Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis

Every year, millions of scientific images are acquired in order to study the auroral phenomena. The accumulated data contain a vast amount of untapped information that can be used in auroral science. Yet, auroral research has traditionally been focused on case studies, where one or a few auroral events have been investigated and explained in detail. Consequently, theories have often been developed on the basis of limited data sets, which can possibly be biased in location, spatial resolution or temporal resolution. Advances in technology and data processing now allow for acquisition and analysis of large image data sets. These tools have made it feasible to perform statistical studies based on auroral data from numerous events, varying geophysical conditions and multiple locations in the Arctic and Antarctic. Such studies require reliable auroral image processing techniques to organize, extract and represent the auroral information in a scientifically rigorous manner, preferably with a minimal amount of user interaction. This dissertation focuses on two such branches of image processing techniques: machine learning classification and multi-viewpoint analysis. Machine learning classification: This thesis provides an in-depth description on the implementation of machine learning methods for auroral image classification; from raw images to labeled data. The main conclusion of this work is that convolutional neural networks stand out as a particularly suitable classifier for auroral image data, achieving up to 91 % average class-wise accuracy. A major challenge is that most auroral images have an ambiguous auroral form. These images can not be readily labeled without establishing an auroral morphology, where each class is clearly defined. Multi-viewpoint analysis: Three multi-viewpoint analysis techniques are evaluated and described in this work: triangulation, shell-projection and 3-D reconstruction. These techniques are used for estimating the volume distribution of artificially induced aurora and the height and horizontal distribution of a newly reported auroral feature: Lumikot aurora. The multi-viewpoint analysis techniques are compared and methods for obtaining uncertainty estimates are suggested. Overall, this dissertation evaluates and describes auroral image processing techniques that require little or no user input. The presented methods may therefore facilitate statistical studies such as: probability studies of auroral classes, investigations of the evolution and formation of auroral structures, and studies of the height and distribution of auroral displays. Furthermore, automatic classification and cataloging of large image data sets will support auroral scientists in finding the data of interest, reducing the needed time for manual inspection of auroral images

A comparative volatility analysis and an enquiry into the future of Bitcoin

Master's thesis in Industrial EconomicsThe main object of this thesis is to investigate if Bitcoin has matured as a financial asset. We intend to do this by comparing the volatility of Bitcoin to the volatility of gold and S&P500 using the best fitting GARCH models. By doing this we can examine whether the volatility is decreasing, suggesting a maturing market. We will also look at the correlation between these assets. As part of this thesis we will provide a clear picture of what Bitcoin is, and how it functions. We are also going to uncover some of the opportunities and limitations that faces Bitcoin. This will be done by giving a thorough explanation of the technical aspects of Bitcoin to get a clear image of the security and reliability of Bitcoin and the blockchain-technology. To answer the questions presented in this thesis we used a variety of GARCH models to model the volatility of Bitcoin and other assets. This revealed that Bitcoin exhibits an extreme volatility, which does not seem to be decreasing or stabilizing. This lead to the conclusion that Bitcoin is not yet maturing as a financial asset.submittedVersio

Modelling Solar Orbiter Dust Detection Rates in Inner Heliosphere as a Poisson Process

Solar Orbiter provides dust detection capability in inner heliosphere, but estimating physical properties of detected dust from the collected data is far from straightforward. First, a physical model for dust collection considering a Poisson process is formulated. Second, it is shown that dust on hyperbolic orbits is responsible for the majority of dust detections with Solar Orbiter's Radio and Plasma Waves (SolO/RPW). Third, the model for dust counts is fitted to SolO/RPW data and parameters of the dust are inferred, namely: radial velocity, hyperbolic meteoroids predominance, and solar radiation pressure to gravity ratio as well as uncertainties of these. Non-parametric model fitting is used to get the difference between inbound and outbound detection rate and dust radial velocity is thus estimated. A hierarchical Bayesian model is formulated and applied to available SolO/RPW data. The model uses the methodology of Integrated Nested Laplace Approximation, estimating parameters of dust and their uncertainties. SolO/RPW dust observations can be modelled as a Poisson process in a Bayesian framework and observations up to this date are consistent with the hyperbolic dust model with an additional background component. Analysis suggests a radial velocity of the hyperbolic component around $(63 \pm 7) \mathrm{km/s}$ with the predominance of hyperbolic dust about $(78 \pm 4) \%$. The results are consistent with hyperbolic meteoroids originating between $0.02 \mathrm{AU}$ and $0.1 \mathrm{AU}$ and showing substantial deceleration, which implies effective solar radiation pressure to gravity ratio $\gtrsim 0.5$. The flux of hyperbolic component at $1 \mathrm{AU}$ is found to be $(1.1 \pm 0.2) \times 10^{-4} \mathrm{m^{-2}s^{-1}}$ and the flux of background component at $1 \mathrm{AU}$ is found to be $(5.4 \pm 1.5) \times 10^{-5} \mathrm{m^{-2}s^{-1}}$

