scholarly journals Extraction Methods for Pipeline Weld Defect Features

2021 ◽  
Vol 2033 (1) ◽  
pp. 012209
Author(s):  
DongLiang Yu ◽  
Heng Xuan ◽  
AiLing Wang ◽  
Ge Chen ◽  
WenQing Chen ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Weld Defect

COMPARISON OF SELECTED WELD DEFECT EXTRACTION METHODS

2008 ◽  
Author(s):  
R. Sikora ◽  
P. Baniukiewicz ◽  
T. Chady ◽  
W. Ruciński ◽  
K. Świadek ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Weld Defect ◽  
Defect Extraction

X-Ray Testing of Weld Defect of Automatic Recognit Ion and Alarm Technology Research

2014 ◽  
Vol 472 ◽  
pp. 495-502 ◽  
Author(s):  
Peng Lin Zhang ◽  
Zhi Qiang Zhao ◽  
Yan Ping Wang
Keyword(s):  
Extraction Procedure ◽  
Dynamic Test ◽  
Extraction Methods ◽  
Technology Research ◽  
Gray Scale ◽  
X Ray ◽  
Signal Characteristics ◽  
Starting Point ◽  
Weld Defect ◽  
Defect Signal

This essay based on X-ray digital automatic detection as a starting point, and then to deal the typical X-ray digital image with median filtering and histogram threshold processing, with the help of a column gray method to extract respective column of each weld image gray scale curve, this essay tries to analyze the defect signal characteristics and discuss the extraction methods. In this essay, the author compiles the image preprocessing procedure, columns, gray-scale extraction procedure and defect alarm procedures; and also tries the X-ray weld defect detection of digital rapid recognition and prompt functions. Thorough the analysis we can draw the conclusion that column gray method can be used as a dynamic test basis of automatic alarm decision.

scholarly journals Radial Basis Function Neural Network sebagai Pengklasifikasi Citra Cacat Pengelasan

Rekayasa ◽  
2018 ◽  
Vol 11 (2) ◽  
pp. 118
Author(s):  
Noorman Rinanto ◽  
Mohammad Thoriq Wahyudi ◽  
Agus Khumaidi
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Radial Basis Function ◽  
Basis Function ◽  
Extraction Methods ◽  
Weld Defect ◽  
Welding Defects ◽  
Radial Basis ◽  
Welding Image ◽  
Hidden Layer

<p>Tingginya resiko kesalahan manusia dalam inspeksi visual untuk cacat pengelasan yang masih mengandalkan kemampuan manusia sulit untuk dihindari. Oleh sebab itu, penelitian ini mengusulkan sebuah klasifikasi cacat las visual dengan menggunakan algoritma <em>Radial Basis Function Neural Network</em> (RBFNN). Masukan RBFNN berupa citra las yang terdiri dari 5 (lima) kelas cacat las visual dan 1 (satu) kelas citra las normal. Citra las tersebut diproses terlebih dahulu menggunakan metode ekstraksi fitur <em>Fast Fourier Transform</em> (FFT) dan <em>Descreate Cosine Transform</em> (DCT). Hasil kedua metode ekstraksi fitur tersebut kemudian akan saling dibandingkan untuk mengetahui kinerja RBFNN. Hasil pengujian menunjukkan bahwa sistem dengan metode FFT-RBFNN dapat menggolongkan citra cacat las dengan akurasi sebesar 91.67% dan DCT-RBFNN sekitar 83.33% dengan jumlah neuron hidden layer sebanyak 15 dan parameter spread adalah 4.<em></em></p><p>Kata Kunci: <em>Radial Basis Function Neural Network</em> (RBFNN), FFT, DCT, cacat las, klasifikasi.</p><p align="center">Radial Basis Function Neural Network as a Weld Defect Classifiers<strong></strong></p><p><strong> </strong></p><p><strong>ABSTRACT</strong></p><p><em>The high risk of human error in visual inspection of welding defects that still rely on human capabilities is difficult to avoid. Therefore, this study proposes a classification of visual welding defects using the Radial Base Function Neural Network (RBFNN) algorithm. The RBFNN input is in the form of a welding image consisting of 5 (five) visual welding defect classes and 1 (one) normal welding image class. The weld image is processed first using the Fast Fourier Transform (FFT) and Descreate Cosine Transform (DCT) feature extraction methods. The results of these two feature extraction methods will be compared to find out the RBFNN performance. The test results show that the system with FFT-RBFNN method can classify the image of weld defects with an accuracy of 91.67% and DCT-RBFNN around 83.33% with the number of hidden layer neurons as much as 15 and the parameters of spread are 4.</em></p><p><em>Keywords: Radial Basis Function Neural Network (RBFNN), FFT, DCT, weld defect, classification.</em></p>

The effect of different extraction methods on chemical composition profile and pharmacological activities of essential oils and hydrosols

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
F Ghavidel ◽  
MM Zarshenas ◽  
A Sakhteman ◽  
A Gholami ◽  
Y Ghasemi ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Essential Oils ◽  
Extraction Methods ◽  
Composition Profile ◽  
Pharmacological Activities

Characterization of dissolved organic matter (DOM) extracted from soils by hot water percolation (HWP)

2010 ◽  
Vol 59 (1) ◽  
pp. 99-108 ◽  
Author(s):  
M. Takács ◽  
Gy. Füleky
Keyword(s):  
Organic Matter ◽  
Humic Acid ◽  
Natural Organic Matter ◽  
Extraction Methods ◽  
Soil Samples ◽  
Hot Water ◽  
Soil Extracts ◽  
Water Percolation ◽  
Soil Humic Acid

The Hot Water Percolation (HWP) technique for preparing soil extracts has several advantages: it is easily carried out, fast, and several parameters can be measured from the same solution. The object of this study was to examine the possible use of HWP extracts for the characterization of soil organic matter. The HPLC-SEC chromatograms, UV-VIS and fluorescence properties of the HWP extracts were studied and the results were compared with those of the International Humic Substances Society (IHSS) Soil Humic Acid (HA), IHSS Soil Fulvic Acid (FA) and IHSS Suwannee Natural Organic Matter (NOM) standards as well as their HA counterparts isolated by traditional extraction methods from the original soil samples. The DOM of the HWP solution is probably a mixture of organic materials, which have some characteristics similar to the Soil FA fractions and NOM. The HWP extracted organic material can be studied and characterized using simple techniques, like UV-VIS and fluorescence spectroscopy.

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

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