According to the existing problems in the development of actual product and working process of excavator working device’s boom, the method of integrated design and analysis for excavator boom is put forward. The defects in the process of CAD/CAE integration are summarized, the integrated design and analysis system including parametric design of boom, database design, detail feature simplificati...

Reynolds’ theory of relational parametricity formalizes parametric polymorphism for System F, thus capturing the idea that polymorphically typed System F programs always map related inputs to related results. This paper shows that Reynolds’ theory can be seen as the instantiation at dimension 1 of a theory of relational parametricity for System F that holds at all higher dimensions, including i...

Recently, several attempts have been made for deriving datadependent kernels from distribution estimates with parametric models (e.g. the Fisher kernel). In this paper, we propose a new kernel derived from any distribution estimators, parametric or nonparametric. This kernel is called the Leave-one-out kernel (i.e. LOO kernel), because the leave-one-out process plays an important role to comput...

The leader node particle filter is a partially distributed approach to tracking in a sensor network, in which the node performing the particle filter computations (the leader node) changes over time. The primary advantage is that the position of the leader node can follow the target, improving the efficiency of data collection. When the leader node changes, the particle filter must be communica...

The primary nature of the C neutron source is very significant for the studies of the s-process nucleosynthesis. In this paper we present an attempt to fit the element abundances observed in 16 s-rich stars using parametric model of the single neutron exposure. The calculated results indicate that almost all s-elements were made in a single neutron exposure for 9 sample stars. Although a large ...

In Bayesian paradigm of survival analysis, we can combine a nonparametric estimator and a parametric model by putting a prior distribution nonparametrically around the entire parametric family. This method can avoids the ineeciency of the nonparametric estimator due to ignoring partial information about a parametric model and at the same time avoids the pitfalls connected with an incorrectly sp...

This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite-dimensional parametric family against a nonparametric alternative. The test does not require nonparametric estimation of g and is not subject to the illposed inverse problem of no...

Many approaches can track objects moving in sequences of images but can su3er in occlusion and noise, and often require initialisation. These factors can be handled by techniques that extract objects from image sequences, especially when phrased in terms of evidence gathering. Since the template approach is proven for arbitrary shapes, we re-deploy it for moving arbitrary shapes, but in a way a...

Probability models of income distribution provide for evaluation of the living standard of population of a country at whole as well as for comparison of the living standard of different social classes or regions in the country. For creation of a probability model it is important both, to find a theoretical distribution function that characterizes empirical frequency distribution and to choose s...

We reformulate algorithm selection as a time allocation problem: all candidate algorithms are run in parallel, and their relative priorities are continually updated based on its current time to solution, estimated according to a parametric model that is trained and used while solving a sequence of problems.