Auroral Image Classification With Deep Neural Networks

Results from a study of automatic aurora classification using machine learning techniquesare presented. The aurora is the manifestation of physical phenomena in the ionosphere-magnetosphereenvironment. Automatic classification of millions of auroral images from the Arctic and Antarctic istherefore an attractive tool for developing auroral statistics and for supporting scientists to study auroralimages in an objective, organized, and repeatable manner. Although previous studies have presentedtools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with ahigh precision ( > 90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete,patchy, edge, and faint. Six different deep neural network architectures have been tested along with thewell-known classification algorithms: k-nearest neighbor (KNN) and a support vector machine (SVM).A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight,twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networksgenerally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highestperformance with an average classification precision of 92%.</p

The volume distribution of artificial aurora induced by HF radio waves in the ionosphere

This thesis presents three-dimensional modelling of artificial aurora in the ionospheric F layer, induced by high frequency (HF) O-mode radio waves from the EISCAT heating facility. Understanding how HF radio waves produces enhanced emission in the ionosphere has been one of the one of the major motivations for conducting heating experiments during the last decades. A key element for understanding the heating process is to accurately determine the RIOE (Radio Induced Optical Emission) volume distribution. There are two main theories explaining the RIOE, the accelerated electron excitation theory and the thermal electron excitation theory. Three different auroral construction techniques were considered, theoretical, semi-theoretical and free parameter search. The theoretical modelling is based on accelerated electron excitation theory predictions. Projections of the three dimensional models were compared to observational images of the artificial aurora, taken at the Auroral Large Imaging System (ALIS) in northern Sweden. ALIS takes simultaneous images of the RIOE at four separate ground based imaging stations. This allows tomographic-like reconstruction of the artificial aurora. The ALIS images were taken with different filters, providing auroral modelling of the 5577 Å, 6300 Å and 8446 Å emission lines. It was clear, by comparisons between the observational images and the projections of the modelled aurora, that the semi-theoretical and free parameter search modelling provided physically sound and statistically feasible constructions of the artificial aural. Whereas the theoretically modelled aurora projections did not agree with the observational images

Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis

Every year, millions of scientific images are acquired in order to study the auroral phenomena. The accumulated data contain a vast amount of untapped information that can be used in auroral science. Yet, auroral research has traditionally been focused on case studies, where one or a few auroral events have been investigated and explained in detail. Consequently, theories have often been developed on the basis of limited data sets, which can possibly be biased in location, spatial resolution or temporal resolution. Advances in technology and data processing now allow for acquisition and analysis of large image data sets. These tools have made it feasible to perform statistical studies based on auroral data from numerous events, varying geophysical conditions and multiple locations in the Arctic and Antarctic. Such studies require reliable auroral image processing techniques to organize, extract and represent the auroral information in a scientifically rigorous manner, preferably with a minimal amount of user interaction. This dissertation focuses on two such branches of image processing techniques: machine learning classification and multi-viewpoint analysis. Machine learning classification: This thesis provides an in-depth description on the implementation of machine learning methods for auroral image classification; from raw images to labeled data. The main conclusion of this work is that convolutional neural networks stand out as a particularly suitable classifier for auroral image data, achieving up to 91 % average class-wise accuracy. A major challenge is that most auroral images have an ambiguous auroral form. These images can not be readily labeled without establishing an auroral morphology, where each class is clearly defined. Multi-viewpoint analysis: Three multi-viewpoint analysis techniques are evaluated and described in this work: triangulation, shell-projection and 3-D reconstruction. These techniques are used for estimating the volume distribution of artificially induced aurora and the height and horizontal distribution of a newly reported auroral feature: Lumikot aurora. The multi-viewpoint analysis techniques are compared and methods for obtaining uncertainty estimates are suggested. Overall, this dissertation evaluates and describes auroral image processing techniques that require little or no user input. The presented methods may therefore facilitate statistical studies such as: probability studies of auroral classes, investigations of the evolution and formation of auroral structures, and studies of the height and distribution of auroral displays. Furthermore, automatic classification and cataloging of large image data sets will support auroral scientists in finding the data of interest, reducing the needed time for manual inspection of auroral images

