Structural Damage Assessment by Using Stiffness-based Method and Cubic Spline Interpolation
نویسندگان
چکیده
منابع مشابه
Monotonic Cubic Spline Interpolation
This paper describes the use of cubic splines for interpolating monotonic data sets. Interpolating cubic splines are popular for fitting data because they use low-order polynomials and have C2 continuity, a property that permits them to satisfy a desirable smoothness constraint. Unfortunately, that same constraint often violates another desirable property: monotonicity. The goal of this work is...
متن کاملImage Compression and Reconstruction using Cubic Spline Interpolation Technique
A new dimension of image compression using random pixels of irregular sampling and image reconstruction using cubic-spline interpolation techniques proposed in this paper. It also covers the wide field of multimedia communication concerned with multimedia messaging (MMS) and image transfer through mobile phones and tried to find a mechanism to transfer images with minimum bandwidth requirement....
متن کاملPiecewise cubic interpolation of fuzzy data based on B-spline basis functions
In this paper fuzzy piecewise cubic interpolation is constructed for fuzzy data based on B-spline basis functions. We add two new additional conditions which guarantee uniqueness of fuzzy B-spline interpolation.Other conditions are imposed on the interpolation data to guarantee that the interpolation function to be a well-defined fuzzy function. Finally some examples are given to illustrate the...
متن کاملUNIQUENESS OF BEST PARAMETRIC INTERPOLATION BY CUBIC SPLINE CURVES by
Best parametric spline interpolation extends and refines the classical spline problem of best interpolation to (∗) inf t inf f { ∫ 1 0 ‖f (t)‖dt : f(ti) = yi, 1 ≤ i ≤ n} Here t : 0 = t1 < . . . < tn = 1 denotes a sequence of nodes and yi data in R d with y i 6= y i+1 . The R valued functionsf(t) lie componentwise in the Sobolev space L2(0, 1) and ‖ ‖ denotes the Euclidean norm in R. This proble...
متن کاملLearning activation functions from data using cubic spline interpolation
Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is a crucial step, which remains an open problem in the research community. Despite a large amount of investigations, most current implementations simply select one fixed function from a small set of candidates, which i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: MATEC Web of Conferences
سال: 2018
ISSN: 2261-236X
DOI: 10.1051/matecconf/201820101004