novel approach to extend the decision-making capabilities of unmanned surface vehicles (USVs) is presented in this work. A multi-objective framework is described where separate controllers command different behaviors according to a desired trajectory. Three behaviors are examined – transiting, station-keeping and reversing. Given the desired trajectory, the vehicle is able to autonomously recognize which behavior best suits a portion of the trajectory. The USV uses a combination of a supervisory switching control structure and a reinforcement learning algorithm to create a hybrid deliberative and reactive approach to switch between controllers and actions. Reinforcement learning provides a deliberative method to create a controller switching policy, while supervisory switching control acts reactively to instantaneous changes in the environment. Each action is restricted to one controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom. Field experiments are presented to validate this system on the water with a physical USV platform under Sea State 1 conditions. Main outcomes of this work are that the proposed system provides better performance than a comparable gain-scheduled nonlinear controller in terms of an Integral of Absolute Error metric. Additionally, the deliberative component allows the system to identify dynamically infeasible trajectories and properly accommodate them. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Silicon carbide as a representative wide band-gap semiconductor has recently received wide attention due to its excellent physical, thermal and especially electrical properties. It becomes a promising material for electronic and optoelectronic device under high-temperature, high-power and high-frequency and intense radiation conditions. During the Silicon Carbide crystal grown by the physical vapor transport process, the temperature gradients induce thermal stresses which is a major cause of the dislocations multiplication. Although large dimension crystal with low dislocation density is required for satisfying the fast development of electronic and optoelectronic device, high dislocation densities always appear in large dimension crystal. Therefore, reducing dislocation density is one of the primary tasks of process optimization. This dissertation aims at developing a transient finite element model based on the Alexander-Haasen model for computing the dislocation densities in a crystal during its growing process. Different key growth parameters such as temperature gradient, crystal size will be used to investigate their influence on dislocation multiplications. The acceptable and optimal crystal diameter and temperature gradient to produce the lowest dislocation density in SiC crystal can be obtained through a thorough numerical investigation using this developed finite element model. The results reveal that the dislocation density multiplication in SiC crystal are easily affected by the crystal diameter and the temperature gradient. Generally, during the iterative calculation for SiC growth, the dislocation density multiples very rapidly in the early growth phase and then turns to a relatively slow multiplication or no multiplication at all. The results also show that larger size and higher temperature gradient causes the dislocation density enters rapid multiplication phase sooner and the final dislocation density in the crystal is higher. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Studies of composite multihull structure under wave loads, extreme loads, and blast loads have been conducted using finite element and computational fluid dynamics (CPF) tools. A comprehensive finite element tool for structural analysis of composite multi-hull structures is developed. Two-way fluid structure interaction (FSI) is implemented by coupling finite element analysis (FEA) and CFD. FEA models have been developed using sandwich construction having composite face sheets and a foam core. Fluid domain was modeled using the CFD code, CFX and a wave motion was simulated based on Sea State 5... In addition to hydrodynamic loads, the simulation of composite ship under extreme loads is performed. Stress analysis was performed and dynamic response of the hull was determined in time domain. In the final analysis, an underwater explosion model was developed to study the composite hull resistance to blast load. / by Siyuan Ma. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

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In this thesis, we develop two exact algorithms and a heuristic procedure for Multiobjective Combinatorial Optimization Problems (MOCO). Our exact algorithms guarantee to generate all nondominated solutions of any MOCO problem. We test the performance of the algorithms on randomly generated problems including the Multiobjective Knapsack Problem, Multi-objective Shortest Path Problem and Multi-objective Spanning Tree Problem. Although we showed the algorithms work much better than the previous ones, we also proposed a fast heuristic method to approximate efficient frontier since it will also be applicable for real-sized problems. Our heuristic approach is based on fitting a surface to approximate the efficient frontier. We experiment our heuristic on randomly generated problems to test how well the heuristic procedure approximates the efficient frontier. Our results showed the heuristic method works well.

In vehicles one of the most important components which affect comfort of the driver and the purchasing decision is the driver&rsquo / s seat. In order to improve design of a driver seat in a leader company of automotive sector, a comprehensive analysis of customer expectations from the driver seat is performed with a cross functional team formed by representatives of design, marketing, production, quality and services departments. In this study, collection of customer voice data and development of an exceptional &ldquo / customer satisfaction estimation model&rdquo / using these data are presented. Data are modeled by the help of Logistic Regression. This model is able to estimate how much a given customer is likely to be satisfied with the driver seat at a certain confidence level. It is also explained how this model can be used to identify design improvement opportunities that help increase the probability that a customer likes the driver seat. The modeling and analysis approach used for the particular case is applicable in general to many other cases of product improvement or development.

