A Model for Predicting the Wear Degree of Electrode Tip

2014 ◽  
Vol 574 ◽  
pp. 292-297
Author(s):  
Jin Zhang ◽  
Peng Xian Zhang ◽  
Xiang Jian Xu
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Digital Image ◽  
Model Prediction ◽  
Support Vector ◽  
Laser Measurement ◽  
Characteristic Parameters ◽  
Laser Measurement System ◽  
Image Characteristic ◽  
Test Result

A new method is put forward to predicting the degree of electrode tip wear based on a laser measurement and digital image of the surface joint indentation. First, in order to monitoring the degree of electrode tip wear, the decline altitudes of sphere ΔH that can indicate variation of electrode tip shape are measured by means of the laser measurement system. Second, through the correlation analysis between the parameters S0, S1, S, K1 reflecting digital image characteristic of joint indentation and the decline altitudes of sphere ΔH, S0, S, K1 are extracted as characteristic parameters of monitoring electrode tip wear. At last, a model of support vector machine (SVM) for predicting the degree of electrode tip wear is established between the parameters S0, S, K1 as the input vector and ΔH as the target vector. Test result shows, the correlation coefficient between model prediction and actual measured values are 0.9907. The prediction model can realize estimating the degree of electrode tip wear.

A Spectrum Sensing Based on Support Vector Machine Algorithm in the Building Indoors Environment

2014 ◽  
Vol 945-949 ◽  
pp. 2297-2300 ◽  
Author(s):  
Xing Hua Xia ◽  
Fang Jun Luan ◽  
Meng Xin Li
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Spectrum Sensing ◽  
Indoor Environment ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Spectral Correlation ◽  
Low Snr

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm in building indoor environment.

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Machine Tool ◽  
Least Squares ◽  
Thermal Error ◽  
Support Vector ◽  
Error Prediction ◽  
Nc Machine Tool ◽  
Nc Machine

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.

Weld Defect Classification in Radiographic Film Using Statistical Texture and Support Vector Machine

2014 ◽  
Vol 896 ◽  
pp. 695-700
Author(s):  
Muhtadan ◽  
Risanuri Hidayat ◽  
Widyawan ◽  
Fahmi Amhar
Keyword(s):  
Support Vector Machine ◽  
Digital Image ◽  
Median Filter ◽  
Image Processing Technique ◽  
Support Vector ◽  
Radiographic Film ◽  
Machine Method ◽  
Defect Type ◽  
Weld Defects ◽  
Weld Defect

Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characteristic of a radiographic film digital image. Multiclass Support Vector Machine (SVM) algorithm was used to perform classification of weld defects type. The result of classification testing shows that the proposed method can classify 83.3% correctly from 60 testing data of weld defects radiographic films.

Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity

2021 ◽  
Vol 5 (3) ◽  
pp. 520-526
Author(s):  
Irbah salsabila ◽  
Yuliant Sibaroni
Keyword(s):  
Support Vector Machine ◽  
Semantic Similarity ◽  
Support Vector ◽  
Product Reviews ◽  
Important Requirement ◽  
Test Result ◽  
Beauty Products

Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

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