Prediction model for maximum shear displacement of pipe joints with preexisting defects based on finite element–multiple nonlinear regression method

2021 ◽  
Author(s):  
Bin Li ◽  
Hongyuan Fang ◽  
Xijun Zhang ◽  
Kangjian Yang ◽  
Boyuan Dong ◽  
...  
Keyword(s):  
Finite Element ◽  
Prediction Model ◽  
Nonlinear Regression ◽  
Regression Method ◽  
Shear Displacement ◽  
Pipe Joints ◽  
Nonlinear Regression Method

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 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

Note. Kinetic parameters of Bacillus stearothermophilus spores under isothermal and non-isothermal heating conditions Nota. Parámetros cinéticos del tratamiento isotérmico y no isotérmico de esporas de Bacillus stearothermophilus

1998 ◽  
Vol 4 (6) ◽  
pp. 443-447 ◽  
Author(s):  
P.M. Periago ◽  
S. Leontidis ◽  
P.S. Fernández ◽  
C. Rodrigo ◽  
A. Martínez
Keyword(s):  
Kinetic Parameters ◽  
Nonlinear Regression ◽  
Bacillus Stearothermophilus ◽  
Confidence Regions ◽  
Regression Method ◽  
Isothermal Heating ◽  
One Step ◽  
Nonlinear Regression Method ◽  
Thermal Kinetic Parameters ◽  
The One

Thermobacteriological studies using Bacillus stearotherrnophilus spores were carried out by heating the spores under isothermal and non-isothermal conditions followed by an isothermal period. Ex perimental data obtained after isothermal heating were analyzed using a two-step linear regression procedure and a one-step nonlinear regression method. Results obtained using both analytical tech niques were close, but the 90% interval of confidence for predictions was lower when the one-step nonlinear regression was used. These results indicated the convenience of using the one-step nonlin ear regression method to obtain thermal kinetic parameters for bacterial spores. Also, the parameters obtained by a non-isothermal program followed by an isothermal heating period were similar to those obtained after isothermal heating. Nevertheless, the joint confidence regions at 90% did not overlap, indicating that there are significant differences. The z values obtained under isothermal (7.0 °C) and non-isothermal (8.5 °C) conditions are close and characteristic for this microorganisms.

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