悟

“悟”是一种对客观的事和物的一种感知,是对事和物获取了解、获取知效,是人们求得理解的一种启示、一种认识的过程。一般地说,“我对事和物,意在彻悟”,是指人们得到充分的悟性,特别对意、意义的悟,有的很快领会,有的要耗费时日,有的要经过一定的时间乃至终身。悟性就是理解的     

《建筑课》之一开学了

“对于建筑事业,没有什么比让全民的建筑意识提高更重要的事情了。”五十多年前,年轻的齐康对此深信不疑,如今已是古稀之年的齐老对此仍然耿耿于怀。对于青年一代的教育,他从未放松;传统与现代的比较、东方与西方差异的研究,他从未停止。从本期起,我们将连续刊登齐康系列文章《建筑课》。     

Pattern analysis of airborne dipole antenna in high-frequency region

The Uniform Geometrical Theory of Diffraction (UTD) has been successfully used in pattern analysis of airborne dipole antenna on the A-5 fighter in high-frequency region. In the analysis, several canonical problems are investigated first, then the complicated problem is calculated with compiling these canonical problems. The numerical results are compared with the experimental data, and good agreement has been seen. The effectiveness and practicality of the present analysis are thus verified.     

95W-Ni-Fe 合金工艺缺陷的 SAM 和 XPS 分析

藉助扫描俄歇探针、Ｘ射线光电子谱等分析了９５Ｗ－Ｎｉ－Ｆｅ合金碳污染和氧化的本质。发现，碳污染试样中碳的化学状态主要为石墨，少量为铁或镍的有机化合物，这些物质的界面富集使材料脆化。钨合金氧化后，氧大量富集于钨／粘结相界面，使沿着该界面的断裂更容易发生。     

Tracking non-rigid objects in clutter background with geometricactive contours

A new variational framework of geometric active contours to track multiple non-rigid moving objects in the clutter background is presented. Incorporating motion edge information, it consists of motion detection and tracking stages. The results of experiments are promising and demonstrate the validity of the proposed framework     

Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis

High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature reduction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, an algorithm called ULDA/QR is proposed to simplify the previous implementation of ULDA. Then, the ULDA/GSVD algorithm is proposed, based on a novel optimization criterion, to address the singularity problem which occurs in undersampled problems, where the data dimension is larger than the sample size. The criterion used is the regularized version of the one in ULDA/QR. Surprisingly, our theoretical result shows that the solution to ULDA/GSVD is independent of the value of the regularization parameter. Experimental results on various types of data sets are reported to show the effectiveness of the proposed algorithm and to compare it with other commonly used feature reduction algorithms.