等离子体刻蚀的数学模型

介绍了等离子体刻蚀中的三种主要的数学模型 ,即 :物理模型、反应表面模型、神经网络模型。简要地叙述了它们的原理和优缺点。     

协方差结构模型分析中的模型等同问题

协方差结构模型分析是近年来逐渐流行的统计分析技术。文章主要讨论了应用该技术中出现的模型等同问题。其一,何为等同模型及如何识别某个模型的备择等同模型;其二,以一个实例说明模型等同问题在研究中所造成的影响;其三,如何处理模型等同问题。     

On the predictive ability of mechanistic models for the Haitian cholera epidemic

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.     

经典彩色预测模型

针对印刷中原稿和复制品之间存在的色差这一事实,详细介绍了至今为止的几种经典彩色预测模型:如直接针对半色调印刷过程的Neugebauer方程,Yule-Nielsen模型和基于减色原理的Kubelka-Munk理论.通过调节模型中的变量来达到控制色差的目的.     

Updating outdated predictive accident models

Reliable predictive accident models (PAMs) (also referred to as safety performance functions (SPFs)) are essential to design and maintain safe road networks however, ongoing changes in road and vehicle design coupled with road safety initiatives, mean that these models can quickly become dated. Unfortunately, because the fitting of sophisticated PAMs including a wide range of explanatory variables is not a trivial task, available models tend to be based on data collected many years ago and seem unlikely to give reliable estimates of current accidents. Large, expensive studies to produce new models are likely to be, at best, only a temporary solution. This paper thus seeks to develop a practical and efficient methodology to allow currently available PAMs to be updated to give unbiased estimates of accident frequencies at any point in time. Two principal issues are examined: the extent to which the temporal transferability of predictive accident models varies with model complexity; and the practicality and efficiency of two alternative updating strategies. The models used to illustrate these issues are the suites of models developed for rural dual and single carriageway roads in the UK. These are widely used in several software packages in spite of being based on data collected during the 1980s and early 1990s. It was found that increased model complexity by no means ensures better temporal transferability and that calibration of the models using a scale factor can be a practical alternative to fitting new models.     

网上三种信任模型的分析与比较

信任是信赖他人的一种意愿,是一个抽象的、跨学科的概念,是阻碍电子商务发展的最大的障碍之一.在现有信任研究成果的基础上,将网上信任的研究内容归纳概括为:网上信任概念及概念模型、网上信任实体模型和网上信任实证研究模型,并分别从这三方面对信任研究进行了阐述.最后,比较分析了这三大模型的研究目的、研究对象和研究方法的差异.     

Verification of optimal models for 2D-full loop simulation of circulating fluidized bed

To verify the optimal models for a two-dimensional (2D) full loop simulation of a circulating fluidized bed (CFB), different turbulent models and drag models are studied according to relevant pressure profile, voidage distribution and particle collision energy. With regard to a laminar model and turbulent models including Standard k-ε, RNG k-ε and Realizable k-ε, the experimental data reveals that the RNG k-ε model is the best at predicting pressure, voidage, axial solid velocity and granular temperature. Besides, through the comparison of four drag models, it is found that the Gidaspow model can achieve a higher accuracy of prediction. Therefore, it can be concluded that the combination of the RNG and Gidaspaw models is suitable for the 2D full loop simulation of a CFB, and therefore potential models for the prediction of flow characteristics.     

Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities

The conventional methods for crash injury severity analyses include either treating the severity data as ordered (e.g. ordered logit/probit models) or non-ordered (e.g. multinomial models). The ordered models require the data to meet proportional odds assumption, according to which the predictors can only have the same effect on different levels of the dependent variable, which is often not the case with crash injury severities. On the other hand, non-ordered analyses completely ignore the inherent hierarchical nature of crash injury severities. Therefore, treating the crash severity data as either ordered or non-ordered results in violating some of the key principles. To address these concerns, this paper explores the application of a partial proportional odds (PPO) model to bridge the gap between ordered and non-ordered severity modeling frameworks. The PPO model allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. This study is based on a five-year (2008–2012) national pedestrian safety dataset for Switzerland. A comparison between the application of PPO models, ordered logit models, and multinomial logit models for pedestrian injury severity evaluation is also included here. The study shows that PPO models outperform the other models considered based on different evaluation criteria. Hence, it is a viable method for analyzing pedestrian crash injury severities.     

