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This research reflects a qualitative exploration of the timeliness of pursuits of higher education by females who were classified as non-traditional students at Concord College in southern West Virginia. The researcher studied females, educated in West Virginia, who had completed at least 30 semester hours, and who were at least 25 years of age at the time of admission. The study focused on identifying several factors common to many non-traditional students, including the factors that influenced them to not pursue their education as traditional students, the factors that changed so they recognized higher education as a viable option for themselves, and the factors that influenced them, positively or negatively, in their perseverance to continue in an undergraduate program. / Ph. D.

This work presents a framework for the exploration and path planning for a collaborative UAV and UGV system. The system is composed of a UAV with a stereo system for obstacle detection and a UGV with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the UAV. Both identify frontiers for exploration with the Dijkstra Frontier method using Dijkstra's Algorithm to identify a frontier with unknown space, and the other uses a bi-directional RRT to identify multiple frontiers for selection. The final algorithm developed was for to give the UGV partial plans when an entire plan is not yet found. This improves the overall mission tempo. The algorithm is designed to keep the UGV a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in Gazebo using the ROS framework. The Dijkstra Frontier method was also tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The UGV partial plan method showed a decreased traveled distance for the UGV but increases in UGV mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions. / Master of Science / This work presents a framework for the exploration and path planning for a collaborative aerial and ground vehicle robotic system. The system is composed of an aircraft with a camera system for obstacle detection and a ground vehicle with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the aircraft. Both identify frontiers for exploration with the Dijkstra Frontier method using path planning algorithms to identify a frontier with unknown space (Dijkstra Frontier), and the other uses a sampling based path planning method (RRT Explore) to identify multiple frontiers for selection. The final algorithm developed was for to give the ground vehicle intermediate plans when an entire plan is not yet found. The algorithm is designed to keep the ground vehicle a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in a simulation framework using Robot Operating System and one exploration method was tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The ground vehicle intermediate plan method showed a decreased traveled distance for the ground vehicle but increases in ground vehicle mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions.

国立情報学研究所で電子化したコンテンツを使用している。

Master of Science / Department of Geology / Matthew Totten / Petroleum producing areas within the mid-continent region discovered in the first half of the 1900’s often ignored the potential of deeper horizons once hydrocarbons were discovered in shallower zones. In Ness County, Kansas the deepest horizon typically explored are Mississippian-aged rocks. One of the largest fields in Ness County is the Aldrich Field, first discovered in 1929. The Mississippian in this field contains an active water-drive, which was produced by an “open-hole” completion method. This precluded drilling deeper horizons. Although modern drilling and completion techniques allow drilling through and isolating water-drive reservoirs like the Mississippian, very few deep exploratory wells have been drilled in Ness County. Wells that penetrate sub-Mississippian horizons are typically drilled as disposal wells, along the flanks of the main structure. This study evaluates the potential of several sub-Mississippian formations to be hydrocarbon reservoirs. Drill cuttings from five wells that penetrate these formations were analyzed using a combination of petrographic microscope, Scanning Electron Microscope (SEM), and chemical methods. Reservoir quality porosity was observed in several sub-Mississippian zones. The presence of hydrocarbon staining was observed in the Viola samples of three wells, and the Arbuckle in one well. Staining was confirmed by EDS spectra under the SEM. The results of this study suggest a good potential of zones deeper than normally drilled to contain hydrocarbons in rocks with reservoir quality porosity. These zones were not drill stem tested in the Aldrich field, and structural advantage to these wells might be expected by drilling the apex of the trapping anticline to further evaluate the deeper horizons.

