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Polarization of Light Scattered by Solar System Bodies and the Aggregate Model of Dust Particles


It was shown that, in the framework of the aggregate model, the behavior of polarization phase curves observed for both comets and regolith surfaces can be explained.


Consequently, an alternate aggregate model that retains the principal features of the KPC problem is formulated.


The proposed heuristic procedure enables us to derive solutions for practical sized problems that could not be handled by directly solving even the aggregate model.


If the market is heterogeneous, however, an aggregate model entails a misspecification problem which could adversely affect the applicability and efficiency of the model.

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 There are basically two kinds of methodologies to forecast ownership of private vehicle: aggregate and disaggregate model. Aggregate ones could be further divided into models with and without limitation onsaturation. Both application scope and data required by these two kinds of models are different,as well theiradvantages and disadvantages. The aggregate model is often used for macropolicy analysis of longterm forecastand the later one usually for microanalysis and shortterm forecast. The disaggregate model... There are basically two kinds of methodologies to forecast ownership of private vehicle: aggregate and disaggregate model. Aggregate ones could be further divided into models with and without limitation onsaturation. Both application scope and data required by these two kinds of models are different,as well theiradvantages and disadvantages. The aggregate model is often used for macropolicy analysis of longterm forecastand the later one usually for microanalysis and shortterm forecast. The disaggregate model requires more data.  目前预测私人汽车拥有率的方法主要有集合模型和非集合模型两类,前者又可以细分为有饱和水平限制和没有饱和水平限制两种。这两种方法的应用范围和所要求的数据各不相同,各有优缺点。集合方法多用于宏观政策分析或远期预测,非集合方法多用于微观政策分析或短期预测,数据要求比较高。  Structural design is subject to uncertainties in material properties,loads and other variables.Reliability analysis for uncertainties has been an important subject in theory and engineering.Boundedbutunknown uncertainty is another kind of uncertainty under research following probabilistic and fuzzy.Interval variables and convex model are usually used to quantify the boundedbutunknown uncertainty.In recent years,some literatures have pointed that an nonprobabilistic measure of reliability can be an alternative... Structural design is subject to uncertainties in material properties,loads and other variables.Reliability analysis for uncertainties has been an important subject in theory and engineering.Boundedbutunknown uncertainty is another kind of uncertainty under research following probabilistic and fuzzy.Interval variables and convex model are usually used to quantify the boundedbutunknown uncertainty.In recent years,some literatures have pointed that an nonprobabilistic measure of reliability can be an alternative to the classical probabilistic reliability for boundedbutunknown uncertainties and then some formulations are brought forward.In this paper,formulations in previous literatures are discussed and compared.We also study the possibility of using the probabilistic reliability analysis to tackle the boundedbutunknown uncertainties.  实际工程中大量存在不确定性因素,处理不确定性因素的可靠性逐渐成为科学和工程中一个非常重要的概念。区间不确定性是继随机性和模糊性之后被人们研究的又一种不确定性。区间不确定性一般可由区间变量或凸集合模型来描述。近年来,有些文献针对区间不确定性提出了计算非概率可靠性的方法。本文对这些方法进行比较和讨论,并和假定各区间不确定参量在允许取值区间内为具有熵最大的矩形分布,采用概率可靠度的理论来处理问题得到的结果进行了比较。  >=Multiple model estimation method is a powerful, robust, and adaptive method. The so  called multiple model (MM) method, The basic idea of the multiple model estimation approach is to assume a set of models M for the hybrid system; run a bank of filters, each based on a unique model in the set; and the overall estimate is given by a certain combination of the estimates from these filters. The VSMM approach addresses some of the shortcomings of FSMM estimation by adaptively varying the number of models. VSMM... >=Multiple model estimation method is a powerful, robust, and adaptive method. The so  called multiple model (MM) method, The basic idea of the multiple model estimation approach is to assume a set of models M for the hybrid system; run a bank of filters, each based on a unique model in the set; and the overall estimate is given by a certain combination of the estimates from these filters. The VSMM approach addresses some of the shortcomings of FSMM estimation by adaptively varying the number of models. VSMM appears to be more promising than FSMM when a large number of models is involved. Most implementable VSMM algorithms consist of IMM type filters with an adap tive model set but they differ in the techniques used for the time  varying model set adaptation and thus differ in their tracking performance. it has received an great deal of attention in recent years due to its unique power and great recent success in handling problems with both structural and parametric uncertainties and/or changes, and in decomposing a complex problem into simpler sub problems, Ranging from target tracking to fault detection and isolation, and from biomedical signal processing to process control .A new VSMM method called minimal submodel set switching (MSMSS) algorithm is presented. The proposed MSMSS algorithm uses the smallest number of models needed to capture the true system mode in effect at any time and include all possible true system modes that the current mode can jump to in the next time step. Their transition probabilities are determined from the model transition probability matrix of the full model set.The performance of the proposed algorithms are evaluated and compared with IMM under several realistic scenarios. Simulation results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.  多模型估计方法一种很有力的自适应方法。在MM方法中设计一系列模型来代表可能的系统行为方式或结构,称为系统模式。整体估计通过并行工作的基于与特定系统模式匹配的模型的滤波器的估计的一个确定组合得到。当前的研究主要有固定结构和变结构多模型方法研究。变结构多模型估计方法是为了解决当前固定结构多模型估计方法存在的问题而提出的,是使用变化的模型集合的多模型算法。变结构多模型算法特别适合应用于解决结构和/或参数未知的估计问题,在机动目标跟踪、失效检测和隔离、生物医疗信号处理以及过程控制等领域有着广阔的应用前景。本文研究了一种新的变结构多模型算法最小子模型集合切换(MSMSS)算法。其基本思想是从总的模型集中自适应确定最小模型集合,用来完成多模型(MM)估计:用一个核心模型去描叙最可能的正确系统模式;用与核心模型相连的边缘模型用来描叙其他可能正确的系统模式;用整个模型集合的模型转移概率矩阵来确定他们的转移概率。通过仿真研究了其对机动目标跟踪的性能,并与对应的交互多模型算法进行了比较,可以看出其性能有了较大的改进。  
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