Sodium in School Lunches Before and After Implementation of the Healthy, Hunger Free Kids Act of 2010

Thesis (Master's)--University of Washington, 2016-08

Multi-Disciplinary Applications of Oceanographic Geophysical Data Collection

Thesis (Ph.D.)--University of Washington, 2016-08

The Impact of Light Rail Transit-Oriented Development on Residential Property Value in Seattle, WA

Thesis (Master's)--University of Washington, 2016-06

Analyse numérique du changement de phase solide-liquide autour d'un tube horizontal soumis à une sollicitation thermique périodique

Ce mémoire présente un modèle mathématique et numérique pour analyser le comportement d’une unité de stockage thermique à changement de phase solide-liquide représentée par un tube autour duquel se trouve le matériau à changement de phase. Le système est soumis à une charge oscillant entre le chauffage et le refroidissement. Une analyse d’ordre de grandeur permet de prédire le comportement du système en fonction des principaux nombres adimensionnels. Un paramètre adimensionnel est proposé pour délimiter les concepts dans lesquels la conduction domine par rapport à ceux où la convection naturelle domine. L’étude dévoile l’impact des paramètres de conception de l’unité de stockage thermique sur son fonctionnement et approfondit les connaissances dans le domaine du changement de phase avec convection naturelle. Différents indicateurs ont été développés pour analyser la performance du système, tels que les dimensions de la zone affectée thermiquement, le volume fondu ou solidifié et une analyse fréquentielle. Des corrélations sont proposées pour déterminer facilement le comportement du système.

Modelación de la prima de riesgo que asumen los agentes en transacciones a plazo a través de los factores que influyen y explican el comportamiento de la tasa de cambio de la moneda colombiana con respecto a la de Estados Unidos

En este trabajo se pretende establecer que factores fundamentales influyen en el movimiento de la tasa de cambio COP/USD en un periodo intra-diario de forma horaria, para así poder establecer un modelo que ayude a estimar la prima de riesgo de la tasa de cambio colombiana -- Basados en Pantoja (2012)1, se pretende la aplicación de un modelo VAR (vectores autorregresivos) para estimar la prima de riesgo de la tasa de cambio, donde se encontró que este modelo no es el modelo más adecuado para explicar la serie de datos utilizada, por lo que se propone un modelo GARCH para modelar la serie -- Se encontró que hay factores fundamentales que explican la prima, como lo son el WTI, el S&P500 y la tasa de cambio EUR/USD

Capacity forecasting for radio access networks

The mobile networks market (focus of this work) strategy is based on the consolidation of the installed structure and the optimization of the already existent resources. The increasingly competition and aggression of this market requires, to the mobile operators, a continuous maintenance and update of the networks in order to obtain the minimum number of fails and provide the best experience for its subscribers. In this context, this dissertation presents a study aiming to assist the mobile operators improving future network modifications. In overview, this dissertation compares several forecasting methods (mostly based on time series analysis) capable of support mobile operators with their network planning. Moreover, it presents several network indicators about the more common bottlenecks.

FOR-TSM: desarrollo de una herramienta de pronósticos con modelos de series de tiempo

Uno de los temas más complejos y necesarios en los cursos de Administración de Operaciones, es el uso de los pronósticos con modelos de series de tiempo (TSM por sus siglas en inglés) -- Para facilitar el entendimiento y ayudar a los estudiantes a comprender fácilmente los pronósticos de demanda, este proyecto presenta FOR TSM, una herramienta desarrollada en MS Excel VBA® -- La herramienta fue diseñada con una Interfaz gráfica de Usuario (GUI por sus siglas en inglés) para explicar conceptos fundamentales como la selección de los parámetros, los valores de inicialización, cálculo y análisis de medidas de desempeño y finalmente la selección de modelos

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Human exposure to particulate and gaseous pollutants in a bar.

There is increasing evidence of a causal link between airborne particles and ill health and this study examined the exposure to both airborne particles and the gas phase contaminants of environmental tobacco smoke (ETS) in a bar. The work reported here utilized concurrent and continuous monitoring using real-time optical scattering personal samplers to record particulate (PM10) concentrations at two internal locations. Very high episodes were observed in seating areas compared with the bar area. A photo-acoustic multi-gas analyser was used to record the gas phases (CO and CO2) at eight different locations throughout the bar and showed little spatial variation. This gave a clear indication of the problems associated with achieving acceptable Indoor Air Quality in a public space and identified a fundamental problem with the simplistic design approach taken to ventilate the space. Both gaseous and particulate concentrations within the bar were below maximum recommended levels although the time-series analysis illustrated the highly episodic nature of this exposure.