SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso

2020 ◽  
Vol 486 ◽  
pp. 110098 ◽  
Author(s):  
Yanhao Huo ◽  
Lihui Xin ◽  
Chuanze Kang ◽  
Minghui Wang ◽  
Qin Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Group Lasso ◽  
Tumor Classification ◽  
Support Vector ◽  
Sparse Group Lasso ◽  
Novel Method

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 26 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Hanguang Xiao
Keyword(s):  
Support Vector Machine ◽  
Input Impedance ◽  
Recursive Algorithm ◽  
Artery Stenosis ◽  
Support Vector ◽  
Arterial Tree ◽  
Novel Method ◽  
The Mean ◽  
Fold Cross Validation ◽  
Systemic Arterial Tree

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.

A Novel Method for Enhancing Bearing Fault Feature Based on Sparse Group Lasso

2021 ◽  
Author(s):  
Changkun Han ◽  
Wei Lu ◽  
Pengxin Wang ◽  
Liuyang Song ◽  
Huaqing Wang
Keyword(s):  
Group Lasso ◽  
Bearing Fault ◽  
Sparse Group Lasso ◽  
Feature Based ◽  
Novel Method

scholarly journals Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5152
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
David Infield ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Anomaly Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Support Vector ◽  
Specific Training ◽  
Average Accuracy ◽  
Operations And Maintenance ◽  
Novel Method ◽  
Isolation Forest

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

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