A robust shot transition detection method based on support vector machine in compressed domain

2007 ◽  
Vol 28 (12) ◽  
pp. 1534-1540 ◽  
Author(s):  
Jianrong Cao ◽  
Anni Cai
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Support Vector ◽  
Compressed Domain

scholarly journals Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine

2021 ◽  
Vol 2076 (1) ◽  
pp. 012004
Author(s):  
Jianfeng Gao ◽  
Yu Zheng ◽  
Kai Ni ◽  
Huaizhi Zhang ◽  
Bin Hao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Working Conditions ◽  
Detection Method ◽  
Leak Detection ◽  
Gas Pipeline ◽  
Support Vector ◽  
Leakage Detection ◽  
Swarm Optimization ◽  
Svm Algorithm ◽  
Oil Gas

Abstract In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a pipeline leak detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This method uses PSO to solve the penalty factor ‘c’ and kernel function parameter ‘g’, and constructs the pipeline leakage detection model of SVM. We set up an experimental platform to collect negative pressure wave signals under different working conditions. After wavelet domain denoising and data preprocessing, four eigenvalues of Mean, Standard Deviation, Kurtosis and Skewness are extracted from the signals to form the eigenvector samples, which are taken as input of SVM, and four working conditions of normal, leakage, rise and fall are taken as output. Through experimental verification, the comprehensive performance of PSO-SVM algorithm is better than that of traditional SVM, Genetic Algorithm optimized SVM and grid search algorithm optimized SVM. The POS-SVM algorithm can be applied to the leak detection of oil-gas pipeline.

Eggshell Crack Detection and Egg Classification Using Resonance and Support Vector Machine Methods

2019 ◽  
Vol 35 (1) ◽  
pp. 23-30
Author(s):  
Ching-Wei Cheng ◽  
Pei-Hsuan Feng ◽  
Jun-Hong Xie ◽  
Yu-Kai Weng
Keyword(s):  
Support Vector Machine ◽  
Characteristic Frequency ◽  
Crack Detection ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Identification Accuracy ◽  
Support Vector ◽  
Training Set ◽  
Processed Products ◽  
Resonance Detection

Abstract. Cracks in eggshells not only affect the egg preservation time but also reduce the success rate for the end-processed products. This study was based on the theory of resonant inspection (RI). The use of the support vector machine (SVM) method as a means of more accurate eggshell crack detection was evaluated. The results revealed that comparing the resonant frequency and amplitude by using a microphone as a sensor allowed non-cracked eggs to be distinguished from cracked eggs. The characteristic frequency of a non-cracked egg was between 4130 and 5500 Hz, and its amplitude was between 0.16 and 0.20 V. The spectrum of a cracked egg was fuzzy, with no obvious characteristic frequency, and the maximum amplitude was approximately 0.06 V. The identification accuracy was 99% and 98% for the SVM training set and testing set, respectively. These results prove that the resonance detection method is effective for identifying eggs with cracked shells. Keywords: Eggshells, Resonant inspection, Fast Fourier transform, Support vector machine.

Fault Sign Detection Method for Rotators based on One-class Support Vector Machine with Successive Multi-level Class Construction

2017 ◽  
Vol 137 (12) ◽  
pp. 858-865
Author(s):  
Koji Nakao ◽  
Tatsushi Toyama ◽  
Takanori Hayashi ◽  
Takamasa Hori ◽  
Tomoki Hamagami
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Support Vector ◽  
Sign Detection ◽  
Multi Level ◽  
Class Construction
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