科创中国

通过对渠家大院院落空间的调查研究,将院落单元作为渠家大院空间形态的构成要素,探究渠家大院的空间形态.通过对该院落空间的平面、立面和微观营造模式进行研究,深入地分析了该院落空间的布局特点、空间意向与营造理念,最终为读者解读了渠家大院空间系统的成长规律,进而揭示了渠家大院院落空间组织的方式与机制,有助对晋中城市民居营造理念与发展趋势有更好的把握.

As a promising technique, surrogate-based design and optimization(SBDO) has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function(SEO-SRBF). Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of the material volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.

详细介绍了SVPWM的原理.采用一种易于数字化实现的SVPWM算法实现三相电压型SVPWM整流器,该整流器采用dq坐标系下的双闭环控制.给出了PI调节器参数,以及主电路电感、电容的计算公式.在Matlab Simulink环境下搭建整流器仿真器模型.仿真结果表明,整流器能实现单位功率因数整流和能量双向流动,验证了SVPWM算法和控制系统设计方法的正确性.

信息社会中在线百科已成为人们获取知识的重要途径,而在线百科的标签系统作为其重要组成部分,不仅可以帮助人们在浏览某张页面时获取其他相关页面的信息,而且对于海量文本分类,以及提高在线百科检索系统的检索效率都有很大帮助.充分利用在线百科页面间的链接关系,提出了一种基于页面间的同质性原理和向量空间模型的全新针对在线百科的标签推荐算法HVSM(homogeneous principle based vector spacemodel).该标签推荐算法具有普适性,可在不同在线百科系统间推荐标签.实验结果表明,通过与朴素推荐算法NAM(nawere commendation model)进行比较,新的推荐算法可以达到更高的准确率.并且通过对实验数据进行分析,得到了若干有益的结论,为今后的研究工作奠定了基础.

针对传统SVM主动学习中批量采样方法的不足,提出了动态可行域划分算法.从特征空间与参数空间的对偶关系入手,深入分析SVM主动学习的本质,将特征空间中对样本的标注视为参数空间中对可行域的划分;通过综合利用当前分类模型和先前标注样本两方面信息,动态地优化可行域划分方案,以确保选取的样本对模型改进的价值,最终实现更为高效的选择性采样.实验结果表明,基于动态可行域划分的SVM主动学习算法能够显著提高所选样本的信息量,从而能够在有限的标注代价下大幅提高其分类性能.