Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen
Keyword(s):  
Support Vector Machine ◽  
Shock Absorber ◽  
Recognition Rate ◽  
Support Vector ◽  
Indicator Diagram ◽  
Fault Recognition ◽  
Average Recognition Rate ◽  
Rbf Kernel ◽  
On Line ◽  
Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

A Data-Driven Method of Engine Sensor on Line Fault Diagnosis and Recovery

2014 ◽  
Vol 490-491 ◽  
pp. 1657-1660 ◽  
Author(s):  
Tie Bin Zhu ◽  
Feng Lu
Keyword(s):  
Fault Diagnosis ◽  
Recognition Rate ◽  
Aircraft Engine ◽  
Primary Data ◽  
Support Vector ◽  
Data Series ◽  
Sensor Failure ◽  
Sensor Fault ◽  
Fault Recognition ◽  
On Line

Considering the requirements of convinced sensor measurements for engine control, a method of aircraft engine sensor on line fault diagnosis and recovery based on least squares support vector machine (LS-SVM) is proposed. First, sensor sets correlations are calculated and the sensor with high correlation is selected by correlation analysis. Then sensor LS-SVM prediction model is established with the sensor itself primary data series and used to sensor fault diagnosis. The sensor recovery module is obtained based on the LSSVM algorithm with the high correlated sensor set, and is activated as the sensor failure detected. Experimental results show that the engine sensor fault recognition rate is satisfied by the proposed method, and could be used to turbofan engine sensor fault diagnosis and data recovery.

scholarly journals Font Recognition of English Letters Based on Distance Profile Features

2020 ◽  
Vol 8 (2) ◽  
pp. 66-71
Author(s):  
Aveen J. Mohammed ◽  
Hasan S.M. Al-Khaffaf
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Final Phase ◽  
Rbf Kernel ◽  
Font Recognition

This paper presents a system for recognizing English fonts from character images. The distance profile is the feature of choice used in this paper. The system extracts a vector of 106 features and feeds it into a support vector machine (SVM) classifier with a radial basis function (RBF) kernel. The experiment is divided into three phases. In the first phase, the system trains the SVM with different Gamma and C parameters. In the second phase, the validation phase, we validate and select the pair of Gamma and C values that yield the best recognition rates. In the final phase, the testing phase, the images are tested and the recognition rate is reported. Experimental results based on 27,620 characters glyph images from three English fonts show a 94.82% overall recognition rate.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

Sign in / Sign up

Export Citation Format

Share Document