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Item Complex Vehicle Modeling: A Data Driven Approach(2019-12) Schoen, Alexander C.; Ben Miled, Zina; Dos Santos, Euzeli C.; King, Brian S.This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks. The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model. The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.Item Multiobjective Optimization of Composite Square Tube for Crashworthiness Requirements Using Artificial Neural Network and Genetic Algorithm(2021-12) Zende, Pradnya; Dalir, Hamid; Agarwal, Mangilal; Tovar, AndresDesign optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.Item Thermoregulatory effects of psychostimulants and exercise: data-driven modeling and analysis(2018-04) Behrouzvaziri, Abolhassan; Molkov, YaroslavThermoregulation system in mammal keeps their body temperature in a vital and yet narrow range of temperature by adjusting two main activities, heat generation, and heat loss. Also, these activities get triggered by other causes such as exercise or certain drugs. As a result, thermoregulation system will respond and try to bring back the body temperature to the normal range. Although these responses are very well experimentally explored, they often can be unpredictable and clinically deadly. Therefore, this thesis aims to analytically characterize the neural circuitry components of the system that control the heat generation and heat loss. This modeling approach can help us to analyze the relationship between different components of the thermoregulation system without directly measuring them and explain its complex responses in mathematical form. The first chapter of the thesis is dedicated to introducing a mathematical modeling approach of the circuitry components of the thermoregulation system in response to Methamphetamine which was first published in [1]. Later, in other chapters, we will expand this mathematical framework to study the other components of this system under different conditions such as different circadian phases, various pharmacological interventions, and exercise. This thesis is composed by materials from the following papers. CHAPTER 1 uses the main idea, model, and figures from References [1]. Meanwhile, CHAPTER 2 is based on [2] coauthored with me and is reformatted according to Purdue University Thesis guidelines. Also, CHAPTER 3 interpolates materials from reference [3] coauthored and is reformatted to comply with Purdue University Thesis guidelines. CHAPTER 4 is inserted from the reference [4] and is reformatted according to Purdue University Thesis guidelines. Finally, CHAPTER 5 is based on Reference [5] and is reformatted according to Purdue University Thesis guidelines. Some materials from each of these references have been used in the introduction Chapter.