Plating thickness evaluation with the radial basis functions

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1081-1089
Author(s):  
Daigo Kosaka ◽  
Kazuhiko Kakishita ◽  
Takaaki Nara
Keyword(s):  
Radial Basis Functions ◽  
Eddy Current ◽  
Test Sample ◽  
Basis Functions ◽  
Evaluation Result ◽  
Current Testing ◽  
Wide Range ◽  
Radial Basis ◽  
Thickness Evaluation ◽  
Plating Layer

The purpose of this work is to evaluate the thickness of the plating layer of vibrating samples using Eddy Current Testing (ECT). In almost previous research, the thickness is evaluated with the fixed liftoff, because the liftoff has an impact on detection signals of the ECT. In this paper, the plating thickness is evaluated in an environment in which the liftoff is greatly changed. A plating thickness evaluation with the Radial Basis Functions was proposed. The thickness of the test sample was evaluated while applying a vibration. It was confirmed that the evaluation result of the method, which uses detection signals in a wide range of liftoffs as calibration signals, was less affected by the liftoff change than the evaluation result of a method which uses phase of detection signals.

Biharmonic navigation using radial basis functions

Robotica ◽  
2021 ◽  
pp. 1-12
Author(s):  
Xu-Qian Fan ◽  
Wenyong Gong
Keyword(s):  
Finite Difference ◽  
Boundary Conditions ◽  
Radial Basis Functions ◽  
Real World ◽  
Biharmonic Equation ◽  
Finite Difference Methods ◽  
Basis Functions ◽  
Large Size ◽  
Radial Basis ◽  
Difference Methods

Abstract Path planning has been widely investigated by many researchers and engineers for its extensive applications in the real world. In this paper, a biharmonic radial basis potential function (BRBPF) representation is proposed to construct navigation fields in 2D maps with obstacles, and it therefore can guide and design a path joining given start and goal positions with obstacle avoidance. We construct BRBPF by solving a biharmonic equation associated with distance-related boundary conditions using radial basis functions (RBFs). In this way, invalid gradients calculated by finite difference methods in large size grids can be preventable. Furthermore, paths constructed by BRBPF are smoother than paths constructed by harmonic potential functions and other methods, and plenty of experimental results demonstrate that the proposed method is valid and effective.

scholarly journals Efficient mesh deformation using radial basis functions with a grouping-circular-based greedy algorithm

2021 ◽  
Vol 433 ◽  
pp. 110200
Author(s):  
Hong Fang ◽  
He Zhang ◽  
Fanli Shan ◽  
Ming Tie ◽  
Xing Zhang ◽  
...  
Keyword(s):  
Greedy Algorithm ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Mesh Deformation ◽  
Radial Basis

EYE DETECTION USING OPTIMAL WAVELET PACKETS AND RADIAL BASIS FUNCTIONS (RBFs)

1999 ◽  
Vol 13 (07) ◽  
pp. 1009-1025 ◽  
Author(s):  
JEFFREY HUANG ◽  
HARRY WECHSLER
Keyword(s):  
Radial Basis Functions ◽  
Detection Task ◽  
Basis Functions ◽  
Wavelet Packets ◽  
Eye Detection ◽  
Robust Performance ◽  
Image Normalization ◽  
Entropy Minimization ◽  
Novel Approach ◽  
Radial Basis

The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to the anchoring on model-based schemes. This paper introduces a novel approach for the eye detection task using optimal wavelet packets for eye representation and Radial Basis Functions (RBFs) for subsequent classification ("labeling") of facial areas as eye versus non-eye regions. Entropy minimization is the driving force behind the derivation of optimal wavelet packets. It decreases the degree of data dispersion and it thus facilitates clustering ("prototyping") and capturing the most significant characteristics of the underlying (eye regions) data. Entropy minimization is thus functionally compatible with the first operational stage of the RBF classifier, that of clustering, and this explains the improved RBF performance on eye detection. Our experiments on the eye detection task prove the merit of this approach as they show that eye images compressed using optimal wavelet packets lead to improved and robust performance of the RBF classifier compared to the case where original raw images are used by the RBF classifier.

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