A multivariate multiple regression analysis of tire-road contact peak triaxial stress by using machine learning methods

2021 ◽  
pp. 1-16
Author(s):  
Xiangwen Li ◽  
Minrui Guo ◽  
Xinglin Zhou
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Triaxial Stress ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multivariate Multiple Regression

scholarly journals Influence of Interest In Learning and How to Learn on Understanding Concepts: Work and Energy Cases

2021 ◽  
Vol 9 (2) ◽  
pp. 305-316
Author(s):  
Alvi Hasanati* ◽  
Endang Purwaningsih
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Student Learning ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Student Interest ◽  
Research Subjects ◽  
Learning Methods ◽  
Concept Understanding ◽  
Energy And Momentum

Student who do not have an interest in lessons will find it difficult to achieve optimal success, and the way students learn also affects student learning outcomes. The research aims to determine the effect of interest in learning and how students learn in understanding student concepts. The type of research used is survey research. The  research subjects were 60 students of class X Ar-Rohmah Islamic Boarding School Dau Malang. The research instrument was adapted and developed from previous research.The concept understanding instrument was adapted from the Energy Concept Assessment and Energy and Momentum Conceptual survey questions. Data processing using multiple regression analysis. The data analysis technique used multiple regression analysis. The data analysis results show that the value of Fcount = 46,946 Ftable = 4.001, these results indicate that there is an influence on students' interest in learning with concept understanding. Obtained the results of testing how to learn and understanding the concept of Fcount = 55.364 Ftable = 4.001, it can be concluded that there is an influence on student learning and conceptual understanding. Furthermore, the results of testing the effect of student interest in learning and learning methods on concept understanding were obtained Fcount = 40.153 Ftable = 3.150, so that there was an effect of interest in learning and learning methods on understanding the concept of business and energy theory

scholarly journals Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

2020 ◽  
Vol 12 (8) ◽  
pp. 3269
Author(s):  
Shinyoung Kwag ◽  
Daegi Hahm ◽  
Minkyu Kim ◽  
Seunghyun Eem
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Seismic Performance ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Assessment Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

scholarly journals Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yong-Hyuk Kim ◽  
Ji-Hun Ha ◽  
Yourim Yoon ◽  
Na-Young Kim ◽  
Hyo-Hyuc Im ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Atmospheric Pressure ◽  
Absolute Error ◽  
Time Of Day ◽  
Support Vector ◽  
Pressure Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Em Clustering

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user’s mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.

scholarly journals Time Series Analysis of Production Decline in Carbonate Reservoirs with Machine Learning

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liqiang Wang ◽  
Mingji Shao ◽  
Gen Kou ◽  
Maoxian Wang ◽  
Ruichao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Multiple Regression ◽  
Carbonate Reservoirs ◽  
Good Prediction ◽  
Analysis Tool ◽  
Learning Methods ◽  
Yield Decline ◽  
Machine Learning Methods ◽  
Good Prediction Accuracy

Classical decline methods, such as Arps yield decline curve analysis, have advantages of simple principles and convenient applications, and they are widely used for yield decline analysis. However, for carbonate reservoirs with high initial production, rapid decline, and large production fluctuations, with most wells having no stable production period, the adaptability of traditional decline methods is inadequate. Hence, there is an urgent need to develop a new decline analysis method. Although machine learning methods based on multiple regression and deep learning have been applied to unconventional oil reservoirs in recent years, their application effects have been unsatisfactory. For example, prediction errors based on multiple regression machine learning methods are relatively large, and deep learning sample requirements and the actual conditions of reservoir management do not match. In this study, a new equal probability gene expression programming (EP-GEP) method was developed to overcome the shortcomings of the conventional Arps decline model in the production decline analysis of carbonate reservoirs. Through model validation and comparative analysis of prediction effects, it was proven that the EP-GEP model exhibited good prediction accuracy, and the average relative error was significantly smaller than those of the traditional Arps model and existing machine learning methods. The successful application of the proposed method in the production decline analysis of carbonate reservoirs is expected to provide a new decline analysis tool for field reservoir engineers.

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