分形粗糙面的电磁散射－扩展边界条件

用扩展边界条件方法对分形粗糙良导体面及介质面的电磁散射问题进行了分析。用推广的Floquet模式，在分界面处将场分量用Fourier级数展开，根据边界条件及扩展边界条件得到了水平极化和垂直极化散射场的幅度分量的表达式。用其它近似方法（Kirchhoff和Rayleigh方法）及能量守恒准则验证了此方法的有效性。     

聚苯胺电化学性能的改性研究

改善聚苯胺的电化学性能是聚苯胺研究中的一个重点，介绍了对聚苯胺的取代，共聚，复合和掺杂等几种改性方法，及其对聚苯胺电化学性能和其他性能的影响。     

基于内容图像检索中的优化鉴别特征

为了提高基于内容图像检索系统的检索速度和准确率,提出一种融合两类线性鉴别分析的方法来提取低维的优化鉴别特征.首先把多类问题转换为多个两类问题,对每个两类问题进行线性鉴别分析,得到鉴别向量;所有的鉴别向量组成鉴别变换矩阵,对图像特征进行投影变换得到鉴别特征;最后用变换后的鉴别特征进行图像检索或分类,得到准确率更高的结果.该方法中鉴别特征空间的维数与类别数相等.与多种特征优化方法进行比较的实验结果表明,采用文中方法可以显著地提高图像检索和图像分类的性能.     

MapReduce Workload Modeling with Statistical Approach

Large-scale data-intensive cloud computing with the MapReduce framework is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop, a state-of-the-art open source project, is by far the most successful realization of MapReduce framework. While MapReduce is easy- to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop impose unexpected challenges on running various workloads with a Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, either because they have no idea how these configurations would influence the performance, or because they are not even aware that these configurations exist. There is a pressing need for comprehensive analysis and performance modeling to ease MapReduce application development and guide performance optimization under different Hadoop configurations. In this paper, we propose a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. We apply principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoop configurations. Regression models are also constructed that attempt to predict the performance of various workloads under different Hadoop configurations. Several non-intuitive relationships between workload characteristics and performance are revealed through our analysis and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations.     

石墨烯改性抗H2S酸性介质高效防腐涂层技术的研究与应用

为解决石油石化装置在有H2S、CO2等酸性介质存在条件下的腐蚀问题,通过对酚醛环氧树脂和酚醛胺固化剂配套成膜物体系、耐酸性颜填料体系等的研究,形成了耐酸性良好的涂料,同时通过在涂料中添加石墨烯分散体,改善了涂层的耐盐雾性、附着力和耐化学品性.实验室研究及现场应用均表明该石墨烯改性耐酸涂料具有优异的综合物理化学性能,其涂层的耐盐雾、耐化学品、抗H2S腐蚀、耐碱等性能优异,满足了存在H2S、CO2的酸性油气田恶劣腐蚀环境下的防腐要求.     

EFFECT OF MnS AND SLAB REHEATING TEMPERATURE ON PROPERTIES OF ORIENTED-GRAIN Fe-3%Si

本文确定了按Mns方案制造3％取向Si钢的主要成分:C=0.03—0.05％,Mn=0.06—0.08％,S=0.018—0.025％,[Mn][S]=(11—20)×10~(-4),O_2<0.007％。证明板坯加热温度提高,初次晶粒尺寸减小,二次晶粒尺寸增大,磁性明显提高(B_(10)>1.78Wb/m~2)而且稳定。合适的板坯加热温度随[Mn][S]积增加而提高。加入适量Al和N_2后,加热温度对磁性较不敏感,二次晶粒尺寸增大,B_(10)值进一步提高。二次再结晶开始温度也就是初次晶粒迅速长大和MnS质点开始聚集的温度。     

γ-MnO2第一电子放电机理及质子嵌入模型

电解二氧化锰是碱性锌锰电池的主要正极材料,其晶型为γ-MnO2。γ-MnO2的放电行为对锌锰电池的放电性能起着决定性的作用。本文综述总结了γ-MnO2第一电子放电机理和质子嵌入模型的新研究进展,主要包括:同相还原机理、双溶液机理、离域-定域机理、同步还原机理和分步还原机理。     

A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network

In this paper, a novel self-adaptive extreme learning machine (ELM) based on affinity propagation (AP) is proposed to optimize the radial basis function neural network (RBFNN). As is well known, the parameters of original ELM which developed by G.-B. Huang are randomly determined. However, that cannot objectively obtain a set of optimal parameters of RBFNN trained by ELM algorithm for different realistic datasets. The AP algorithm can automatically produce a set of clustering centers for the different datasets. According to the results of AP, we can, respectively, get the cluster number and the radius value of each cluster. In that case, the above cluster number and radius value can be used to initialize the number and widths of hidden layer neurons in RBFNN and that is also the parameters of coefficient matrix H of ELM. This may successfully avoid the subjectivity prior knowledge and randomness of training RBFNN. Experimental results show that the method proposed in this thesis has a more powerful generalization capability than conventional ELM for an RBFNN.     

Wavelet twin support vector machine

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.