高钒高速钢碳化钒形态的数值化分析

针对碳化钒的不同形态,设计出定量分析软件,并利用形状因子K,实现了高钒高速钢不同碳化物形态数值化处理,当K介于0.785～1.000之间碳化钒形态为团球状;K≤0.223,碳化钒呈开花状.高钒高速钢当碳含量达到4.2%时,K为0.86和当量直径D为0.78μm,碳化物呈团球状均匀分布.     

逆变电源的可靠性及其提高途径

影响逆变焊接电源可靠性的因素是多方面的.如产品的设计和生产等各个环节.根据实践经验提出了解决可靠性问题的一些有效途径和方法.     

“不择手段”——艺术设计基础课教学形式的多样化探索

教学是一个动态的过程,艺术设计的基础课教学更是如此,需要教师不断地更新适应时代发展的教学理念,在教学过程中更要"不择手段",探索丰富多样的教学形式。文章论述了在教学过程中对教学形式多样化的研究与实践,主要包括教学过程、学生作业形式、教学地点三个方面,以及对"基础"这一概念的再诠释。     

Robust image hashing using non-uniform sampling in discrete Fourier domain

This paper proposes a robust image hashing method in discrete Fourier domain that can be applied in such fields as image authentication and retrieval. In the pre-processing stage, image resizing and total variation based filtering are first used to regularize the input image. Then the secondary image is obtained by the rotation projection, and the robust frequency feature is extracted from the secondary image after discrete Fourier transform. More sampling points are chosen from the low- and middle-frequency component to represent the salient content of the image effectively, which is achieved by the non-uniform sampling. Finally, the intermediate sampling feature vectors are scrambled and quantized to produce the resulting binary hash securely. The security of the method depends entirely on the secret key. Experiments are conducted to show that the present method has satisfactory robustness against perceptual content-preserving manipulations and has also very low probability for collision of the hashes of distinct images.     

运用多媒体改革经济数学教学存在的问题与对策

如何将多媒体技术有效地应用于经济数学课堂教学中,是目前经济数学教学改革值得注意的问题。结合教学实践,分析当前经济数学多媒体教学存在的问题,提出经济数学课程使用多媒体教学必须结合课程的特点,教学的需要以及学生的实际情况,将传统黑板教学与多媒体教学优势互补,以实现最佳教学效果。     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

An ant direction hybrid differential evolution heuristic for the large-scale passive harmonic filters planning problem

This article presents a heuristic which combines the orthogonal array experiment technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) for planning of a large-scale passive harmonic filters problem. The addressed problem has a multi-bus and under abundant harmonic current sources in the system. In this study, an orthogonal array is first conducted to obtain the initial solution. Next, an ant direction hybrid differential evolution (ADHDE) is applied to search for a near optimum solution. The objective is to minimize the cost of the filter, the total demand distortion of harmonic currents and the total harmonic distortion of voltages at each bus simultaneously. In order to determine a set of weights of each term in the objective function, the simplest and most efficient form of triangular membership functions has been considered. The designed heuristic ADHDEOA is applied to a practical harmonic problem in a steel plant, and three design schemes are compared to demonstrate the performance of the ADHDEOA.