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Analytic knowledge about the systematic variation in a language has an important place in the description of the language. Such knowledge is interesting e.g. in the language teaching domain, as a background for various types of linguistic studies, and in the development of more dynamic speech technology applications. In previous studies, the effects of single variables or relatively small groups of related variables on the pronunciation of words have been studied separately. The work described in this thesis takes a holistic perspective on pronunciation variation and focuses on a method for creating general descriptions of phone-level pronunciation in discourse context. The discourse context is defined by a large set of linguistic attributes ranging from high-level variables such as speaking style, down to the articulatory feature level. Models of phone-level pronunciation in the context of a discourse have been created for the central standard Swedish language variety. The models are represented in the form of decision trees, which are readable for both machines and humans. A data-driven approach was taken for the pronunciation modelling task, and the work involved the annotation of recorded speech with linguistic and related information. The decision tree models were induced from the annotation. An important part of the work on pronunciation modelling was also the development of a pronunciation lexicon for Swedish. In a cross-validation experiment, several sets of pronunciation models were created with access to different parts of the attributes in the annotation. The prediction accuracy of pronunciation models could be improved by 42.2% by making information from layers above the phoneme level accessible during model training. Optimal models were obtained when attributes from all layers of annotation were used. The goal for the models was to produce pronunciation representations representative for the language variety and not necessarily for the individual speakers, on whose speech the models were trained. In the cross-validation experiment, model-produced phone strings were compared to key phonetic transcripts of actual speech, and the phone error rate was defined as the share of discrepancies between the respective phone strings. Thus, the phone error rate is the sum of actual errors and discrepancies resulting from desired adaptations from a speaker-specific pronunciation to a pronunciation reflecting general traits of the language variety. The optimal models gave an average phone error rate of 8.2%. / QC 20100901

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A power system consists of several critical components necessary for providing electricity from the producers to the consumers. Monitoring the lifetime of power system components becomes vital since they are subjected to electrical currents and high temperatures, which affect their ageing. Estimating the component's ageing rate close to the end of its lifetime is the motivation behind our project. Knowing the ageing rate and life expectancy, we can possibly better utilize and re-utilize existing power components and their parts. In return, we could achieve better material utilization, reduce costs, and improve sustainability designs, contributing to the circular industry development of power system components. Monitoring the thermal distribution and the degradation of the insulation materials informs the estimation of the components' health state. Moreover, further study of the employed paper material of their insulation system can lead to a deeper understanding of its thermal characterization and a possible consequent improvement. Our study aims to create a model that couples the physical equations that govern the deterioration of the insulation systems of power components with modern machine learning algorithms.  As the data is limited and complex in the field of components' ageing, Physics-Informed Neural Networks (PINNs) can help to overcome the problem. PINNs exploit the prior knowledge stored in partial differential equations (PDEs) or ordinary differential equations (ODEs) modelling the involved systems. This prior knowledge becomes a regularization agent, constraining the space of available solutions and consequently reducing the training data needed.  This thesis is divided into two parts: the first focuses on the insulation system of power transformers, and the second is an exploration of the paper material concentrating on cellulose nanofibrils (CNFs) classification. The first part includes modelling the thermal distribution and the degradation of the cellulose inside the power transformer. The deterioration of one of the two systems can lead to severe consequences for the other. Both abilities of PINNs to approximate the solution of the equations and to find the parameters that best describe the data are explored. The second part could be conceived as a standalone; however, it leads to a further understanding of the paper material. Several CNFs materials and concentrations are presented, and this thesis proposes a basic unsupervised learning using clustering algorithms like k-means and Gaussian Mixture Models (GMMs) for their classification. / Ett kraftsystem består av många kritiska komponenter som är nödvändiga för att leverera el från producenter till konsumenter. Att övervaka livslängden på kraftsystemets komponenter är avgörande eftersom de utsätts för elektriska strömmar och höga temperaturer som påverkar deras åldrande. Att uppskatta komponentens åldringshastighet nära slutet av dess livslängd är motivationen bakom vårt projekt. Genom att känna till åldringshastigheten och den förväntade livslängden kan vi eventuellt utnyttja och återanvända befintliga kraftkomponenter och deras delar   bättre. I gengäld kan vi uppnå bättre materialutnyttjande, minska kostnaderna och förbättra hållbarhetsdesignen vilket bidrar till den cirkulära industriutvecklingen av kraftsystemskomponenter. Övervakning av värmefördelningen och nedbrytningen av isoleringsmaterialen indikerar komponenternas hälsotillstånd. Dessutom kan ytterligare studier av pappersmaterial i kraftkomponenternas isoleringssystem leda till en djupare förståelse av dess termiska karaktärisering och en möjlig förbättring.  Vår studie syftar till att skapa en modell som kombinerar de fysiska ekvationer som styr försämringen av isoleringssystemen i kraftkomponenter med moderna algoritmer för maskininlärning. Eftersom datan är begränsad och komplex när det gäller komponenters åldrande kan  fysikinformerade neurala nätverk (PINNs) hjälpa till att lösa problemet. PINNs utnyttjar den förkunskap som finns lagrad i partiella differentialekvationer (PDE) eller ordinära differentialekvationer (ODE) för att modellera system och använder dessa ekvationer för att begränsa antalet tillgängliga lösningar och därmed minska den mängd träningsdata som behövs.  Denna avhandling är uppdelad i två delar: den första fokuserar på krafttransformatorers isoleringssystem, och den andra är en undersökning av pappersmaterialet som används med fokus på klassificering av cellulosananofibriller (CNF). Den första delen omfattar modellering av värmefördelningen och nedbrytningen av cellulosan inuti krafttransformatorn. En försämring av ett av de två systemen kan leda till allvarliga konsekvenser för det andra. Både PINNs förmåga att approximera lösningen av ekvationerna och att hitta de parametrar som bäst beskriver datan undersöks. Den andra delen skulle kunna ses som en fristående del, men den leder till en utökad förståelse av själva pappersmaterialet. Flera CNF-material och koncentrationer presenteras och denna avhandling föreslår en simpel oövervakad inlärning med klusteralgoritmer som k-means och Gaussian Mixture Models (GMMs) för deras klassificering. / <p>QC 20231010</p>

