Forecasting Volcanic Activity Using An Event Tree Analysis System And Logistic Regression

Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree’s decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system

Urban sprawl in the state of Missouri : current trends, driving forces, and predicted growth on Missouri's natural landscape

Title from PDF of title page (University of Missouri--Columbia, viewed on March 5, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Hong S. HeIncludes bibliographical references.Vita.Ph.D. University of Missouri--Columbia 2012."December 2012"Missouri reflects a full range of sprawl characteristics that include large metropolitan centers, which led growth in 1980s, and smaller metropolitan and rural areas, which led growth in 1990s. In order to study the historical patterns of sprawl, there is a need to quantitatively and geographically depict the extent and density of impervious surface for three time periods of 1980, 1990, and 2000 for the entire state of Missouri. Mapped impervious surface is the best candidate of ancillary data for dasymetric mapping of population in several comparison studies. The current research examines the performances of dasymetric mapping of population with imperviousness as ancillary data and regression analysis of population using imperviousness as a predictor Results from this work can be aggregated to any geographical unit (hydrologic boundaries, administrative boundaries, etc.). A pilot future urban growth study for the two decades of 1980s and 1990s was done in Missouri. The historical urban growth of the two decades were analyzed then coupled with various predictor variables to investigate the influence of each predictor variables towards the process of urban growth. The knowledge learned from the process is then used to build an urban growth simulation model that is GIS-based with open framework for ease of management and improvement. Pixel level urban growth was simulated for year 2010, 2020 and 2030. This model framework is developed with the ultimate goal of simulating urban growth for the entire state of Missouri.Includes bibliographical reference

Monitoring bioinspired fibrillar grippers by contact observation and machine learning

The remarkable properties of bio-inspired microstructures make them extensively accessible for various applications, including industrial, medical, and space applications. However, their implementation especially as grippers for pick-and-place robotics can be compromised by multiple factors. The most common ones are alignment imperfections with the target object, unbalanced stress distribution, contamination, defects, and roughness at the gripping interface. In the present work, three different approaches to assess the contact phenomena between patterned structures and the target object are presented. First, in-situ observation and machine learning are combined to realize accurate real-time predictions of adhesion performance. The trained supervised learning models successfully predict the adhesion performance from the contact signature. Second, two newly developed optical systems are compared to observe the correct grasping of various target objects (rough or transparent) by looking through the microstructures. And last, model experiments are provided for a direct comparison with simulation efforts aiming at a prediction of the contact signature and an analysis of the rate and preload-dependency of the adhesion strength of a soft polymer film in contact with roughness-like surface topography. The results of this thesis open new perspectives for improving the reliability of handling systems using bioinspired microstructures.Durch die besonderen Eigenschaften bioinspirierter Mikrostrukturen können diese für verschiedene Anwendungen genutzt werden, einschließlich industrieller, medizinischer und Weltraumanwendungen. Ihre Implementierung, insbesondere als Greifer für Pick-and-Place-Robotiker, kann jedoch durch mehrere Faktoren beeinträchtigt werden. Am häufigsten sind Ausrichtungsmängel an das Zielobjekt, unausgeglichene Spannungsverteilungen, Defekte und Rauheit an der Greifschnittstelle. Die vorliegende Arbeit zeigt drei verschiedene Ansätze, um den Kontakt zwischen strukturierten Adhäsiven und Zielobjekten zu untersuchen. Zunächst werden in-situ Beobachtungen und maschinelles Lernen kombiniert, um Echtzeitvorhersagen der Adhäsionsleistung zu ermöglichen. Die trainierten Modelle werden verwendet, um die Haftungsleistung anhand der Kontaktsignatur des Pads erfolgreich zu prognostizieren. Anschließend werden zwei neu entwickelte, optische Systeme verglichen, mit denen das korrekte ” Greifen“ von verschiedenen Objekten (mit rauen oder undurchsichtigen Oberflächen) durch die Mikrostrukturen live verfolgt werden kann. Zuletzt werden Modellexperimente durchgeführt, die mit Simulationen der Signatur des Kontakts einer weichen Polymerschicht mit einer idealisierten rauen Gegenfläche direkt verglichen werden können. Die Ergebnisse dieser Arbeit eröffnen neue Perspektiven zur zuverlässigeren Verwendung von Handhabungssystemen mit bioinspirierten Mikrostrukturen.Leibniz Competition Grant MUSIGAND (No. K279/2019) awarded to Eduard Arz

Optical In-Process Measurement Systems

Information is key, which means that measurements are key. For this reason, this book provides unique insight into state-of-the-art research works regarding optical measurement systems. Optical systems are fast and precise, and the ongoing challenge is to enable optical principles for in-process measurements. Presented within this book is a selection of promising optical measurement approaches for real-world applications

A study of deep learning and its applications to face recognition techniques

El siguiente trabajo es el resultado de la tesis de maestría de Fernando Suzacq. La tesis se centró alrededor de la investigación sobre el reconocimiento facial en 3D, sin la reconstrucción de la profundidad ni la utilización de modelos 3D genéricos. Esta investigación resultó en la escritura de un paper y su posterior publicación en IEEE Transactions on Pattern Analysis and Machine Intelligence. Mediante el uso de iluminación activa, se mejora el reconocimiento facial en 2D y se lo hace más robusto a condiciones de baja iluminación o ataques de falsificación de identidad. La idea central del trabajo es la proyección de un patrón de luz de alta frecuencia sobre la cara de prueba. De la captura de esta imagen, nos es posible recuperar información real 3D, que se desprende de las deformaciones de este patrón, junto con una imagen 2D de la cara de prueba. Este proceso evita tener que lidiar con la difícil tarea de reconstrucción 3D. En el trabajo se presenta la teoría que fundamenta este proceso, se explica su construcción y se proveen los resultados de distintos experimentos realizados que sostienen su validez y utilidad. Para el desarrollo de esta investigación, fue necesario el estudio de la teoría existente y una revisión del estado del arte en este problema particular. Parte del resultado de este trabajo se presenta también en este documento, como marco teórico sobre la publicación

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization