In Situ Parameter Estimation of a Single-Phase Voltage Source Inverter using Pseudo-Random Impulse Sequence Perturbation

Electrical and Electronic Engineerin

A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations

This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB_1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB_1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described

Turbulence observations on soundings balloons: geophysical Interpretations based on instrumental revisions

Turbulence is of fundamental importance for energy transport in the atmosphere. In this work, several severe instrumental effects on turbulence measurements from sounding balloons are investigated. They are circumvented with the new LITOS version that has been developed and tested against another instrument in the course of this thesis. Furthermore, LITOS is used for two geophysical case studies: First, we investigate turbulence generation by wave breaking. Second, we interpret a measurement where small-scale turbulence influences large-scale weather patterns around the jet stream.Turbulenz ist von fundamentaler Bedeutung für den Energietransport in der Atmosphäre. In dieser Arbeit werden wichtige instrumentelle Effekte von ballongestützten Turbulenzmessungen untersucht. Die neue, im Rahmen dieser Arbeit entwickelte und vergleichend getestete Version des LITOS-Instruments umgeht diese Beeinflussungen. Des Weiteren wird LITOS für zwei geophysikalische Fallstudien genutzt: Erstens untersuchen wir Turbulenzentstehung durch Wellenbrechen. Zweitens interpretieren wir eine Messung, bei der kleinskalige Turbulenzen großskalige Wettergeschehen am Jet-Stream beeinflussen

Proceedings of the NASA Symposium on Global Wind Measurements

This Proceedings contains a collection of the papers which were presented at the Symposium and Workshop on Global Wind Measurements. The objectives and agenda for the Symposium and Workshop were decided during a planning meeting held in Washington, DC, on 5 February 1985. Invited papers were presented at the Symposium by meteorologists and leading experts in wind sensing technology from the United States and Europe on: (1) the meteorological uses and requirements for wind measurements; (2) the latest developments in wind sensing technology; and (3) the status of our understanding of the atmospheric aerosol distribution. A special session was also held on the latest development in wind sensing technology by the United States Air Force

PSA 2016

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

Review of Particle Physics (2010)

A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. All tables, listings, and reviews (and errata) are also available on the Particle Data Group website: pdg.lbl.gov.This biennial Review summarizes much of particle physics. Using data from previous editions, plus 2158 new measurements from 551 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. Among the 108 reviews are many that are new or heavily revised including those on neutrino mass, mixing, and oscillations, QCD, top quark, CKM quark-mixing matrix, Vud & Vus, Vcb & Vub, fragmentation functions, particle detectors for accelerator and non-accelerator physics, magnetic monopoles, cosmological parameters, and big bang cosmology.MICINN, Spain (FPA2009-07264-E). The publication of the Review of Particle Physics is supported by the Director, Office of Science, Office of High Energy and Nuclear Physics, the Division of High Energy Physics of the U.S. Department of Energy under Contract No. DE–AC02–05CH11231; by the U.S. National Science Foundation under Agreement No. PHY-0652989; by the European Laboratory for Particle Physics (CERN); by an implementing arrangement between the governments of Japan (MEXT: Ministry of Education, Culture, Sports, Science and Technology) and the United States (DOE) on cooperative research and development; and by the Italian National Institute of Nuclear Physics (INFN)