Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters. Artificial neural networks model exhibits higher performance than traditional statistical model for predicting deformation modulus and coefficient of subgrade reaction.     

Parameter sensitivity study of the Nelder-Mead Simplex Method

This paper presents a parameter sensitivity study of the Nelder-Mead Simplex Method for unconstrained optimization. Nelder-Mead Simplex Method is very easy to implement in practice, because it does not require gradient computation; however, it is very sensitive to the choice of initial points selected. Fan-Zahara conducted a sensitivity study using a select set of test cases and suggested the best values for the parameters based on the highest percentage rate of successful minimization. Begambre-Laier used a strategy to control the Particle Swarm Optimization parameters based on the Nelder Mead Simplex Method in identifying structural damage. The main purpose of the paper is to extend their parameter sensitivity study to better understand the parameter’s behavior. The comprehensive parameter sensitivity study was conducted on seven test functions: B2, Beale, Booth, Wood, Rastrigin, Rosenbrock and Sphere Functions to search for common patterns and relationships each parameter has in producing the optimum solution. The results show important relations of the Nelder-Mead Simplex parameters: reflection, expansion, contraction, and Simplex size and how they impact the optimum solutions. This study is crucial, because better understanding of the parameters behavior can motivate current and future research using Nelder-Mead Simplex in creating an intelligent algorithm, which can be more effective, efficient, and save computational time.     

基于Python的实时嵌入式软件测试脚本

基于Python语言设计一种实时测试脚本,开发扩展模块,实现测试脚本与嵌入式软件仿真测试环境（ESSTE）的接口。该测试脚本使用简便,实时性高、描述能力强、具有良好的可复用性,目前已成功应用到ESSTE中。对多个实时嵌入式软件进行测试,结果验证了该测试脚本的正确性和实时性。     

一致性测试的静态需求测试研究

针对一致性测试中的静态需求测试通常采用手工方法而产生的效率低问题,提出一种基于统计学习的方法,包括静态需求的关联规则表示,并对采样的协议数据进行关联规则挖掘,如果得到的规则与需求规则一致,静态需求就可以得到统计上的测试检验。该方法在OSPFv3协议测试中取得了较好的效果。     

一种改进的GUI测试框架DART

针对当前的图形用户界面自动化测试中存在的局限性,提出一种每日自动回归测试（DART）框架的改进方案。研究DART流程、事件流图,包含了事件关联图的方法,实现对事件流图的优化,并采用权重选择法对DART方法进行改进,使之成为一种高效、简洁的自动冒烟测试框架。     

TPC-H自动测试工具TPCHDriver的研究

TPC—H基准测试可以比较不同数据库系统之间的查询性能差别,它的测试结果已经成为评价DBMS和服务器的重要标准。该文开发了自动化的TPC—H测试工具TPCHDriver,该工具应用面向对象设计、Java语言开发,图形用户界面,实现了从数据装载到Power测试和Throughtput测试、生成测试报告整个过程的自动化。简述了TPC—H基准的测试模型、TPCHDriver程序的结构和主要功能,阐述了TPCHDriver的关键实现技术。     

认证测试理论的扩展和应用

通过在串空间中加入新的密码原语,重新定义认证测试的前提条件、扩展认证测试理论,使其能够分析有签名运算的密码协议。通过对基于Diffie—Hellman密钥交换的安全协议进行合理的假设,使其参数项具有现时值的性质,从而可以直接使用扩展的认证测试理论进行分析。使用扩展后的认证测试分析一个包含签名和Diffie—Hellman密钥交换的密码协议,得出该协议满足认证特性。     

基于正则表达式的测试数据自动生成技术

软件测试是提高软件可靠性、保证软件质量的重要手段。数据自动生成是测试自动化的重要组成部分。提出一种基于正则表达式的测试数据自动生成技术,生成的数据应该具有随机性、可控制、可定义、有意义、无错误等特性,特别是对大字段／专有类型等特殊类型的支持,可以解决目前测试数据自动生成存在的问题,具有良好的扩展性。     

混沌敏感性在图像加密中的2种应用

混沌系统的结构参数敏感性、初值敏感性和以同一分布遍历各态的特性较好地对应密码系统的一些基本特性。分析结构参数和初始值变化对混沌序列敏感性影响,得到混沌序列敏感性效应是一个积累过程。将混沌序列按照敏感性划分为混沌敏感性前后期序列并给出界定原则。探究混沌序列敏感性在图像加密时的2种不同应用。     

Applying eco-visualisations of different interface formats to evoke sustainable behaviours towards household water saving

The shortage of water resources is a global issue. This study attempts to utilise eco-visualisation interface design to affect users’ water-usage behaviour. Three interface proposals were designed in this study: numeric, water droplet (abstract symbol), and swimming fish (animated image). These three different interface formats range from abstract to figurative and from neutral to affective. A total of 93 subjects were divided into three groups and randomly performed designated dishwashing tasks twice. One of the tasks was completed without the assistance of any interface, whereas the other involved applying one of the three interface formats with eco-visualisations. In this study, the Self-Assessment Manikin scales, the System Usability Scale, and the Questionnaires for User Interaction Satisfaction were used to measure the emotional dimensions and perceived usability and to record the differences in water usage volume. The results indicated that the emotional valence evoked by the animated image interface was significantly higher than that by the numeric interface. The effect of the numeric interface formats on the subjects focused on emotional arousal, whereas other interfaces emphasised emotional valence. With the aid of the eco-visualisations of interface design, the evoked emotional response and enhanced usability could help improve sustainable behaviours towards water saving.