乳胶涂料中填料粒径测定与分散剂的选择

介绍了重力沉降法和容量沉降法两种测定乳胶涂料中填料粒径的方法。通过比较粉体粒径结果显示：沉降法比激光法更形象直观，更能代表体系粒径真实情况。以滑石粉、重钙、轻钙粉体为基准，采用不同用量、不同类型的分散剂的容量沉降法进行比较得出：用容量沉降法能定量地对分散剂性能做出评价。     

NETEM阅读部分的真实性分析

从真实性的角度出发,利用Bachman的任务特征框架分析了NETEM阅读理解部分对考生交际语言能力的测试.分析表明,NETEM在输入材料方面有较高的真实性,任务涉及的语言能力的真实性也有很大的提高.但从输入与回答之间的关系看,个别题目未能体现交际语言能力的特点,仍有待改进.     

Specific energy optimization in sawing of rocks using Taguchi approah简

This work aims at selecting optimal operating variables to obtain the minimum specific energy （SE） in sawing of rocks. A particular granite was sampled and sawn by a fully automated circular diamond sawblades. The peripheral speed, the traverse speed, the cut depth and the flow rate of cooling fluid were selected as the operating variables. Taguchi approach was adopted as a statistical design of experimental technique for optimization studies. The results were evaluated based on the analysis of variance and signal-to-noise ratio （S/N ratio）. Statistically significant operating variables and their percentage contribution to the process were also determined. Additionally, a statistical model was developed to demonstrate the relationship between SE and operating variables using regression analysis and the model was then verified. It was found that the optimal combination of operating variables for minimum SE is the peripheral speed of 25 m/s, the traverse speed of 70 cm/min, the cut depth of 2 cm and the flow rate of cooling fluid of 100 mL/s. The cut depth and traverse speed were statistically determined as the significant operating variables affecting the SE, respectively. Furthermore, the regression model results reveal that the predictive model has a high applicability for practical applications.     

Optimal blank design based on finite element method for blades of large Francis turbines

Metal pressing process that is widely used in industries has advantages over casting process for producing large Francis turbine blades from thick plates. Prior to the pressing process, blank design is firstly performed to determine flat blanks. The traditional trial and error approach is not applicable to blade design for Francis turbines that are not standard due to hydraulic characteristics of power plant sites. The rapid development of computing technology makes it possible to obtain optimal flat blanks by numerical modelling and simulation. In this paper, inverse finite element approach is investigated for blank design and an elasto-plastic model has been built using the well-known commercial software ANSYS. Numerical simulations for blade unfolding models with thick shell elements, solid elements and shell elements have given results with negligible differences. Unfolding tests with simple geometries have been carried out and the numerical results agree well with the analytical solutions. A large and thick shape of a Francis turbine blade for a hydropower plant has been successfully unfolded by inverse FE model. Sensibility analysis shows that the middle surface of the flat blank is independent of blade thickness. For ensuring the machining of the blade after the pressing process, a new contour is obtained by extending the boundary of the flat blank provided by the numerical model. This research may provide a useful tool for optimal blank design of Francis turbine blades.     

基于积分二次约束时滞不确定系统的H∞可靠性控制

把基于积分二次约束H∞可靠性控制方法引入状态与时滞不确定系统。通过LMI方法导出无扰动时滞系统具有二次约束H∞可靠性控制的时滞依赖标准,降低有关判定系统鲁棒稳定性条件的保守性。通过推导获得参数与时滞不确定系统的时滞依赖标准。通过数例说明所得结论。     

基于最优模糊PID温度控制方法研究

针对某智能大厦空调的温度控制系统,引入了一种新的模糊推理方法,设计了模糊PID控制器.该模糊推理方法将优化和反馈的思想引入推理过程中,依据不同的优化指标进行推理,使推理过程更为有效,论文给出了应用该推理方法的具体过程.以建立起的温度系统模型为基础,设计了基于最优模糊推理的温度系统模糊PID控制器,进行了仿真研究,仿真结果和实际运行表明了该控制方法具有强鲁棒性和良好控制品质.     

以培养复合型人才为目标的计算机专业英语教学体系的研究

本文探讨如何建立以培养具有较高计算机专业知识和较强的英语听、说、读、写等应用能力的复合型人才为目标的计算机专业英语教学体系,尝试改进教学方法,修正课程内容与结构。实践表明,新的教学体系不仅明确了计算机专业英语的教学目的和人才培养目标,更能够使学生在英语中学习信息技术、从计算机专业知识的应用中体会和使用英语。     

Horizontal Voting Ensemble Based Predictive Modeling System for ColonCancer

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.