Applicability of damage models for failure analysis of threaded bolts

Damage models have been applied in engineering applications. In the present work both Gurson–Tvergaard–Needleman (GTN) model and the progressive damage model have been employed to describe plastic deformation and damage behaviour in the tensile bars and threaded bolts. The predictive capabilities of the models are critically assessed. The parameters in the damage models are identified from uniaxial tensile tests. The models are applied to predict failure of the threaded bolts. Numerical results reveal both models agree with experimental data in uniaxial tension and in threaded bolts. The overall results do not show substantial differences. However, damage evolutions from both damage models display very different developments. Whereas the GTN model confirms damage nucleation in the specimen center of the uniaxial tensile bars, as observed in experiments, the progressive damage model predicts damage starts from the specimen surface where the plastic strain reaches its maximum. In threaded bolts both models do not show significant deviations in predicting the ultimate loading. The progressive damage model without stress triaxiality consideration predicts an unrealistic ductile fracture process and cannot be applied to metallic materials.     

基于云模型的信任评估研究

讨论了信任关系的随机性和模糊性共存以及相互融合问题。分析云模型描述不确定性概念的方法和实现定性语意与定量数值相互转换的算法,提出了基于云理论的信任评估模型—信任云。该模型提出云特征参数表达的信任传递和合并算法,在精确描述信任期望值的同时,通过熵和超熵刻画了信任的不确定性。相对于传统的信任评估策略,该模型获取的信任值包含更多的语意信息,更适合作为信任决策的依据。     

格构式输电塔结构多质点简化模型研究

采用“串联多质点系”力学模型,建立了典型500kV酒杯型输电塔和鼓型输电塔简化模型。根据高频动态测力天平风洞试验结果,计算了输电塔简化模型的风致响应,与根据实际结构有限元模型的结果进行了比较,对简化模型的计算误差及适用性进行了分析。结果表明,简化模型不适合用于格构式结构局部杆件的响应分析,但在计算反映结构整体特性的响应时具有较高的精度。     

Structural dynamic modifications via models

Structural dynamic modification techniques attempt to reduce dynamic design time and can be implemented beginning with spatial models of structures, dynamic test data or updated models. The models assumed in this discussion are mathematical models, namely mass, stiffness, and damping matrices of the equations of motion of a structure. These models are identified/ extracted from dynamic test data viz. frequency response functions (FRFs). Alternatively these models could have been obtained by adjusting or updating the finite element model of the structure in the light of the test data. The methods of structural modification for getting desired dynamic characteristics by using modifiers namely mass, beams and tuned absorbers are discussed.     

超滤通量模型的研究进展

超滤膜通量模型的研究对超滤技术的广泛应用具有重要的意义.重点阐述了超滤过程中几种主要通量预测模型的研究情况,并简要展望了超滤通量模型的发展方向.     

Underreporting in traffic accident data, bias in parameters and the structure of injury severity models

Injury severities in traffic accidents are usually recorded on ordinal scales, and statistical models have been applied to investigate the effects of driver factors, vehicle characteristics, road geometrics and environmental conditions on injury severity. The unknown parameters in the models are in general estimated assuming random sampling from the population. Traffic accident data however suffer from underreporting effects, especially for lower injury severities. As a result, traffic accident data can be regarded as outcome-based samples with unknown population shares of the injury severities. An outcome-based sample is overrepresented by accidents of higher severities. As a result, outcome-based samples result in biased parameters which skew our inferences on the effect of key safety variables such as safety belt usage. The pseudo-likelihood function for the case with unknown population shares, which is the same as the conditional maximum likelihood for the case with known population shares, is applied in this study to examine the effects of severity underreporting on the parameter estimates. Sequential binary probit models and ordered-response probit models of injury severity are developed and compared in this study. Sequential binary probit models assume that the factors determining the severity change according to the level of the severity itself, while ordered-response probit models assume that the same factors correlate across all levels of severity. Estimation results suggest that the sequential binary probit models outperform the ordered-response probit models, and that the coefficient estimates for lap and shoulder belt use are biased if underreporting is not considered. Mean parameter bias due to underreporting can be significant. The findings show that underreporting on the outcome dimension may induce bias in inferences on a variety of factors. In particular, if underreporting is not accounted for, the marginal impacts of a variety of factors appear to be overestimated. Fixed objects and environmental conditions are overestimated in their impact on injury severity, as is the effect of separate lap and shoulder belt use. Combined lap and shoulder belt usage appears to be unaffected. The parameter bias is most pronounced when underreporting of possible injury accidents in addition to property damage only accidents is taken into account.     

