Papers by Keyword: Discrete Data

Abstract: Current industrial-hardface reinforcements presentdiverse geometrical shapes, reinforcement-particle size, chemicalcompounds proportions, volume-spatial-distribution, and chemicalcomposition—several kinds of manufactured material types orrecycling ones. Functional Erosion Models, namely, Integral-Differential Models, implement hardness spatial-distributionfunctions to determine accurately the erosion wear magnitude alongthe hardface [Casesnoves, 2016-7]. In previous contributions,several models were simulated/optimized in erosion impact wear overthese hardness differentiable functions—with separated mathematicalanalysis for matrix and reinforcement. This research is focused onextent numerical-computational comparison between two types ofreinforcements manufactured with the same hardface matrix andmanufactured with equal alloy substrate. The simulations, based onlarge laboratory data, are performed from hardface hardness part-distributions to complete/total hardface hardness plottings.Programming software was developed in two kinds of compilators,that is, Freemat and Matlab. Results involve both numerical anduseful graphical determinations for further modellingimplementation. Practical engineering conclusions for erosionfunctional algorithms and accurate tribological models, andrecycling engineering industry, are obtained from the study.

Abstract: In order to optimize the big data management of smart grid construction, analyze and process the extreme weather grid measuring point data of grid operation data which is abundant and discrete with mathematics. Considering of the continuous changing characteristics of weather in effective field of surface, buffer and expand the influential range of grid measuring point, then merge the same data, embellish the data with B-spline curve, store the spatial coordinates in enterprise-class grid resource centre, publish in map with unified service, and show the visual graphical distribution of extreme weather through OGC protocol, realize the visualization of grid resource.

Abstract: Sweep is useful in CAD modeling process. But its precision is hard to control when we use discrete modeling method. In this paper, the sweep surface is constructed by projected-based approach. Vector and distance methods are proposed to detect the local self-intersection of sweep surface. Beside these, it gives the precision control methods in the corner of sweep surface to improve the precision. Finally, we discuss the factors that influence the precision of sweep surface.

Abstract: The paper researches the general procedure and method of 3D modeling on the mine terrain surface. The terrain modeling is being reconstructed with Delaunay triangular network. On this basis, Bezier triangular surface is adopted to approach, which effectively solves the problems of unsmooth surface and large amount of data caused by 3D reconstruction.

Abstract: To address problems of Least squares method (LSM) fitting curves in application domains, the essay attempts to build a new model by using LMS (Least Median Squares) to analyze the error points, and pretreating the dynamic measuring errors and then getting the fitting curves of testing data. This model is used for electromotor parameters testing which includes load testing and unload testing. Experiments show that the model can erase the influence of outline points, while improving the effects of data curve fitting and reflecting the characteristic of the motor, provide more accurate data fitting curve with small sample data that is in discrete distribution compared with Least squares method.

Abstract: Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

Abstract: The uncertainty variable in mechanical design is expressed in blind number and the relation among variables is expressed in the operation rule of blind number; By combining the reliability design theory with blind number calculating method, the reliability of the mechanical elements with arbitrary distribution parameters and discrete test data is discussed; and a numerical method for mechanical reliability based on blind numbers is presented. Besides, the typical examples show that the method is reasonable and effective.