In this study, we analyze a decentralized supply chain with a single retailer and a single manufacturer where the retailer sells two products in a single period. The products offered by the retailer consist of families of closely related products, which differ from each other in terms of a limited number of features only. The retailer places initial orders based on preliminary demand forecasts at the beginning of the period and has an opportunity to modify his initial order after receiving perfect demand information. However, the final orders of the retailer are constrained by his initial orders. Furthermore, the manufacturer is obligated to fill the retailer&rsquo / s final order for each product. The manufacturer has two options for procurement. The first procurement option is regular delivery at the beginning of the period, after the initial orders of the retailer. The next one is expedited delivery, after the updated orders of the retailer are received. The expedited delivery is more expensive than regular. In this setting, our objective is to present an analytical model for this contract and characterize the optimal policies for the retailer and the manufacturer. We analyze three different levels of order adjustment flexibility settings: (i) no order adjustment, (ii) unlimited order adjustment and (iii) limited order adjustment.

In this dissertation, we consider a multi-item two-echelon inventory distribution system in which the central warehouse operates with (Q, R) policy, and each local warehouse implements base-stock policy. The objective is to find the policy parameters minimizing the relevant system-wide costs subject to an aggregate mean response time constraint at each facility. We first propose an exact solution procedure based on a branch-and-price algorithm to find the relevant policy parameters of the system considered. Then, we propose four alternative heuristics to find the optimal or near-optimal policy parameters of large practical-size systems. The first heuristic, which we call the Lagrangian heuristic, is based on the simultaneous approach and relies on the integration of a column generation method and a greedy algorithm. The other three heuristics are based on the sequential approach, in which first the order quantities are determined using a batch size heuristic, then the reorder levels at the central warehouse and the basestock levels at the local warehouses are determined through the same method used for the Lagrangian heuristic. We also propose a lower bound for the system-wide cost. Later, we extend our study to compound Poisson demand. The performance of the Lagrangian heuristic is found to be extremely well and improves even further as the number of parts increases. Also the computational requirement of the heuristic is quite tolerable. This makes the heuristic very promising for large practical industry-size problems. The performance of the sequential heuristics is also satisfactory, but not as much as the Lagrangian heuristic.

With the recent development of smart-well technology, the reservoir community now faces the challenge of developing robust and efficient techniques for reservoir characterization by means of data inversion. Unfortunately, classical history-matching methodologies do not possess computational efficiency and robustness needed to assimilate data measured almost in real time. Therefore, the reservoir community has started to explore techniques previously applied in other disciplines. Such is the case of the representer method, a variational data assimilation technique that was first applied in physical oceanography. The representer method is an efficient technique for solving linear inverse problems when a finite number of measurements are available. To the best of our knowledge, a general representer-based methodology for nonlinear inverse problems has not been fully developed. We fill this gap by presenting a novel implementation of the representer method applied to the nonlinear inverse problem of identifying petrophysical properties in reservoir models. Given production data from wells and prior knowledge of the petrophysical properties, the goal of our formulation is to find improved parameters so that the reservoir model prediction fits the data within some error given a priori. We first define an abstract framework for parameter identification in nonlinear reservoir models. Then, we propose an iterative representer-based scheme (IRBS) to find a solution of the inverse problem. Sufficient conditions for convergence of the proposed algorithm are established. We apply the IRBS to the estimation of absolute permeability in single-phase Darcy flow through porous media. Additionally, we study an extension of the IRBS with Karhunen-Loeve (IRBS-KL) expansions to address the identification of petrophysical properties subject to linear geological constraints. The IRBS-KL approach is compared with a standard variational technique for history matching. Furthermore, we apply the IRBS-KL to the identification of porosity, absolute and relative permeabilities given production data from an oil-water reservoir. The general derivation of the IRBS-KL is provided for a reservoir whose dynamics are modeled by slightly compressible immiscible displacement of two-phase flow through porous media. Finally, we present an ad-hoc sequential implementation of the IRBS-KL and compare its performance with the ensemble Kalman filter. / text