End-capped polystyrene with 8-hydroxyquinoline group by ATRP method

8-Hydroxyqunioline end-capped polystyrene was prepared through atom transfer free radical polymerization (ATRP) with 8-(5-chloromethyl) quinolyl acetate as initiator. The results indicated that this polymerization is a first order reaction with respect to monomer conversion. The molecular weight increased linearly with monomer consumption and very narrow distribution of molecular weight was obtained (polydispersity index less than 1.2). The FT-IR and NMR results show that the 8-hydroxyquinloine group was chemically bonded to the polymer end and there is nearly one 8-hydroxyquinoline group in per polymer chain. All those data show that polymerization of styrene at such conditions displayed living characters. The polymer with 8-hydroxyqquinoline end group reacted with triethylaluminum to form polymeric light-emitting complex and single layer LED was prepared by common spin-coating method. The peak wavelength of LED based on synthesized polymeric complex was around 570 nm.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.     

An improved ν-twin support vector machine

Twin support vector machine (TSVM) is regarded as a milestone in the development of the powerful SVM. It finds two nonparallel planes by resolving a pair of smaller-sized quadratic programming problems rather than a single large one, which makes the learning speed of TSVM approximately four times faster than that of the standard SVM. However, the empirical risk minimization principle is implemented in the TSVM, so it easily leads to the over-fitting problem and reduces the prediction accuracy of the classifier. ν-TSVM, as a variant of TSVM, also implements the empirical risk minimization principle. To enhance the generalization ability of the classifier, we propose an improved ν-TSVM by introducing a regularization term into the objective function, so there are two parts in the objective function, one of which is to maximize the margin between the two parallel hyper-planes, and the other one is to minimize the training errors of two classes of samples. Therefore the structural risk minimization principle is implemented in our improved ν-TSVM. Numerical experiments on one artificial dataset and nine benchmark datasets show that our improved ν-TSVM yields better generalization performance than SVM, ν-SVM, and ν-TSVM. Moreover, numerical experiments with different proportions of outliers demonstrate that our improved ν-TSVM is robust and stable. Finally, we apply our improved ν-TSVM to two BCI competition datasets, and also obtain better prediction accuracy.     

An efficient KPCA algorithm based on feature correlation evaluation

Classic kernel principal component analysis (KPCA) is less computationally efficient when extracting features from large data sets. In this paper, we propose an algorithm, that is, efficient KPCA (EKPCA), that enhances the computational efficiency of KPCA by using a linear combination of a small portion of training samples, referred to as basic patterns, to approximately express the KPCA feature extractor, that is, the eigenvector of the covariance matrix in the feature extraction. We show that the feature correlation (i.e., the correlation between different feature components) can be evaluated by the cosine distance between the kernel vectors, which are the column vectors in the kernel matrix. The proposed algorithm can be easily implemented. It first uses feature correlation evaluation to determine the basic patterns and then uses these to reconstruct the KPCA model, perform feature extraction, and classify the test samples. Since there are usually many fewer basic patterns than training samples, EKPCA feature extraction is much more computationally efficient than that of KPCA. Experimental results on several benchmark data sets show that EKPCA is much faster than KPCA while achieving similar classification performance.     

A power spectrum optimization algorithm inspired by magnetotactic bacteria

Magnetotactic bacteria (MTB) are one kind of bacteria with magnetic particles called magnetosomes in their bodies. These particles often connect together like a chain. The MTB move toward the ideal living conditions under the interaction between magnetic field produced by the magnetic particles chain and that of the earth. In the paper, a new magnetic bacteria algorithm based on power spectrum (PSMBA) for optimization is proposed. The candidate solutions are decided by power spectrum in the algorithm. It mainly includes four steps: power spectrum calculation, bacteria swimming, bacteria rotation and bacteria replacement. The effect of swimming schemes and parameter settings on the performance of PSMBA is studied. And it is compared with GA, PSO and its variants and some other optimization algorithms on 25 benchmark functions including CEC2005. The simulation results show that PSMBA has better performance on most of the problems than most of the compared algorithms.     

碳沉积法制富氮用炭分子筛的研究

本文提出的碳沉积法是用煤焦油馏分油的有机溶液浸渍煤炭化物,然后热解以进行“孔调变”。确定了最佳工艺条件,制得富氮性能良好的炭分子筛。并用D-A方程求取了各种样品的微孔结构参数。     

鹽酸氟桂利嗪有关物质测定方法的研究

在Balestrion·F.,Jirillo·R. 等人的研究及卫生部部颁标准在基础上,对盐酸氟桂利嗪的有关物质的测定方法进行了研究,由薄层色谱法改为高效液相色谱法,操作简单易行,分辨率高,重现性好,结果准确可靠。     

甲基对苯二酚的合成进展

综述了甲基对苯二酚的各种制备方法,并比较了各自的优缺点.着眼于反应条件温和,环境友好的发展方向及选择高效的催化剂,指出双氧水直接羟基化邻甲酚制备甲基对苯二酚是最具有潜力的工艺方法.