Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

2010 ◽  
Vol 22 (10) ◽  
pp. 2441-2447
Author(s):  
金晶 Jin Jing ◽  
魏彪 Wei Biao ◽  
冯鹏 Feng Peng ◽  
唐跃林 Tang Yuelin ◽  
周密 Zhou Mi
Keyword(s):  
Support Vector Machine ◽  
Feature Analysis ◽  
Support Vector ◽  
Time Frequency ◽  
Fission Neutrons ◽  
Frequency Feature

scholarly journals Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Entropy ◽  
2015 ◽  
Vol 18 (1) ◽  
pp. 7 ◽  
Author(s):  
Nantian Huang ◽  
Huaijin Chen ◽  
Shuxin Zhang ◽  
Guowei Cai ◽  
Weiguo Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
High Voltage ◽  
Support Vector ◽  
Circuit Breakers ◽  
Time Frequency

Engine Knock Detection Based on Time-Frequency Images and Support Vector Machine

2013 ◽  
Vol 694-697 ◽  
pp. 1387-1390
Author(s):  
Ning Li ◽  
Rui Zhou
Keyword(s):  
Support Vector Machine ◽  
Economic Performance ◽  
Detection Task ◽  
Support Vector ◽  
Image Generation ◽  
Time Frequency ◽  
Engine Cylinder ◽  
Engine Knock ◽  
Knock Detection ◽  
Knock Control

Knock is a major cause of pollution, parts damage and metallic noise in an engine. But slight knock can improve the power and economic performance of an engine. Therefore, existence and intensity of the knock are preconditions for the automatic knock control system to work. This paper describes an advanced approach solving the knock detection task. It is based on a time-frequency image generation solution followed by a support vector machine detection step trained in a constructive supervised way. The proposed method is applied to analyze the pressure signals measured from an engine cylinder to detect the knock based on the popular cycle-by-cycle classification. It is shown that this approach can qualify well for knock detection.

scholarly journals Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Liu ◽  
Wenxiang Gu ◽  
Wenyi Zhang ◽  
Jianan Wang
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Prediction Accuracy ◽  
Support Vector Machine Classifier ◽  
Biological Function ◽  
Reducing Sugar ◽  
Computational Method ◽  
Protein Glycation ◽  
Feature Analysis ◽  
Support Vector

Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules. The identification of glycation sites in protein may provide guidelines to understand the biological function of protein glycation. In this study, we developed a computational method to predict protein glycation sites by using the support vector machine classifier. The experimental results showed that the prediction accuracy was 85.51% and an overall MCC was 0.70. Feature analysis indicated that the composition ofk-spaced amino acid pairs feature contributed the most for glycation sites prediction.

scholarly journals Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

2020 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Mohd Hatta Jopri ◽  
Abdul Rahim Abdullah ◽  
Jingwei Too ◽  
Tole Sutikno ◽  
Srete Nikolovski ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Current Source ◽  
Naïve Bayes ◽  
Support Vector ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Voltage ◽  
Harmonic Source

<span>A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.</span>

Sign in / Sign up

Export Citation Format

Share Document