光学薄膜元器件外观特性机器视觉检测系统

本系统利用工业级摄像头及开发包的机器视觉功能与计算软件编程控制技术相结合,通过与输入控制系统中的标准膜片模板外观品质进行对比,实现高速高效、准确识别检测光学薄膜器件中滤光片的外观缺陷。可在生产线上根据外观品质标准模板中设定的类别进行自动分类装盒。     

考试网上报名系统的技术实现框架探讨

随着Internet互联网技术及电子政务网站建设的飞速发展,无处不在的网络应用为社会考试报名提供了可靠、快捷的报名通道。通过分析当前考试网上报名系统使用的社会需求,本文结合网报系统的系统开发模式、工具,从系统用户管理、考生报名、考务管理及超级用户管理等模块对名系统的技术实现框架作了初步的探讨。     

遗传算法在试题组卷中的应用与研究

相信大家对达尔文的进化论并不陌生,其中的基因遗传,基因突变等等对基因学得发展起了不可估量的作用。优胜劣汰,即优的基因大量被遗传复制,劣的基因较少被遗传复制。借用这些理论思路,我们可以将其思想用到自动组卷中。所谓自动组卷,即在各种方面不同难度不同题型的题库中选择一套试卷出来,这套试卷对平均难度,总体量,题型比例,重点章节所占试卷比例等等有一定的要求。这样我们就需要一种算法来满足组卷的各方面需求,即遗传算法。本文我们就对遗传算法在组卷中的应用做详细的介绍。     

普通话测试语音信息库的设计与实现

每年的国家普通话测试,都留下普通话测试的相关信息。为了有效利用这些信息,建立了普通话测试语音信息库,实现了信息录入、信息查询、数据统计、权限管理等多种功能。为了实现最小到音节的录音语料查询,信息库利用HTK进行音频文件的自动切分处理。普通话测试语音信息库可方便地用于包括测试录音语料在内的各种资料的调用和各种关联数据的统计。     

一例Java语言与C语言代码运行效率的比较

通过构建一个算法效率测试平台的简易模型,比较Java语言和C语言两种主流语言在实际环境中执行同一算法时所花费时间的区别．揭示C语言在算法效率测试平台上相对于Java语言具有执行速度快、效率高的特点．为程序设计人员在编写算法效率测试平台时选用何种语言进行编程提供了一种参考。     

数据驱动技术在WRSAFS中的研究与应用

本文以自动化测试过程中应用数据驱动时遇到的问题为切入点,提出了一种更为先进的数据驱动技术并与自动化测试框架WRSAFS(Win Runner Software Automation Framework Support)相结合,重点分析了数据驱动技术在WRSAFS中的应用及相互关系。借助实际被测试软件,应用此技术设计测试用例并取得了很好的效果。     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.     

Automatic Construction and Verification of Isotopy Invariants

We extend our previous study of the automatic construction of isomorphic classification theorems for algebraic domains by considering the isotopy equivalence relation. Isotopism is an important generalisation of isomorphism, and is studied by mathematicians in domains such as loop theory. This extension was not straightforward, and we had to solve two major technical problems, namely, generating and verifying isotopy invariants. Concentrating on the domain of loop theory, we have developed three novel techniques for generating isotopic invariants, by using the notion of universal identities and by using constructions based on subblocks. In addition, given the complexity of the theorems that verify that a conjunction of the invariants form an isotopy class, we have developed ways of simplifying the problem of proving these theorems. Our techniques employ an interplay of computer algebra, model generation, theorem proving, and satisfiability-solving methods. To demonstrate the power of the approach, we generate isotopic classification theorems for loops of size 6 and 7, which extend the previously known enumeration results. This work was previously beyond the capabilities of automated reasoning techniques. The author’s work was supported by EPSRC MathFIT grant GR/S31099.     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.     

Computer-based testing: An alternative for the assessment of Turkish undergraduate students

Virtually errorless high speed data processing feature has made computers popular assessment tools in education. An important concern in developing countries considering integrating computers as an educational assessment tool before making substantial investment is the effects of computer-based testing on students’ test scores as compared to paper-and-pencil tests. This study investigated whether test scores of Turkish students were different in the computer-based test and in the paper-and-pencil test, with forty-seven undergraduate students studying at a public university located in the Blacksea region of Turkey. Findings of this study showed that test scores of undergraduate students were not different in the computer-based test and in the paper-and-pencil test which led us to reach the conclusion that computer-based testing can be considered as a promising alternative technique for the undergraduate students in Turkey.