A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

Reducing numerical dissipation in smoke simulation

Numerical dissipation acts as artificial viscosity to make smoke viscous. Reducing numerical dissipation is able to recover visual details smeared out by the numerical dissipation. Great efforts have been devoted to suppress the numerical dissipation in smoke simulation in the past few years. In this paper we investigate methods of combating the numerical dissipation. We describe visual consequences of the numerical dissipation and explore sources that introduce the numerical dissipation into course of smoke simulation. Methods are investigated from various aspects including grid variation, high-order advection, sub-grid compensation, invariant conservation, and particle-based improvement, followed by discussion and comparison in terms of visual quality, computational overhead, ease of implementation, adaptivity, and scalability, which leads to their different applicability to various application scenarios.     

Improved formic acid oxidation using electrodeposited Pd–Cd electrocatalysts in sulfuric acid solution

In the present research, nanostructured Pd–Cd alloy electrocatalysts with different compositions were produced using the electrodeposition process. The morphology of the samples was studied by scanning electron microscopy analysis. Also, the elemental composition of the samples was determined by energy-dispersive X-ray spectroscopy and elemental mapping tests. Tafel polarization and electrochemical impedance spectroscopy methods were employed to determine the electrochemical corrosion properties of the synthesized samples in a solution containing 0.5 M sulfuric acid and 0.1 M formic acid. The linear sweep voltammetry, cyclic voltammetry, and chronoamperometry techniques were also employed to evaluate the electrocatalytic activity of prepared samples toward the oxidation of formic acid. In this respect, the influence of some factors such as formic acid and sulfuric acid concentrations and also potential scan rate was investigated. Compared to the pure Pd sample, the Pd–Cd samples were more reactive for the oxidation of formic acid. Besides, the sample with a lower amount of Pd (Pd_1·3Cd) demonstrated much higher electrocatalytic activity than the Pd_7·1Cd and Pd_2·1Cd samples. The observed high mass activity of 15.06 A mg^?1_Pd for the Pd_1·3Cd sample which is 21.1 times higher than Pd/C is an interesting result of this study.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

A Lattice Approach to Image Segmentation

After a formal definition of segmentation as the largest partition of the space according to a criterion σ and a function f, the notion of a morphological connection is reminded. It is used as an input to a central theorem of the paper (Theorem 8), that identifies segmentation with the connections that are based on connective criteria. Just as connections, the segmentations can then be regrouped by suprema and infima. The generality of the theorem makes it valid for functions from any space to any other one. Two propositions make precise the AND and OR combinations of connective criteria. The soundness of the approach is demonstrated by listing a series of segmentation techniques. One considers first the cases when the segmentation under study does not involve initial seeds. Various modes of regularity are discussed, which all derive from Lipschitz functions. A second category of examples involves the presence of seeds around which the partition of the space is organized. An overall proposition shows that these examples are a matter for the central theorem. Watershed and jump connection based segmentations illustrate this type of situation. The third and last category of examples deals with cases when the segmentation occurs in an indirect space, such as an histogram, and is then projected back on the actual space under study. The relationships between filtering and segmentation are then investigated. A theoretical chapter introduces and studies the two notions of a pulse opening and of a connected operator. The conditions under which a family of pulse openings can yield a connected filter are clarified. The ability of segmentations to generate pyramids, or hierarchies, is analyzed. A distinction is made between weak hierarchies where the partitions increase when going up in the pyramid, and the strong hierarchies where the various levels are structured as semi-groups, and particularly as granulometric semi-groups. The last section is based on one example, and goes back over the controversy about “lattice” versus “functional” optimization. The problem is now tackled via a case of colour segmentation, where the saturation serves as a cursor between luminance and hue. The emphasis is put on the difficulty of grouping the various necessary optimizations into a single one.     

叠前地震数据优化处理技术分析

由于叠前时间偏移成像是在叠前数据上进行的，因此对数据的质量要求较高。针对这一问题进行了数据优化处理技术研究，研究的基础资料是胜利油田渤深6区块的三维地震数据，首先对资料的质量进行了分析一由于是拼接资料，因此存在着能量不均衡、覆盖次数不均匀、坡形不一致以及各类干扰发育等问题；然后针对这些问题研究了时间-空间域内分频随机噪声衰减、频率一空间域相干噪声衰减、波形匹配处理和数据拟合三维随机噪声衰减等4种处理方法，分别取得了很好的处理效果；在对实际资料进行数据优化处理时，根据资料的特点采用了合适的组合方式，处理后资料的品质得到了较大提高，相干噪声、随机噪声得到了有效衰减，波形一致性得到了恢复，中、深层有效频率和有效能量得到了加强。     

一类Schrdinger方程组解的爆破

本文研究一类Schrodinger方程组解的爆破行为,在Y.Tsutsumi等(1998)文的基础上对非线性项作了修改,并用变分的方法得到了解在有限时间内爆破的一个充分条件。     

Taxometrics in Psychopathology Research: An Introduction to Some of the Procedures and Related Methodological Issues.

This article introduces a special section on the use of taxometrics to examine the categorical versus the dimensional structure of various forms of psychopathology. Paving the way into the special section, this introduction briefly describes 3 taxometric methods--mean above minus below a sliding cut (MAMBAC), maximum covariation (MAXCOV), and maximum eigenvalue (MAXEIG)--and discusses possible threats to statistical conclusion validity that often emerge when such techniques are applied in psychopathology research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)