DEEP WAVE HEIGHT PREDICTION FOR ALEXANDRIA SEA REGION BY USING NONLINEAR REGRESSION METHOD COMPARED TO SUPPORT VECTOR MACHINE

2018 ◽  
Vol 10 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Tamer Elgohary ◽  
Moussa S. Elbisy ◽  
Amir M. Mobasher ◽  
Hassan Salah
Keyword(s):  
Support Vector Machine ◽  
Wave Height ◽  
Nonlinear Regression ◽  
Regression Method ◽  
Support Vector ◽  
Height Prediction ◽  
Nonlinear Regression Method

scholarly journals Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

2021 ◽  
Vol 15 (4) ◽  
pp. 68-74
Author(s):  
Alireza Afradi ◽  
Arash Ebrahimabadi ◽  
Tahereh Hallajian
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Gene Expression Programming ◽  
Regression Method ◽  
Support Vector ◽  
Tunnel Boring ◽  
Mechanized Tunneling ◽  
Disc Cutters

Purpose. Disc cutters are the main cutting tools for the Tunnel Boring Machines (TBMs). Prediction of the number of consumed disc cutters of TBMs is one of the most significant factors in the tunneling projects. Choosing the right model for predicting the number of consumed disc cutters in mechanized tunneling projects has been the most important mechanized tunneling topics in recent years. Methods. In this research, the prediction of the number of consumed disc cutters considering machine and ground conditions such as Power (KW), Revolutions per minute (RPM) (Cycle/Min), Thrust per Cutter (KN), Geological Strength Index (GSI) in the Sabzkooh water conveyance tunnel has been conducted by multiple linear regression analysis and multiple nonlinear regression, Gene Expression Programming (GEP) method and Support Vector Machine (SVM) approaches. Findings. Results showed that the number of consumed disc cutters for linear regression method is R2 = 0.95 and RMSE = 0.83, nonlinear regression method is – R2 = 0.95 and RMSE = 0.84, Gene Expression Programming (GEP) method is – R2 = 0.94 and RMSE = 0.95, Support Vector Machine (SVM) method is – R2 = 0.98 and RMSE = 0.45. Originality. During the analyses, in order to evaluate the accuracy and efficiency of predictive models, the coefficient of determination (R2) and root mean square error (RMSE) have been used. Practical implications. Results demonstrated that all four methods are effective and have high accuracy but the method of support vector machine has a special superiority over other methods.

scholarly journals Application of neural networks and support vector machine for significant wave height prediction

Oceanologia ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 331-349 ◽  
Author(s):  
Jadran Berbić ◽  
Eva Ocvirk ◽  
Dalibor Carević ◽  
Goran Lončar
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Support Vector ◽  
Significant Wave ◽  
Height Prediction

Dynamic Behavior of Cellular Materials under Combined Shear-Compression

2016 ◽  
Vol 835 ◽  
pp. 649-653
Author(s):  
Yuan Yuan Ding ◽  
Shi Long Wang ◽  
Zhi Jun Zheng ◽  
Li Ming Yang ◽  
Ji Lin Yu
Keyword(s):  
Finite Element ◽  
Shear Stress ◽  
Finite Element Model ◽  
Nonlinear Regression ◽  
Element Model ◽  
Cellular Materials ◽  
Regression Method ◽  
Plateau Stress ◽  
Nonlinear Regression Method ◽  
Biaxial Behavior

A 3D cell-based finite element model is employed to investigate the dynamic biaxial behavior of cellular materials under combined shear-compression. The biaxial behavior is characterized by the normal stress and shear stress, which could be determined directly from the finite element results. A crush plateau stress is introduced to illustrate the critical crush stress, and the result shows that the normal plateau stress declines with the increase of the shear plateau stress, which climbs with the increase of loading angle. An elliptical criterion of normal plateau stress vs. shear plateau stress is obtained by the nonlinear regression method.

Multi-temporal Nonlinear Regression Method for Landsat Image Simulation

2018 ◽  
Vol 23 (2) ◽  
pp. 777-787 ◽  
Author(s):  
Hye Jin Kim ◽  
Dae Kyo Seo ◽  
Yang Dam Eo ◽  
Min Cheol Jeon ◽  
Wan Yong Park
Keyword(s):  
Nonlinear Regression ◽  
Regression Method ◽  
Landsat Image ◽  
Image Simulation ◽  
Multi Temporal ◽  
Nonlinear Regression Method

scholarly journals Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

2015 ◽  
Vol 22 (3) ◽  
pp. 341-350 ◽  
Author(s):  
Łukasz Lentka ◽  
Janusz M. Smulko ◽  
Radu Ionescu ◽  
Claes G. Granqvist ◽  
Laszlo B. Kish
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Temperature Change ◽  
Nonlinear Regression ◽  
Ultraviolet Irradiation ◽  
Support Vector ◽  
Gas Mixture ◽  
Gas Concentration ◽  
Single Sensor

Abstract This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

scholarly journals A simplified nonlinear regression method for human height estimation in video surveillance

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Shengzhe Li ◽  
Van Huan Nguyen ◽  
Mingjie Ma ◽  
Cheng-Bin Jin ◽  
Trung Dung Do ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Nonlinear Regression ◽  
Regression Method ◽  
Height Estimation ◽  
Human Height ◽  
Nonlinear Regression Method

scholarly journals Fast nonlinear regression method for CT brain perfusion analysis

2016 ◽  
Vol 3 (2) ◽  
pp. 026003 ◽  
Author(s):  
Edwin Bennink ◽  
Jaap Oosterbroek ◽  
Kohsuke Kudo ◽  
Max A. Viergever ◽  
Birgitta K. Velthuis ◽  
...  
Keyword(s):  
Nonlinear Regression ◽  
Brain Perfusion ◽  
Regression Method ◽  
Nonlinear Regression Method ◽  
Ct Brain
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