Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2005. / Includes bibliographical references (p. 55-58).

In this dissertation, we apply stochastic approximation (SA) to two different problems addressed respectively in Part I and Part II. / The contribution of Part I is mostly theoretical. We consider the problem of online tracking of moving targets such as a signals, through noisy measurements. In particular, we study a non-stationary environment that is subject to sudden discontinuous changes in the underlying parameters of the system. We assume no a priori knowledge about the parameters nor the change-times. Our approach is based on constant stepsize SA. However, because of the unpredictable discontinuous changes, the choice of stepsize is difficult. Small stepsizes improve precision while large stepsizes allow the SA iterates to react faster to sudden changes. / We first investigate target estimation. Our work appears in [Levy 09]. We propose to combine a small constant stepsize with change-point monitoring, and to reset the process at a value closer to the new target when a change is detected. Because the environment is not stationary, we cannot directly apply the usual limit theorems. We thus give a theoretical characterization and discuss the tradeoff between precision and fast adaptation. We also introduce a new monitoring scheme, the regression-based hypothesis test. / Secondly, we consider an online version of the well-known Q-learning algorithm, which operates directly in its target environment, to optimize a Markov decision process. Online algorithms are challenging because the errors, necessarily made when learning, affect performance. Again, under a switching environment the usual limit theorems are not applicable. We introduce an adaptive stepsize selection algorithm based on weak convergence results for SA. Our algorithm automatically achieves a desirable balance between speed and accuracy. These findings are published in [Levy 06, Costa 09]. / In Part II, we study an applied problem related to the mining industry. Strategic management requires managing large portfolios of investments. Because financial resources are limited, only the projects with the highest net present value (NPV), their measure of economic value, will be funded. To value a mine project we need to consider future uncertainties. The approach commonly taken to value a project is to assume that if funded, the mine will be operated optimally throughout its life. Our final aim is not to provide an exact strategy, but to propose an optimization tool to improve decision-making in complex scenarios. Of all the variables involved, the typically large investments in infrastructure, as well as the uncertainty in commodity price, have the most significant impact on the mine value. We thus adopt a simplified model of the infrastructure and extraction optimization problem, subject to price uncertainty. / Common optimization methods are impractical for realistic size models. Our main contribution is the threshold optimization methodology based on measured valued differentiation (MVD) and SA. We also present another simulation-based method, the particles method [Dallagi 07], for comparison purposes. Both methods are well-adapted for high dimensional problems. We provide numerical results and discuss their characteristics and applicability.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2008. / Includes bibliographical references (p. 85-89).

The use of digital three-dimensional (3D) data has increased over the last two decades as private and public firms have begun to realize its utility. Mobile Terrestrial Laser Scanning (MTLS) or Mobile Mapping Systems (MMS), which utilizes LiDAR (Light Detection and Ranging) data collection from a moving platform along with advances in positioning systems—e.g., Global Navigation Satellite Systems (GNSS), Inertial Navigation Systems (INS), and Distance Measurement Instruments (DMIs)—have paved the way for efficient, abundant, and accurate 3D data collection. Validation and control targets are vital to ensure relative and/or absolute accuracy for MTLS projects. The focus of this dissertation is to evaluate several types of targets and the positional spacing of said targets for MTLS.<div><br></div><div>A mostly planar two-dimensional (2D) targeting system (painted target on ground) is commonly used to constrain, register, and validate the 3D point clouds from MTLS. In this dissertation, 3D objects—a sphere and a cube—were evaluated with varied angles of incidence and point densities as more appropriate alternatives to constrain and validate the 3D MTLS point clouds. Next, a planar circular 2D target—with the use of the raw intensity of the LiDAR pulse as another measured dimension—was evaluated as a proof of concept to also constrain and validate 3D LiDAR data. A third and final component of this dissertation explored analyses of INS data to determine the positional spacing of control and validation targets in MTLS projects to provide maximum accuracy for all data points.<br></div>