Hydropower is an important energy source since it is fossil-free, renewable, and controllable. Characteristics that become especially important as the reliance on intermittent energy sources increases. However, the dams for the hydropower plants are also associated with large risks as a dam failure could have fatal consequences. Dams are therefore monitored by several sensors, to follow and evaluate any changes in the dam. One of the most important dam surveillance measurements is seepage since it can examine internal erosion. Seepage is affected by several different parameters such as reservoir water level, temperature, and precipitation. Studies also indicate the existence of a time lag between the reservoir water level and the seepage flow, meaning that when there is a change in the reservoir level there is a delay before these changes are reflected in the seepage behaviour. Recent years have seen increased use of AI in dam monitoring, enabling more dynamic warning systems.  This master’s thesis aims to develop a model for dynamic warning signals by predicting seepage using reservoir water level, temperature, and precipitation. Furthermore, a snowmelt variable was introduced to account for the impact of increased water flows during the spring season. The occurrence of a time lag and its possible influence on the model’s performance is also examined. To predict the seepage, three models with different complexity are used – linear regression, support vector regression, and long short-term memory. To investigate the time lag, the linear regression and support vector regression models incorporate a static time lag by shifting the reservoir water level data up to 14 days. The time lag was further investigated using the long short-term memory model as well.  The results show that reservoir water level, temperature, and the snowmelt variable are the combination of input parameters that generate the best results for all three models. Although a one-day time lag between reservoir water level and seepage slightly improved the predictions, the exact duration and nature of the time lag remain unclear. The more complex models (support vector regression and long short-term memory) generated better predictions than the linear regression but performed similarly when evaluated based on the dynamic warning signals. Therefore, linear regression is deemed a suitable model for dynamic warning signals by seepage prediction.

In diesem Beitrag wird exemplarisch anhand des Verschmutzungsmechanismus von Wärmetauschern (Fouling) gezeigt wie datengetriebene Methoden zur Vorhersage des Verschmutzungsgrades und somit zu einem effizienteren Anlagenbetrieb genutzt werden können. Hierzu werden zu Beginn neben der wirtschaftlichen Bedeutung des Foulings die strömungsphysikalischen und thermodynamischen Hintergründe vorgestellt. Danach wird der konkrete Anwendungsfall mit den zur Verfügung stehenden Daten aufgezeigt. Aufgrund der stark limitierten Datenlage wird mit dem Verfahren der segmentierten Regression ein relativ einfacher jedoch robuster Ansatz für ein Vorhersagemodell erarbeitet und diskutiert. / This contribution uses the fouling mechanism of heat exchangers (fouling) as an example to show how data-driven methods can be used to predict the degree of fouling and thus achieve more efficient plant operation. In addition to the economic significance of fouling, the flow-physical and thermodynamic background is presented at the beginning. Then the specific application case with the available data is shown. Due to the very limited data available, a relatively simple but robust approach for a prediction model is developed and discussed using the segmented regression method.