计算机在电阻炉设计中的应用

本文介绍了286/386型个人计算机在电阻炉计算机辅助设计中的应用,包括整体思路,数学模型的建立方法。还以连续式电阻炉为例编制了设计软件,详细介绍了该软件的功能和使用方法。本文提出的计算机辅助设计方法亦适用于其它工业电炉。     

针对产品的CAD／CAM一体化技术

本文系统地介绍了一种基于ＣＡＤ工作站，利用Ｉ－ＤＥＡＳ软件实现的针对壳体类零件的ＣＡＤ／ＣＡＭ一体化技术。论述了利用ＰＥＡＲＬ数据库实现的从ＣＡＤ模型到ＣＡＰＰ之间信息的传输，分析了在ＣＡＤ与ＣＡＭ集成中创成式ＣＡＰＰ所起的关键作用，并介绍了利用ＧＮＣ图形辅助数控软件对加工过程的仿真以及机床文件的准备，最后系统能生成正确的数控指令。系统初步实现了ＣＡＤ／ＣＡＭ一体化。     

CAD／CAPP并行集成理论及系统研究

并行工程作为一个新的设计哲理在工程实践中的实现手段不尽相同，本文中我们提出了ＣＡＤ／ＣＡＰＰ并行集成概念，并开发了原型系统ＣＥＣＡＤＰ，初步实现了一种并行设计的运行模式。     

一个用于VHSIC连接线瞬态分析的电路模拟器

本文介绍了一个可对含连接线的电路进行瞬态分析的电路模拟器,该模拟器是在SPICE基础上经修改扩充而成的,连接线的处理采用了文献[1]的方法.文中介绍了方法的基本原理,讨论了数值Laplace反变换时参数的选择与误差的控制.实际VHSIC电路的试算结果表明,该模拟器是相当有效的.     

计算机专业CAD课程的深化实践

在现行的社会技术人才的高度需求下,CAD课程的教学任务更显得意义重大。探讨如何提高教学效率．培养学生的技术应用能力、职业实践能力和创新能力,缩短计算机专业学生cAI）技术应用与单位需求的差距而进行相应的有效实用教学。     

《电路》课程教学模式改革探索与实践

针对传统教学模式下《电路》课程教学中学生积极性和主动性不强,缺少综合能力训练内容等问题,对教学方法和教学实践环节进行改革。提出基于任务驱动课堂教学的方法和通过科研小课题初步培养大学生创新学习能力的方法。教学实践证明,本校学生较为适应改革后的教学模式,改进后的教学效果良好。此项研究对于高校教学改革,尤其是培养大学生创新学习能力具有一定的价值。     

一种分析时序逻辑电路的补充方法

介绍一种分析时序逻辑电路的补充方法,并应用于教学实践中。其特点是原理简单、易于理解、直观方便,相对于传统的分析方法,避免了对次状态方程的分析处理计算,有时比传统方法简便、快捷。通过两个示例展示其优越性。     

混合集总—分布参数微波功率分配器的机助设计

在本文中,将微波功率分配器看做用多个两端口网络构成的多端口匹配网络,用集总与分布参数元件混合组成的模块来综合这种网络,从而得到了一种新型的混合集中分布参数微波功率分配器及其机助设计方法。     

Exact probabilistic testability measures for multi-output circuits

The computation of probabilistic testability measures has become increasingly important and some methods have been proposed, although the exact solution of the problem is NP-hard. An exact analytical method for singleoutput combinational circuits is extended to deal with multi-output circuits. Such circuits are reduced to singleoutput ones by introducing a dummy gate, the X-gate, and applying to the resulting graph the analysis based on supergates.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.