基于知识的计算机辅助工艺设计系统

介绍了一个较为通用的基于知识的计算机辅助工艺设计（ＣＡＰＰ）系统结构，包括知识表达和关联函数，知识库管理，任务分解与综合策略，解释机制，与其他系统的接口。

计算机辅助带时延多变量系统的离散控制设计

本文将Smith预估技术与逆 Nyquist 阵列法结合对多时延多变量对象进行离散控制系统设计,采用这种方法设计出的控制器易用计算机实现,系统仿真结果也是令人满意的。

计算机辅助机械绘图语言与系统设计

本文针对我国计算机绘制机械图领域内的现状,提出了一种易于掌握的机械图描述语言,以及一个相应的功能较强的绘图系统。该系统便于图形输入,并具有图形的平移、旋转、映射等功能,和较强的剖面域的处理能力。系统带有一个标注专用的笔写式字符库(包括汉字和专用符号)。该系统用 FORTRAN 语言写成,便于向大、中型计算机和存储容量较大的微型机移植。

快速成型技术中分层算法的研究与进展

根据对零件制造精度和效率的关注程度的不同,开发出了多种分层算法。在同等加工时间的情况下,根据加工精度的不同,将这些分层算法分为等层厚分层算法和适应性分层算法两类。通过对STL模型、原始CAD模型和点云数据的分析,讨论了两类分层算法的研究和发展,然后介绍了斜边分层算法和曲面分层算法等先进分层算法的原理和成果,最后讨论了快速成型分层算法的研究方向和趋势。

基于点集曲面投影算法的自由曲面匹配

测量数据的精确定位是实现复杂曲面加工检测的关键,针对测量点云数据与NURBS表示的CAD自由曲面模型匹配中求最近点计算方面存在的问题,提出了一种简单、有效的寻找最近点的方法。该方法与由测量点集评估给定曲面上的最近点的传统算法相反,采用点集曲面(point set surface,PSS)投影算法,对给定自由曲面模型上有限个点与不附加任何几何和拓扑信息的散乱点集之间进行粗匹配获得初始位置,进而以最近点迭代算法(ICP)完成测量数据定位的精确调整,达到全局及局部最优的目标。实验结果表明,采用PSS投影算法法寻找最近点不仅效率高,而且能得到全局匹配结果,可以为精匹配提供较好的计算初值,减少了ICP算法进行二次匹配时,迭代次数及执行时间并且精度得到了较大提高。

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.