Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Comparative study on economic contribution rate of education of China and foreign countries based on soft computing method

Economic contribution rate of education (ECRE) is the key factor of education economics. This article selected China, South Korea, United States and other countries for a total of 15 samples, and put the data of the same period under the framework of soft computing, to simulate the production chain of “education–potential human capital–actual human capital–economic growth”. The basic idea is: Firstly, 15 countries are softly categorized according to the level of science and technology (S&T) progress. Secondly, potential human capital and actual human capital establish the internal correlation (fuzzy mapping) in the same classification, and we conceptualize actual human capital as one production factor, joined with the other two production factors, fixed asset and land, to set up the fuzzy mapping to economic growth., and then calculate economic contribution rate of education of China and foreign by two fuzzy mapping of them. Thirdly, this paper analyzes the present state and differences in the development of education between China and foreign according to different ECRE, and offers proposals for promoting the education in China.     

Manufacturing Bespoke Architecture

At the disposal of today??s architect is an evolving array of interoperable tools and processes that allow the fabrication of design propositions to be increasingly complex, non?Cstandard and adaptive. How are we equipped to deal with such a growing breadth of new potential, and how are the philosophies that underpin this potential being defined? This paper attempts to address what is something of a contemporary dilemma in architecture, as the constraints of industrial standardisation are relaxed. Have the roles of designers and makers changed in a way that we??ve not experienced before, and is a new approach to making architecture emerging?     

铁矿石境外投资风险评估的模糊神经网络模型

论文将模糊理论与神经网络结合,对铁矿石境外投资所涉及的风险因素分别从资产影响、威胁频度两方面进行分析,建立了铁矿石境外投资的风险层次化结构,并构造了各因素所对应评判集的隶属度矩阵;综合运用模糊推理算法与神经网络仿真技术,对铁矿石境外投资的风险进行评估,进而判定铁矿石境外投资风险等级。最后,通过计算铁矿石境外投资的市场风险说明了算法的应用,并将结果与模糊综合评估得到的风险值进行比较,检测了模型的有效性。     

基于OpenCv运动目标识别技术的研究

在智能视频监控系统中,快速、准确、有效地检测出运动目标成为人们研究的主要课题之一。本文根据背景差分与帧间差分各自的特点,提出一种基于高斯模型的背景差分与帧间差分相结合的运动目标检测算法。说明高斯背景建模原理以及算法的背景模型更新方法,最后介绍了算法的实现流程和实验结果。在VisualC++6.0环境下,利用计算机视觉类库OpenCV对算法进行了验证。实验结果表明:该算法能快速、有效地提取目标。     

基于广义回归神经网络的蛋白质二级结构预测

在生化实验中,关于优化蛋白质预测问题,由于采集的信息、参数、选取和设置等优化处理存在随机性,限制了蛋白质二级结构预测精确度。为解决上述问题,针对广义回归神经网络学习速率快、网络稳健的特点,提出基于广义回归神经网络预测蛋白质二级结构的方法。鉴于编码方式对预测精度有重要影响,首先基于5位编码和不同的滑动窗口构建多个广义回归神经网络预测器对蛋白质二级结构进行预测,取得了较好的结果。并采用富含生物进化信息的序列谱(Profile)编码构建输入向量、并针对不同大小的滑动窗口设置多个spread值重新创建广义回归神经网络预测器,大大提高了预测精确度,仿真结果证明了预测模型的有效性和可行性,为预测提供了有效方法。     

项目驱动式教学法在计算机基础课程中的应用

项目驱动式教学做为一种新兴的教学模式越来越多的应用于各门学科的教学应用当中。计算机基础这门课程由于其理论知识更新速度快,实践操作要求高的特点,将之与项目驱动式教学法相结合可以起到很好的教学效果。本文将传统教学方法和项目驱动式教学法进行对比,针对当前教学过程中存在的问题提出相应的解决方法。     

高温水冷后循环加卸载条件下花岗岩的渗透性

针对水下隧道火灾喷水灭火降温过程中围岩的渗透性问题,对高温水冷后循环加卸载条件下花岗岩的渗透性开展了试验研究.以高温(25、400和900 ℃)水冷后的花岗岩试样为研究对象,通过轴向压应力循环加卸载过程中气体渗透性试验,研究花岗岩渗透率变化规律.结果表明:轴向压应力循环加卸载试验过程中,花岗岩试样的卸载模量随着高温水冷处理温度升高而减小,随着轴向压应力增大,卸载模量总体呈上升趋势,气体渗透率随温度升高而增大;花岗岩BET比表面积与BJH孔体积在900 ℃高温水冷后明显减小.     

焊接箱型截面构件在不同焊缝熔深下承载力性能试验研究

为了有效改善大跨度桥梁中管翼缘焊接箱型截面构件的耐久性的问题,提出采用耐候钢代替普通钢的管翼缘箱型截面梁。同时,为了研究耐候钢管翼缘焊接箱型截面构件填充混凝土后在全熔透和部分熔透条件下的单轴承载力性能,开展了焊接矩形截面在三种不同焊缝熔深下的耐候钢管混凝土截面轴压短柱试验研究。分别测试每个试件在单轴压力作用下的静态应变、动态应变,以及竖向位移和侧向位移。对比构件承载力的试验结果及理论分析结果,得出不同熔深下焊接箱型截面管翼缘组合构件的承载力性能。研究结果表明:1）耐候钢管翼缘焊接箱型截面构件的全熔透焊缝承载力高于部分熔透焊缝承载力。2）全熔透试件开裂时的竖向位移较部分熔透试件的位移值大,而不同熔深下构件的横向位移测点L4随轴压荷载的增大而有增加,L3测点横向位移规律不明显。3）不同耐候钢管翼缘焊接箱型截面构件横向整体受拉,局部在顶部角部位置受压。构件的混凝土内部和钢板表面应变情况复杂,规律不明显。