Når relasjonar betyr noko

Denne oppgåva viser korleis andre aktørar innan forvaltninga påverkar ein kommune sitt handlingsrom for å drive innovasjonsarbeid. Både ulike problem, og arbeidet med problemløysinga, strekk seg over både kommunegrenser og forvaltningsnivå. Problemstillingar knytt til samferdsel og mobilitet er eit godt døme på dette. Kommunen er avhengig av andre aktørar der mange av desse også høyrer til offentleg sektor. Norske kommunar er under kontinuerleg press. Som det forvaltningsnivået som står nærast befolkninga, er det dei som både kjenner best behovet til innbyggarane sine og som sterkast føler konsekvensane av å stå ovanfor komplekse samfunnsproblem. Saman med eit statleg ønske om at oppgåvene til kommunane skal auke i omfang, set dette krav til at dei må evne å drive kontinuerleg fornying og forbetring. Oppgåva viser korleis det er å vere i ein kommune i ein slik situasjon. Datamaterialet kjem frå semistrukturerte djupneintervju med informantar frå Nittedal kommune og ein informant med nasjonalt perspektiv på situasjonen. Gjennom materialet viser eg korleis handlingar i relasjonar spelar inn på kommunen sitt handlingsrom for innovasjonsarbeid. Oppgåva koplar aktivt situasjonen i Nittedal med den generelle kommunale situasjonen i Norge. Det synleggjer ein dissonans mellom statlege signal og statleg tilrettelegging. Kombinert med utstrekt statleg detaljstyring gjennom øyremerking av middel, bidreg det til å krympe det kommunale handlingsrommet for innovasjon. I analysen blir dette sett i lys av teoretiske perspektiv. Desse er innovasjon gjennom omsetting (translasjonsprosessar), korleis makt blir utøvd gjennom handling i relasjonar og korleis spreiing av innovasjonar mellom organisasjonar kan skje gjennom omsetting. Oppgåva argumenterer for at Nittedal kommune har store problem med å lukkast med omsettingsarbeid mot andre aktørar i forvaltninga. Relasjonane er svekka, eller blir svekka, og dermed blir kommunen sin posisjon svakare i nettverket rundt mobilitetsproblematikken sin. Det blir synleggjort at Nittedal i denne situasjonen ikkje greier å kople saman innovasjonsmontasjen sin, noko som gjer at kommunen har ei redusert evne til å drive problemløysing og innovasjonsarbeid. Situasjonen viser også til nokre problematiske trekk rundt spreiingsperspektivet i staten. Spreiingsperspektivet som blir vist fram verkar å vere bygd rundt erfaringsdeling etter prosjekt. Oppgåva trekker heller fram at suksessfull spreiing ofte avhenger av at mottakarane er med på å forme prosjektet som det skal spreiast praksis frå. Oppgåva er skrive på oppdrag av Direktorat for forvaltning og IKT

Auroral classification ergonomics and the implications for machine learning

The machine-learning research community has focused greatly on bias in algorithms and have identified different manifestations of it. Bias in training samples is recognised as a potential source of prejudice in machine learning. It can be introduced by the human experts who define the training sets. As machine-learning techniques are being applied to auroral classification, it is important to identify and address potential sources of expert-injected bias. In an ongoing study, 13 947 auroral images were manually classified with significant differences between classifications. This large dataset allowed for the identification of some of these biases, especially those originating as a result of the ergonomics of the classification process. These findings are presented in this paper to serve as a checklist for improving training data integrity, not just for expert classifications, but also for crowd-sourced, citizen science projects. As the application of machine-learning techniques to auroral research is relatively new, it is important that biases are identified and addressed before they become endemic in the corpus of training data

Auroral Image Classification With Deep Neural Networks

Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere‐magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized, and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (>90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete, patchy, edge, and faint. Six different deep neural network architectures have been tested along with the well‐known classification algorithms: k‐nearest neighbor (KNN) and a support vector machine (SVM). A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight, twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet‐50 architecture achieved the highest performance with an average classification precision of 92%