自我评价在大学英语教学中的应用研究

小组合作学习有利于增强学生的学习兴趣和责任感,是一种有效的课堂教学组织形式。该研究探索小组合作学习中五要素之一的“小组自我学习评价和反馈”在大学英语教学中所起到的作用。实验采用了调查问卷、水平测试等试验工具进行调查和收集数据。结果表明小组合作学习有助于增强学生在语言学习中的元认知意识;其中小组自我评价在增强学生的元认知意识和促进小组活动方面起导航作用;能促使学生更多地参与学习过程,从而提高学生学习语言的效果。     

数字微波140Mb/s、16QAM系统基带自适应均衡器

目前国内外广泛使用的数字微波140Mb/s、16OAM系统,由于选择性衰落造成的码间干扰,一般都采用基带自适应均衡器予以补偿。本文研究了基带横向均衡器和判决反馈均衡器在不同抽头个数时最小相位衰落和非最小相位衰落的均衡能力、并分析了定时相位偏差对均衡器的影响。     

线弹性有限元的自适应加密与多重网格法求解

基于Ｚｉｅｎｋｉｅｗｉｃｚ－Ｚｈｕ误差估计方法，自适应策略以及分子表结构，研制出功能强大的自适应多重网格有限元程序。该程序可对任意曲线组成的计算区域进行全局与局部加密。以应力集中问题为例，展示了自适应多重网格有限元的主要特征。研究结果表明：本文采用的误差枯计方法，自适应策略对线弹性问题的有效的，自适应多重网格有限元求解应力集中问题具有精度高，收敛速度快的优点。     

高校图书馆工作人员素质提高策略初探

现代社会的发展，给高校图书馆带来巨大的压力与挑战。图书馆员的整体素质如何，将关系到图书馆事业的未来发展。高校图书馆应从提高其馆员整体学历水平、建立和完善岗位培训制度、使全体馆员牢固树立终身学习的观念三方面来提高工作人员的整体素质，推动图书事业的持续发展。     

关于双语课程《数字电子技术》学习状况的调查与思考

以问卷调查的形式,通过描述性统计和定量分析,对我校双语课程《数字电子技术》学习状况进行了调查和分析,在此基础上,对双语教学提出了一些建议.     

研究性学习中的心理学思想探析

研究性学习视学生为一个完整的人，注重学生个性的培养，重视学生潜能的开发以及整体素质的协调，这其中渗透着激发学生学习动机、使学生达到自我实现等心理学思想．该文对研究性学习的几个特点的心理学思想进行了分析。     

大学生学习焦虑心理研究

通过分析大学生学习焦虑心理的成因以及学习焦虑控制问题。论述了大学生学习焦虑心理与学习成绩的关系。     

Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components     

Schedule-an expert-like system for machine scheduling

The construction of an expert-like system for machine scheduling called SCHEDULE is presented. Essential parts of SCHEDULE were developed by students in a laboratory course Operations Research on Microcomputers at the University of Karlsruhe, Germany. SCHEDULE consists of the components data base, knowledge base, inference engine, explanation facility, dialog component, and knowledge acquisition component. The knowledge base contains an algorithm base for solving different types of scheduling problems. To establish the rules of the knowledge base the well-known three-field classification of deterministic machine scheduling problems and the concept of the reduction digraph are exploited. Experiences gained during building and demonstrating SCHEDULE are reported.     

Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing

Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.