Partial Oxidation of Methane to Syngas over Calcined Ni–Mg/Al Layered Double Hydroxides

The catalytic partial oxidation of CH₄ to syngas was carried out over an Ni–Mg/Al mixed-oxide catalyst prepared from layered double hydroxide-type precursors. The catalysts were characterized by XRD, TPR, UV-DRS, XRF, BET and CHNS analysis. The effects of the catalyst composition and the calcination temperature on the catalytic performance and the extent of catalyst deactivation were investigated. Ni–Mg/Al oxide catalysts converted CH₄ into syngas efficiently with high selectivity. The catalyst performance was strongly related to the Ni particle size and the calcination temperature. The catalysts that were calcined at higher temperature exhibited a better catalytic performance. In conclusion, the NiAl₂O₄ spinel phase had a positive effect on the stability of the catalyst.     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

A continuous-time framework for least squares parameter estimation

This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, constant parameters are investigated. Two estimators are studied. The first one consists of a linear evolution equation while the second one consists of an impulsive linear evolution equation. The paper discusses some theoretical aspects related to the proposed estimators: uniqueness of a solution and an attractive equilibrium point which solves for the unknown parameters. A deterministic framework for the estimation under noisy measurements is proposed using a Sobolev space with negative index to model the noise. The noise can be of large magnitude. Concrete signals issued from an electronic device are used to discuss numerical aspects.     

聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的同时测定

为了在线监控聚氯乙烯塑料中增塑剂的含量,利用傅立叶变换红外光谱法-ATR技术测定样品的红外光谱,采用偏最小二乘法对所测红外光谱进行分析,建立了一种无损快速分析方法,定量地测定聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的含量。该方法测量精密度的变异系数小于6%,测量准确度的变异系数小于3%。该方法不受基体的干扰,简单快速,适合于流水线的质量控制。     

线性多智能体系统一致性问题的部分稳定性方法（英文）

A linear transformation is proposed to deal with the consensus problem of high-order linear multi-agent systems(LMASs).In virtue of the linear transformation, the consensus problem is equivalently translated into a partial stability problem. We discuss three issues of the LMASs under a generalized linear protocol: 1) to find criteria of consensus convergence; 2) to calculate consensus function; 3) to design gain matrices in the linear consensus protocol. Precisely, we provide a necessary and sufficient criterion of consensus convergence in terms of Hurwitz stability of a matrix and give an analytical expression of the consensus function. In addition, we set up a relation between the gain matrices in the protocol and the convergence time and consensus accuracy of the agents, and then design the gain matrices with respect to a pre-specified convergence time and a required consensus accuracy.     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

压缩感知中高斯矩阵的优化研究

为了优化高斯矩阵,对部分哈达玛矩阵与高斯矩阵的统计学参数作了对比分析,确定了导致部分哈达玛矩阵信号重构能力好的主要因素,并提出了高斯矩阵优化算法.验证了优化算法对不同规模高斯矩阵的通用性和有效性,确定了优化矩阵对各种稀疏信号重构算法的适用性.最后对优化矩阵的性能作了初步的理论和实验分析,优化矩阵的信号重构能力可达到、甚至超过哈达玛矩阵.研究成果为测量矩阵的分析、设计和优化提供了新的思路和方法.     

面向大规模数据流处理的偏最小二乘法

用光谱分析鉴别生物特征,导致数据量大,而实际需要必须实时处理。偏最小二乘法是使用最广泛的鉴别算法,但是对于大规模数据流该算法无法达到实时性。为了解决这个应用矛盾,提出了一种基于NVIDIA CUDA架构下的并行计算策略,利用具有大规模并行计算特征的图形处理器（GPU）作为计算设备,结合GPU存储器的优势实现了偏最小二乘算法。实验的测试结果表明,在GPU上使用CUDA实现的偏最小二乘算法比在CPU上实现该算法快了47倍,性能得到了显著提高,从而使偏最小二乘算法应用于大规模数据流处理成为可能。