Résumé: Dans le cadre de sa « Stratégie d'exploration globale » établie en 2009, l'Agence spatiale canadienne désire caractériser la répartition du minéral ilménite, un oxyde de fer et titane (FeTiO3). Ce minéral est considéré comme une ressource clé, puisque l'oxygène qu'il contient peut être extrait et pourrait servir à la fois de carburant et de matière première à l'établissement d'humains sur la Lune. La cartographie de l'ilménite n'a pas été réalisée à ce jour en raison de l'absence de données dans les longueurs d'ondes de l'ultraviolet et/ou de données hyperspectrales. L'objectif du projet est de cartographier l'ilménite lunaire pour les régions de Mare Australe et de la Mare Ingenii là où sont disponibles les récentes données ultraviolets du capteur Wide Angle Camera (WAC) de Lunar Reconnaissance Orbiter. Pour ce faire, les données du capteur WAC sont intégrées aux données des capteurs UVVIS/NIR de Clementine, généralement utilisées. L'abondance en ilménite est modélisée à l'aide de la théorie du transfert radiatif de Hapke et limitée par le contenu maximal en ilménite calculé à partir des cartes de FeO et Ti02, celles-ci étant dérivées des données des capteurs UV VIS/NIR de Clementine. L'abondance en ilménite est modélisée pour les pixels offrant le meilleur rapport signal/bruit, soit 0,47 % de la région de la Mare Australe et 1,62 % la région de la Mare Ingenii, puis interpolée par krigeage. L'abondance en ilménite modélisée est de 0 à 11,01 % pour la région de la Mare Australe et de 0 à 6,01 % pour la région de la Mare Ingenii. La précision (RMSE) est de t- 2,87 % pour les pixels modélisés. La précision (RMSE) est de ± 3,55 et de ± 3,25 % pour les pixels interpolés de la région de la Mare Australe et la région de la Mare Ingenii respectivement. Pour la première fois, les données de l'ultraviolet sont intégrées aux données du visible au proche infrarouge et un contenu maximal en ilménite est utilisé. Cela permet de cartographier l'ilménite avec précision; la corrélation entre l'abondance en ilménite modélisée et l'abondance réelle en ilménite contenue dans les échantillons lunaires utilisés pour valider le modèle est de 0,88.||Abstract: According to its " Global Exploration Strategy " established in 2009, the Canadian Space Agency wants to map the distribution of ilmenite, an iron and titanium oxide mineral (FeTiO[subscript 3]), on the Moon. Ilmenite is considered a key mineral, because the oxygen it contains can be extracted and could be used for life support and as a propellant in the perspective of human exploration of the Moon. No ilmenite map actually exists because ultraviolet (UV) and/or hyperspectral data necessary for its identification were not available. The objective of this study is to map ilmenite distribution on the Moon, over Mare Australe and Mare Ingenii regions, where recent UV data from the Wide Angle Camera (WAC) onboard Lunar Reconnaissance Orbiter has been released. To do so, we integrate WAC UV data to the widely used Clementine UVVIS/NIR cameras (UV, visible and near-infrared) data. We model ilmenite abundance using Hapke radiative transfer theory and the maximum ilmenite abundance we calculate based on available UVVIS/NIR FeO and TiO[subscript 2] maps. We model ilmenite abundance only for pixel having a high signal-to-noise ratio (0.47 % of Mare Australe region and 1.62 % of Mare Ingenii region) and interpolate the results by kriging. We find that the modeled ilmenite abundances range between 0 and 11.01 % for Mare Australe regions, and between 0 and 6.01 % for Mare Ingenii region. The root mean square error (RMSE) is +/- 2.87 % of ilmenite for the modeled pixels. The root mean square error (RMSE) is +/- 3.55 and +/- 3.25 % of ilmenite for the interpolated values in Mare Australe and Mare Ingenii regions respectively. For the first time, UV data have been integrated to visible and near-infrared data, and a maximum ilmenite content has been used as a constraint in the radiative transfer model. This enable's to obtain an enhanced precision of ilmenite abundances; the correlation between modeled and real ilmenite abundances from Apollo samples used to validate the model is 0,88. [Symboles non conformes]

For robotic systems to continue to move towards ubiquity, robots need to be more autonomous. More autonomy dictates that robots need to be able to make better decisions. Control theory and machine learning are fields of robotics that focus on the decision making process. However, each of these fields implements decision making at different levels of abstraction and at different time scales. Control theory defines low-level decisions at high rates, while machine learning defines high-level decision at low rates. The objective of this research is to integrate tools from both machine leaning and control theory to solve higher dimensional, complex problems, and to optimize the decision making process. Throughout this research, multiple algorithms were created that use concepts from both control theory and machine learning, which provide new tools for robots to make better decisions. One algorithm enables a robot to learn how to optimally explore an unknown space, and autonomously decide when to explore for new information or exploit its current information. Another algorithm enables a robot to learn how to locomote with complex dynamics. These algorithms are evaluated both in simulation and on real robots. The results and analysis of these experiments are presented, which demonstrate the utility of the algorithms introduced in this work. Additionally, a new notion of “learnability” is introduced to define and determine when a given dynamical system has the ability to gain knowledge to optimize a given objective function.

This thesis investigates the relationship between elastic properties and rock properties of the Haynesville Shale using rock physics modeling, simultaneous seismic inversion, and grid searching. A workflow is developed in which a rock physics model is built and calibrated to well data in the Haynesville Shale and then applied to 3D seismic inversion data to predict porosity and mineralogy away from the borehole locations. The rock physics model describes the relationship between porosity, mineral composition, pore shape, and elastic stiffness using the anisotropic differential effective medium model. The calibrated rock physics model is used to generate a modeling space representing a range of mineral compositions and porosities with a calibrated mean pore shape. The model space is grid searched using objective functions to select a range of models that describe the inverted P-impedance, S-impedance, and density volumes. The selected models provide a range of possible rock properties (porosity and mineral composition) and an estimate of uncertainty. The mineral properties were mapped in three dimensions within the area of interest using this modeling technique and inversion workflow. This map of mineral content and porosity can be interpreted to predict the best areas for hydraulic fracturing. / text

<p>Urban Exploration går ut på att utforska övergivna platser. Uppsatsens syfte är att beskriva fenomenet och att diskutera det utifrån ett kulturarvs- och ungdomskulturperspektiv. För att kunna göra detta argumenterar jag, med utgångspunkt i litteraturstudier, för att Urban Exploration bör ses som ett uttryck för senmodernitet, med de särskilda förutsättningar för identitetsskapande och världsuppfattning som det innebär för individen. Som källmaterial har det Urban Exploration-utövarna själva skriver om sin verksamhet på olika Internetsidor använts. Min slutsats är att individen genom att fylla platsen med subjektiv mening skapar dels ett kulturarv, dels sig själv som ett politiskt och historiskt subjekt.</p>

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