西宁特钢连铸机技术改造

对西钢新建生产线五机五流小方坯连铸机改造进行了综述.连铸机优良的装备为整条生产线向品种化、规模化、高效化方向快速发展奠定了基础.     

A new approach to the epsilon expansion of generalized hypergeometric functions

Assuming that the parameters of a generalized hypergeometric function depend linearly on a small variable ε$\epsilon$, the successive derivatives of the function with respect to that small variable are evaluated at ε=0$\epsilon =0$ to obtain the coefficients of the ε$\epsilon$-expansion of the function. The procedure, which is quite naive, benefits from simple explicit expressions of the derivatives, to any order, of the Pochhammer and reciprocal Pochhammer symbols with respect to their argument. The algorithm may be used algebraically, irrespective of the values of the parameters. It reproduces the exact results obtained by other authors in cases of especially simple parameters. Implemented numerically, the procedure improves considerably, for higher orders in ε$\epsilon$, the numerical expansions given by other methods.     

带可变随机函数和变异算子的粒子群优化算法

标准粒子群优化算法的收敛分析表明,改变随机函数、个体历史最优,群体全局最优,有助于提高该算法的性能。为此,本文提出了一种带可变随机函数和变异算子的粒子群优化算法,即通过改变速度更新方程中的随机函数分布来调节粒子在迭代过程中飞向个体历史最优和群体全局最优的比重,通过对个体历史最优和群体全局最优进行变异来增强种群的搜索能力。实验结果证实了该算法的有效性。     

基于马尔可夫链的自适应性神经网络训练算法

提出一种基于马尔可夫链的自适应性神经网络训练方法,对传统的S型激励函数进行了改进,建立了自适应性的神经网络分类器。在假设样本中噪声服从于正态分布的情况下建立最大似然估计,通过后验概率建立马尔科夫链对样本进行训练,提高了神经网络训练速度。在轴承故障诊断中的测试结果表明,该算法可以迅速稳定地训练出神经网络,有效提高诊断的分类结果。     

多流形判别分析在人脸识别中的研究

局部保持投影LPP(Locality Preserving Projection)是一种有效的非线性降维方法,能够使投影降维后的数据与原输入空间中的相似局部结构保持一致,但是该方法没有充分利用类间样本点的权重等重要信息。为了解决这个问题,提出基于Fisher准则的多流形判别分析FMMDA(Fisher Multi-Manifold Discriminant Analysis)方法。结合Fisher准则训练样本类内拉普拉斯图和样本均值类间拉普拉斯图,既保持了原样本的相似局部结构,又充分地利用了不同类别之间的权重。在ORL及Yale人脸库上验证了该方法的有效性。与其他几种最先进的方法相比,FMMDA方法取得了更好的识别效果。     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。     

基于线性判别边信息结合PHL的三维人脸识别

针对三维人脸识别中受光照、姿态、表情等变化而影响识别性能的问题,提出了一种原型超平面学习算法。利用SVM将弱标记数据集中的每个样本表示为一个原型超平面中层特征,使用学习组合系数从未标记的通用数据集中选择支持向量稀疏集;借助于Fisher准则最大化未标记数据集的判别能力,使用迭代优化算法求解目标函数;利用SILD进行特征提取,余弦相似性度量完成最终的人脸识别。在USCD/Honda、FRGC v2、LFW及自己搜集的人脸数据集上的实验结果表明,该算法优于其他几种三维人脸识别算法。     

Credal classification rule for uncertain data based on belief functions

In this paper we present a new credal classification rule (CCR) based on belief functions to deal with the uncertain data. CCR allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. Each specific class is characterized by a class center (i.e. prototype), and consists of all the objects that are sufficiently close to the center. The belief of the assignment of a given object to classify with a specific class is determined from the Mahalanobis distance between the object and the center of the corresponding class. The meta-classes are used to capture the imprecision in the classification of the objects when they are difficult to correctly classify because of the poor quality of available attributes. The selection of meta-classes depends on the application and the context, and a measure of the degree of indistinguishability between classes is introduced. In this new CCR approach, the objects assigned to a meta-class should be close to the center of this meta-class having similar distances to all the involved specific classes? centers, and the objects too far from the others will be considered as outliers (noise). CCR provides robust credal classification results with a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this CCR method with respect to other classification methods.     

Older adults’ use of smart phones: an investigation of the factors influencing the acceptance of new functions

After adopting mobile phones, most older adults use them only for calling and SMS. The purpose of this study is to extend their usage of mobile phones to new functions. To understand older adults’ requirements of mobile phones, a questionnaire was constructed and 351 Chinese older adults were recruited to complete the questionnaires. Data collected through the questionnaires were analysed using explorative factor analysis. The results revealed that older adults’ requirements were composed of 10 factors: Find a Specific Function, Awareness and Attractiveness, Readability, Personal Concern, Soft Keys and Multi-tap, Hardware Capacity, Touch Screen, Concern of Learning, Connectivity, and Social Influence. Then, from the above 10 factors, the 6 most important factors were revealed using multiple linear regression analysis. The results imply that accepting new functions is different from accepting a product. Readability and Find a Specific Function, which are critical for older adults’ acceptance of feature phones, are not determinants of their acceptance of new functions in smart phones.