在强夯法处理湿陷性黄土路基施工中,为了及时掌握和了解强夯加固深度,并对加固效果进行正确评估,利用测振仪进行现场测振,通过对测得的振动波参数进行数值分析,获得强夯法有效加固深度计算公式,同时利用重力触探方法对强夯后路基承载力进行平行检验.结果表明,有效加固深度计算公式与经验公式有较好的一致性,采用测振仪测控强夯法处理湿陷性黄土路基加固效果是一种有效的新方法

一.前言 空间句法的由来 空间句法的字面含义 空间句法的特质 二.解读空间句法 空间句法的相关理论 空间句法的技术手段 三.空间句法的应用 基于宏观层面——城市规划的应用 基于微观层面——建筑设计的应用 四.结语 空间句法的学术意义 空间句法的定位

矩量法(简称MoM)，就其数值分析而言就是广义Galerkin(伽略金)法。矩量法包括两个过程，离散化过程和选配过程，从而把线性算子方程转化为矩阵方程。 &4.1 矩量法概述 &4.2 基函数与权函数选择 &4.3 MOM法应用举例

Origin 是 Microcal Software 公司推出的一个功能强大的数据分析/科学绘图软件， 作为 Windows 应用程序，具备了 Windows 所提供的诸多方便直观的特点，尤其适用 于那些经常进行大量数据处理及科学绘图的人员使用。现流行的版本为 2.8, 3.0, 3.5, 5.0, 6.0, 6.1, 6.1 版本的特色主要有： 1) 动态用户界面：计划组织；用计划管理器组织计划，对选择的窗口批处理

为了研究山区高速公路深路堑边坡的稳定性问题,基于三维非线性应力及位移有限元计算,对边坡稳定性进行了数值分析,并与Sarma法的计算结果进行了对比.结果表明,有限元法解决边坡问题可以起到其他方法不可替代的重要作用

假设，检验统计量，显著水平，功效 两种假设下的统计检验 纽曼-皮尔森引理 如何构造一个检验统计量 Fisher甄别函数与神经网络

8.1 Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support 8.2 Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem 8.3 Describe the current and future use of cloud computing in business analytics 8.4 Describe how geospatial and location-based analytics are assisting organizations 8.5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8.7 Identify key characteristics of a successful data science professional

7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics

6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation

5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis