显像管调制特性的测量与分析

测试了显象管调制特性曲线，并分析了特性曲线与显像管工作原理．     

集值映射的切导数与向量优化问题的最优性条件

利用集值映射切导数与半可微概念，给出了无约束与具约束的集值向量优化问题局部真有效解与局部强有效解的最优性条件。     

三组元乳化液雾化特性的研究

采用Malvern粒度仪对柴油、甲醇和水三组元乳化液的雾化特性进行了研究。实验发现:对于压力雾化喷嘴来说,本文所涉及的乳化液由于粘度高于纯柴油,因此雾化效果比纯柴油差,而且喷射压力、乳化剂的粘度和乳化液的组份对乳化液的雾化特性具有显著的影响。随着喷射压力的升高,乳化液喷雾粒径将随之减小;若乳化液中柴油的含量(柴油不少于50％)降低,乳化液的雾化粒径将随之增加;若采用高粘度的乳化剂,相应乳化液喷雾的粒径也大。     

Support vector machine via nonlinear rescaling method

In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see [Polyak in Math Program 54:177–222, 1992; Polyak in Math Program Ser A 92:197–235, 2002; Polyak and Teboulle in Math Program 76:265–284, 1997] and references therein). The formulation of the linear SVM based on the NR method leads to an algorithm which reduces the number of support vectors without compromising the classification performance compared to the linear soft-margin SVM formulation. The NR algorithm computes both the primal and the dual approximation at each step. The dual variables associated with the given data-set provide important information about each data point and play the key role in selecting the set of support vectors. Experimental results on ten benchmark classification problems show that the NR formulation is feasible. The quality of discrimination, in most instances, is comparable to the linear soft-margin SVM while the number of support vectors in several instances were substantially reduced.     

X波段五腔渡越管振荡器的理论与实验研究

 提出了一种新结构的X波段五腔渡越管振荡器，进行了理论和实验研究。根据场分布进行了一维非线性分析，结果表明该结构可以产生高功率微波，并判断了工作模式，为TM₀₁模的3π/5模。采用粒子模拟验证了一维非线性分析的结论，并优化设计出五腔渡越管振荡器,优化结果为：输出功率约1 GW， 工作频率9.3 GHz，束波转换效率约22%。实验中，通过参数调节，得到频率约9.25 GHz，峰值功率约780 MW，脉宽（半高宽）21 ns的输出微波，束波转换效率约为16%。实验结果与模拟结果基本符合。     

Difference schemes on uniform grids performed by general discrete operators

Our aim is to set the foundations of a discrete vectorial calculus on uniform n-dimensional grids, that can be easily reformulated on general irregular grids. As a key tool we first introduce the notion of tangent space to any grid node. Then we define the concepts of vector field, field of matrices and inner products on the space of grid functions and on the space of vector fields, mimicking the continuous setting. This allows us to obtain the discrete analogous of the basic first order differential operators, gradient and divergence, whose composition define the fundamental second order difference operator. As an application, we show that all difference schemes, with constant coefficients, for first and second order differential operators with constant coefficients can be seen as difference operators of the form for suitable choices of q, and  . In addition, we characterize special properties of the difference scheme, such as consistency, symmetry and positivity in terms of q, and  .     

Space–time integrated least squares: a time‐marching approach

A time‐marching formulation is derived from the space–time integrated least squares (STILS) method for solving a pure hyperbolic convection equation and is numerically compared to various known methods. Copyright © 2004 John Wiley & Sons, Ltd.     

基于Karhunen-Loève变换和小波谱特征矢量量化的三维谱像数据压缩

提出了基于Karhunen Lo埁ｖｅ变换的小波谱特征矢量量化三维谱像数据压缩方法耍幔颍瑁酰睿澹?Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化方法应用了Karhunen Lo埁ｖｅ变换的消除谱相关性优良性能 ,应用二维小波变换消除空间相关性 ,在小波变换域内应用二维集分割嵌入块编码和一维谱特征矢量量化对三维谱像数据压缩 ,获得较高的压缩性能。实验结果表明 :Karhunen Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化编码比Karhunen Lo埁ｖｅ变换 /小波变换 /改进对块零树编码和Karhunen Lo埁ｖｅ变换 /小波变换 /快速矢量量化编码方法在同样压缩比条件下 ,峰值信噪比提高 2dB和 1dB以上 ,而速度提高了 1.5和 8倍 ,整体压缩性能有较大的提高     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.     

On the tribological characteristics of dynamically loaded journal bearing with micropolar fluids

The addition of the additives to the lubricant oil to enhance the characteristics of the lubricant will influence the performance of the bearings. Based on the theory of micropolar fluids, the tribological characteristics of a dynamically-loaded journal bearing are numerically studied. Comparisons are made between the Newtonian fluids and the micropolar fluids. It is shown that for a dynamically-loaded journal bearing, the micropolar fluids yield an increase not only in the friction force, but also in the friction coefficient. In addition, the oil film pressure and the oil film thickness are obviously higher than that of Newtonian fluids.