通环境、交通管理3个方面论述了对排放污染的综合控制。     

关于哈尔滨市城市防洪工程的建设情况

简要介绍哈尔滨市城市防洪工程建设情况。     

武隆隧道2#、3#岩溶暗河探测及施工地质条件分析

武隆隧道为渝怀铁路第二长大隧道,岩溶发育是本隧道的最主要特征之一.尤涫?#和3#岩溶暗河,为本隧道最复杂、最困难的地质条件,多次出现大型～特大型岩溶涌水,给隧道施工造成无法抗拒的自然灾害和经济损失.作者详细介绍了2#和3#岩溶暗河的探测技术,并根据探测结果提出岩溶暗河处理措施和建议.     

遗传算法在区段站到发线的应用研究

为解决车站到发线的合理运用问题，采用遗传算建立了到发线运用的染色体结构和适应度函数，对这种复杂的非线性组合优化问题进行了仿真。     

设计-施工总承包在武汉阳逻长江大桥南锚碇工程中的应用

介绍武汉阳逻长江公路大桥南锚碇工程采用设计-施工总承包模式的经验,对相关的招标和项目管理工作进行总结.针对我国的实际情况,分析该模式在工程建设中的适用范围.     

间歇式沥青拌和站冷料供给系统流量的标定方法

以实例介绍间歇式沥青拌和冷料供给系统流量的标定方法和步骤，以确保冷料供给器按目标配合比准确供料，使沥青混合料的生产配比更加接近目标配比，保证拌和成品料的质量。     

Decomposing and Cluster Refinement Design Method for Application-Specific Network-on-Chips

Along with higher and higher integration of intellectual properties (IPs) on a single chip, traditional bus-based system-on-chips (SoC) meets several design difficulties (such as low scalability, high power consumption, packet latency and clock tree problem). As a promising solution, network-on-chips (NoC) has been proposed and widely studied. In this work, a novel algorithm for NoC topology synthesis, which is decomposing and cluster refinement (DCR) algorithm, has been proposed to minimize the total power consumption of application-specific NoC. This algorithm is composed of two stages: decomposing with cluster generation, and cluster refinement. For partitioning and cluster generation, an initial low-power solution for NoC topology is generated. For cluster refinement, the clustering is optimized by performing floorplan to further reduce power consumption. Meanwhile, a good tradeoff between power consumption and CPU time can be achieved. Experimental results show that the proposed method outperforms the existing work.     

朔黄铁路POC语音调度系统服务质量测试方法研究

POC语音调度系统在朔黄铁路运营生产中发挥了重要作用,本文为朔黄POC语音调度系统设计了一种语音服务质量评价指标体系,并详细描述了各指标的计算公式和测试方法,为朔黄铁路LTE系统服务质量检测评价提供指导和支撑。     

Research on Web Page Classification Method Based on Query Log

Web page classification is an important application in many fields of Internet information retrieval, such as providing directory classification and vertical search. Methods based on query log which is a light weight version of Web page classification can avoid Web content crawling, making it relatively high in efficiency, but the sparsity of user click data makes it difficult to be used directly for constructing a classifier. To solve this problem, we explore the semantic relations among different queries through word embedding, and propose three improved graph structure classification algorithms. To reflect the semantic relevance between queries, we map the user query into the low-dimensional space according to its query vector in the first step. Then, we calculate the uniform resource locator (URL) vector according to the relationship between the query and URL. Finally, we use the improved label propagation algorithm (LPA) and the bipartite graph expansion algorithm to classify the unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other Web page classification methods based on query log.