On the estimation of nonlinear time series models

In the present paper, a framework for parametric estimation in nonlinear time series is developed. Strong consistency and asymptotic normality of minimum Hellinger distance estimates for a determined class of nonlinear models are investigated. The main Interest for these estimates is motivated by their robustness under perturbations as it has been emphazized in Beran [2]. The first part of the paper is devoted to the study of some probabilistic properties which ensure the existence and the optimal properties of the estimates     

Time optimality and the maximum principle in infinite dimension

We present three counterexamples related to the maximum principle for the time optimal problem. The system is linear, infinite dimensional, with point target and “full” control     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Linear codes associated to skew-symmetric determinantal varieties

In this article we consider linear codes coming from skew-symmetric determinantal varieties, which are defined by the vanishing of minors of a certain fixed size in the space of skew-symmetric matrices. In odd characteristic, the minimum distances of these codes are determined and a recursive formula for the weight of a general codeword in these codes is given.     

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.     

气驱油油气混相过程的界面传质特性及其分子机制

油气混相过程的界面传质特性对气驱提高原油采收率技术非常重要。本文针对吉林某油田的实际油组分,采用分子动力学模拟研究了气驱油过程,分析了不同气体和驱替压力下油气两相的状态变化以及界面特性,获得不同驱替气体的最小混相压力(MMP)。结果表明,随着驱替气体压力的升高,气相的密度逐渐增大,油相膨胀密度降低,气相与油相的混合程度增强,油气两相界面厚度增加,界面张力随之减小。同时发现,驱替相中二氧化碳浓度越高,在同等气体压力下,油气界面更厚,油气混合程度更高。纯CO₂驱油得到的MMP远远小于纯N₂驱油,当这两种气体摩尔比为1 : 1混合时MMP介于两种纯气体之间,说明要达到同样的驱油效果二氧化碳需要的压力更小。最后,本文从分子微观作用力角度解释了驱替气体不同时影响油气混相程度的机制,通过分子平均作用势曲线发现油相分子对CO₂的吸引力要大于N₂分子,因此CO₂分子更容易与油相混合,驱替效果更明显。     

Fluidization characteristics of sawdust in a cold flow fluidized bed

India has abundance of biomass such as rice husk, bagasse, wheat straw, sawdust etc. which is used as a main or auxiliary fuel in the fluidized bed combustor, gasifier and pyrolizer. Design of such fluidized bed equipments require the knowledge of minimum fluidization velocity (U_mf), complete fluidization velocity (U_cf) and transport disengagement height (TDH). The present work reports the fluidization characteristic, U_mf, U_cf and TDH of the individual size groups of sawdust and mixture thereof. The results indicate that the U_mf and U_cf have a tendency to increase with increase in particle diameter, however the TDH shows the reverse trend. The sawdust particle size of 925 and 1200 ​μm showed significant difference between their U_mf and U_cf, an essential parameter for controlled fluidization. Based on the experimental work new correlations for the prediction of U_mf, U_cf and TDH for sawdust sample are proposed. The proposed correlations of U_mf, U_cf and TDH are in good agreement with experimental values and the deviations found within the range of nearly ±10% for all the samples.     

机动目标逆合成孔径激光雷达方位成像快速算法

针对逆合成孔径激光雷达对机动目标成像时存在方位多普勒时变的问题,提出了一种基于方位时频域keystone变换的机动目标逆合成孔径激光雷达方位成像快速算法.利用多分量线性调频子回波信号的调频斜率与起始频率的比值为常量这一特点,在方位时频域采用keystone变换将多分量线性调频信号同时转换为多分量单频信号,利用快速傅里叶变换实现方位聚焦.采用基于分数阶傅里叶变换和最小熵的线性调频参量估计方法,实现了对调频斜率与起始频率比值的精确、快速估计.结果表明,与现有的基于Radon-Wigner变换的距离-瞬时多普勒成像算法相比,所提出的算法成像效率大大提高,且能够保留更多的目标细节信息,适合于逆合成孔径激光雷达的实时成像.     

Effect of Gaussian size distribution width on minimum fluidization velocity in tapered gas–solid fluidized beds

This paper investigated the effect of Gaussian distribution width, average particle diameter, particle loading, and the tapered angle on minimum fluidization velocity (U_mf) by conducting extensive experiments in tapered fluidized beds. Three powders with Gaussian size distribution and different distribution widths were used in the experiments. An increase in U_mf with increasing the average particle diameter, particle loading, and the tapered angle was observed. There was also a nonmonotonic behavior of U_mf as the Gaussian distribution width increased. An empirical correlation including dimensionless groups for predicting U_mf in tapered beds was developed in which the effect of distribution width was considered. The proposed correlation predictions were in good agreement with the experimental data, with a maximum deviation of 16.5% and average and standard deviations of, respectively, 6.4% and 7.4%. The proposed correlation was also compared with three earlier models, and their accuracy was discussed.     

Gaussian states as minimum uncertainty states

In bosonic fields, Gaussian states, which consist of a rather wide family of states including coherent states, squeezed states, thermal states, etc., have many classical-like features, and are usually defined from the mathematical perspective in terms of characteristic functions. It is well known that some special Gaussian states, such as coherent states, are minimum uncertainty states for the conventional Heisenberg uncertainty relation involving canonical pair of position and momentum observables. A natural question arises as whether all Gaussian states can be characterized as minimum uncertainty states. In this work, we show that indeed Gaussian states coincide with minimum uncertainty states for an information-theoretic refinement of the conventional uncertainty relation established in Luo (2005) [40]. This characterization puts Gaussian states on a novel basis of physical significance.