数码摄影技术网络课程设计与实践

基于混合式学习模式,结合数码摄影技术网络课程建设与应用,阐述如何进行网络课程功能、模块、要素等设计,并对教学与实践进行分析和反思,为其他网络课程的建设提供思路。     

基于项目的高职大学英语混合学习模式研究

项目教学和混合学习是目前教育界盛行的学习方式和方法。针对高职院校的特点,提出将项目教学与混合学习模式进行有机融合来改革大学英语教学的思路,并重点探讨了学习模式的设计和实施。实践表明,基于项目的混合学习模式有助于增强学生的学习动机,培养学生的语言能力和自主学习能力。     

机织间隔织物柱纱高度的确定

机织间隔织物的厚度由柱纱的高度决定.为了确定影响柱纱高度的因素,从几何结构角度出发,定义了影响机织间隔织物柱纱形态的5个结构参数,并建立了结构参数间的几何关系.通过将柱纱高度计算值同实测值的比较,发现计算值与实测值吻合得较好,证明所建立的柱纱结构参数间几何关系能够很好地反映实际情况.     

电厂混煤燃烧最优掺配比例的确定

根据电厂不同煤种的指标数据、锅炉燃烧的实际情况以及外部环境的要求,应用线性规划理论,对电厂混煤燃烧的配比问题进行了研究,得出了电厂混煤燃烧时各种煤质的最优掺配比例．研究成果对提高电厂锅炉安全经济运行,减少二氧化硫对环境的污染,有着重要的作用．     

电路原理全英文混合式教学模式研究与实施——以大连民族大学为例

教学模式改革是高等教育的一个永恒课题。结合电路原理全英文教学的特点,以学生为中心,全面实施混合式教学模式,引入“雨课堂”等新型教学工具,改革课程评价方式,强化学习过程考核,学生的学习效果得到较大程度提升。     

纯毛精纺针织绒的工艺研究

简要介绍了全毛精纺针织绒生产工艺方面的探索和实践。主要讨论了全毛针织绒的生产流程,原料的选择、毛油合理的油水比例,纺纱设备的工艺调试对产品生产和成品质量的影响,以及在生产中采用的主要技术措施。     

Torsional Models of Two - ply Yarn

In this paper,the vector equation of a generaliseddoubly wound helix was derived.Treloar's pliedyarn geometry could be obtained as a special case ofthe generalised doubly wound helix.The shortest fi-bre length around the surface of a helical tube(formed by fibre helices)was determined by apply-ing variational principles.The fibre length as calcu-lated by using Treloar's geometry was compared in-directly with the shortest possible fibre length at dif-ferent levels of yarn deformation when some of Tre-loar's rigid geometrical constraints were relaxed.Anew idea based on non-concentric circles was intro-duced to approximate the fibre helix movement in atwo-ply yarn.A torsional model of two-ply yarnwas developed and the theoretical predictions werecompared with some preliminary experimental re-sults.     

驼绒/水溶性维纶包芯纱的包覆效果及性能

采用改进的环锭纺细纱小样机,试纺了9种不同规格参数的驼绒/水溶性维纶包芯纱,考察芯纱张力、芯纱含量和捻度等因素对包覆效果的影响,并测试包芯纱的拉伸性能、条干均匀度和毛羽指数等指标,获得了最佳纺纱工艺参数.研究结果表明:适中的芯纱张力可以提高包芯纱的包覆效果;芯纱含量越小,纱线包覆效果越好,断裂强力越高;捻度越大,包覆效果越好,毛羽指数越小,但断裂强力先增加后降低.优选工艺参数:芯纱含量为19.1%~19.9%;捻度为300~325捻/m;在芯纱不断裂的情况下,提高芯纱张力有利于改善包覆效果.     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.     

环锭纺纱线气圈的研究

用圆柱坐标系建立纱线微段的微分方程,用数值方法求解可获得气圈形态及其纱线受力的精确解。用解析方法建立气圈形态及其纱线受力的近似解析表达式,可反映气圈各参数对形态或受力的影响,其分析过程清晰、简捷的特点显著。两种计算结果的相对误差甚微。