Based on the theory of peridynamics, least squares and moving method are proposed to fit physical information at nondiscrete points. It makes up for shortcomings peridynamic that only solves discrete nodes cannot obtain other blank areas. The extended is used one-way vibration problem rod, curve displacement a node in rod extracted with time. fitted results compared theoretical verify feasibili...

We present an algorithm to approximate a set of unorganized points with a simple curve without self-intersections. The moving least-squares method has a good ability to reduce a point cloud to a thin curve-like shape which is a near-best approximation of the point set. In this paper, an improved moving least-squares technique is suggested using Euclidean minimum spanning tree, region expansion ...

In this paper, we compare the performance of different agent’s investment strategies in an investment scenario with periodic returns and different types and levels of noise. We consider an investment model, where an agent decides the percentage of budget to risk at each time step. Afterwards, agent’s investment is evaluated in the market via a return on investment (RoI), which we assume is a st...

This paper addresses the definition, contouring, and visualization of scalar functions on unorganized point sets, which are sampled from a surface in 3D space; the proposed framework builds on moving least-squares techniques and implicit modeling. Given a scalar function f : P → R, defined on a point set P , the idea behind our approach is to exploit the local connectivity structure of the k-ne...

Analysis of high dimensional data is a common task. Often, small multiples are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot matrix. Associating data points between different views can be difficult though, as the points are not fixed. Other times, dimensional reduction techniques are employed to summarize the whole dataset in one image, but individual dimensions are lo...