Application of finite mixture models for vehicle crash data analysis

Developing sound or reliable statistical models for analyzing motor vehicle crashes is very important in highway safety studies. However, a significant difficulty associated with the model development is related to the fact that crash data often exhibit over-dispersion. Sources of dispersion can be varied and are usually unknown to the transportation analysts. These sources could potentially affect the development of negative binomial (NB) regression models, which are often the model of choice in highway safety. To help in this endeavor, this paper documents an alternative formulation that could be used for capturing heterogeneity in crash count models through the use of finite mixture regression models. The finite mixtures of Poisson or NB regression models are especially useful where count data were drawn from heterogeneous populations. These models can help determine sub-populations or groups in the data among others. To evaluate these models, Poisson and NB mixture models were estimated using data collected in Toronto, Ontario. These models were compared to standard NB regression model estimated using the same data. The results of this study show that the dataset seemed to be generated from two distinct sub-populations, each having different regression coefficients and degrees of over-dispersion. Although over-dispersion in crash data can be dealt with in a variety of ways, the mixture model can help provide the nature of the over-dispersion in the data. It is therefore recommended that transportation safety analysts use this type of model before the traditional NB model, especially when the data are suspected to belong to different groups.     

The analysis of ordered categorical data: An overview and a survey of recent developments

This article review methodologies used for analyzing ordered categorical (ordinal) response variables. We begin by surveying models for data with a single ordinal response variable. We also survey recently proposed strategies for modeling ordinal response variables when the data have some type of clustering or when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. Primary models in that case includemarginal models andcluster-specific (conditional) models for which effects apply conditionally at the cluster level. Related discussion refers to multi-level and transitional models. The main emphasis is on maximum likelihood inference, although we indicate certain models (e.g., marginal models, multi-level models) for which this can be computationally difficult. The Bayesian approach has also received considerable attention for categorical data in the past decade, and we survey recent Bayesian approaches to modeling ordinal response variables. Alternative, non-model-based, approaches are also available for certain types of inference. This work was partially supported by a grant for A. Agresti from NSF and by a research study leave grant from Victoria University for I. Liu.     

Can we learn more from the data underlying the statistical α-β model with respect to the dynamical behavior of avalanches?

Hazard and risk assessment in avalanche-prone areas involves estimation of runout distances of potential avalanches. Methods for determination of the runout may be divided into two categories: 1) methods based on statistical approaches such as the well known α-β model or 2) methods based on numerical avalanche models such as the PCM-model or VS-type models (just to name the more traditional ones). Methods in the second group have the advantage that besides the runout distance, velocity and impact pressure distributions along the avalanche track can also be obtained, this being a requisite for meaningful risk assessments. However, the predictive power of dynamical models depends on the use of appropriate rheological models and their parameters.In the statistical α-β model, the maximum runout distance is solely a function of topography. The runout distance equations were found by regression analysis, correlating the longest registered runout distance of several hundred avalanche paths with a selection of topographic parameters.In this paper, we re-evaluate Norwegian and Austrian avalanche data, which served as basis for the α-β model in the respective countries, and additional avalanche data with respect to dynamical measures. As most of those avalanche data originate more or less from extreme events (i.e. avalanches with return periods of the order of 100 years), the dynamical measures may give hints about an appropriate rheology for dynamical models suitable for extreme avalanche events.The analysis raises reasonable doubt whether the classical ansatz for the retarding acceleration of snow avalanches with additive terms involving Coulomb-friction and a velocity-squared dependency, which is used in many avalanche models, is adequate for a physically-based model. Back-calculations of runout distances using a simple block model show a discrepancy between commonly proposed parameter values (and of the underlying rheological models) and the observations.     

TWO SECTIONAL MODELS INVOLVING THREE WEIBULL DISTRIBUTIONS

This paper deals with two sectional models each involving three Weibull distributions. It presents a characterization of the different shapes for the density and the failure rate functions for these models. It also discusses the Weibull Plotting Paper (WPP) plots to determine the suitability of such models to model a given failure data set. © 1997 by John Wiley & Sons, Ltd.