A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

This study uses multivariate adaptive regression spline (MARS) for determination of maximum shear modulus (Gmax) and minimum damping ratio (ξmin) of synthetic reinforced soil. MARS employs confining pressure (σ, psi), rubber (r, %) and sand (s, %) as input variables. The outputs of the MARS are Gmax and ξmin. The developed MARS gives equations for determination of Gmax and ξmin. The results of MARS have been compared with the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perception (MLP) and multiple regression analysis method (MRM). A sensitivity analysis has been also carried out to determine the effect of each input variable on Gmax and ξmin. This study shows that the developed MARS is a robust model for prediction of Gmax and ξmin

Surrogate-based optimization with improved support vector regression for non-circular vent hole on aero-engine turbine disk

Abstract(#br)This paper investigates a structural optimization for the non-circular vent hole on an aero-engine turbine disk. A novel optimization approach, namely SO-ISVR (the surrogate-based optimization using an improved support vector regression), for the vent hole design is developed. ISVR (the improved support vector regression) is proposed to extract some ignored valuable information from the existing training data by using the least squares method, so as to improve the performance of SVR (support vector regression). To validate the advantages of SO-ISVR, another two optimization approaches are also developed. They are SO-SVR (the surrogate-based optimization using SVR) and FEMO (the finite element method based optimization). The results show that ISVR is suitable and valuable in engineering optimization. Compared to the initial scheme, the maximum von Mises stress of the optimal scheme obtained by SO-ISVR is reduced from 1189.488 MPa to 948.530 MPa. Further comparative study for SO-ISVR, SO-SVR, and FEMO demonstrates that SO-ISVR possesses some advantages in both computational efficiency and optimization effect

Using Multiple Instance Learning techniques to rank maize ears according to their traits

AbstractMultiple-Instance Learning (MIL) is a sub-field of machine learning. Its main goal is to do accurate predictions on new data based on a predictive model generated from previously group of labeled bags of data, known as training data, containing many instances. MIL has many real world important applications such as image retrieval or text categorization and medical diagnosis problems.It is often difficult for crop breeders to predict yield by combining different yield components to produce better plants with superior performance. Data analysis is one area that is striving to let farmers have an idea of their expected yield pre-harvest. Accurate early yield prediction will improve agricultural strategies plan, proper resources allocation and improve management of maize ear cultivation with consequent increase in productivity. Most experiments on maize ears traits only considered ear evaluation and maize improvement without yield prediction. One of the experiments that included yield prediction was PR. NDCG measure which was developed to rank maize evaluation for Sousa Valley Best Ear Competition. The focus of this work was to make an intelligent regression models recognition and analysis by running some MIL algorithms to predict and assign real value to maize yield from randomly group N vary parameter sizes of maize ear traits and soil parameters of maize population dataset. Furthermore, this dissertation also ranked the models per result and establish a relationship between variables

Investigation of Emission Characteristics during Low Temperature Combustion using Multivariate Adaptive Regression Splines

Exhaust emissions from diesel engines operating in a low temperature combustion (LTC) regime are significantly affected by fuel composition and injection strategy. The starting point of this study is a collection of data correlating injection system parameters, and fuel characteristics, to response parameters such as engine-out emissions (oxides of nitrogen (NOx), total particulate matter (TPM), carbon monoxide (CO), hydrocarbons (HC)) and brake thermal efficiency (BTE).;The purpose of this work is to develop a statistical analysis tool to assist the emission analyst in modeling problems in which a response of interest is influenced by several variables and the objective is to optimize this response. The experimental data produced during LTC operation have been analyzed using an approach commonly known as Response Surface Methodology (RSM). Since the system under study may be responding to hidden inputs that are neither measured nor controlled, regression analysis must be performed via a flexible procedure. The methodology that will be used in this sense is called Multivariate Adaptive Regression Splines (MARS), which allows to approximate functions of many input variables given the value of the function at a collection of point in the input space.;Data was collected at West Virginia University\u27s Engine and Emissions Research Laboratory for the project CRC AVFL-16. The test engine was a turbo-charged GM 1.9L operated in the LTC mode utilizing a split injection strategy. Main and pilot SOI timing and fuel split were varied per a 5 X 3 X 3 full factorial design. Advanced Vehicle Fuel Lubricants (AVFL) Committee of the Coordinating Research Council (CRC) defined a matrix of nine test Fuels for Advanced Combustion Engines (FACE) based on the variation of three properties: cetane number, aromatic content, and 90 percent distillation temperature. The experimental data was used has a platform for the code development, and for its validation.;Using multivariate data analysis is not only useful in visualizing correlations that otherwise would be hidden by the large amount of experimental data points, but it is also capable to predict the behavior of those points inside the domain where no data are available. As suggested by the name this is a regression methodology capable of adapting the shape of the regression splines to the data analyzed. Validation datasets which were independent of the `calibration\u27 datasets were used to check the accuracy of the model predictions