Orientador: Antonio José Ranalli Nardy / Banca: Leila Soares Marques / Banca: Marcos Aurélio Farias de Oliveira / A investigação petrográfica e geoquímica das rochas intrusivas associadas à Província Magmática do Paraná (PMP), na porção leste da Bacia do Paraná, no Estado de São Paulo, mostraram que as rochas estudadas são constituídas essencialmente por plagioclásio, augita, minerais opacos e pigeonita, caracterizando diabásio com texturas predominantemente intergranular, subofítica e ofítica. Dados geoquímicos indicaram que as rochas apresentam natureza básica, afinidade toleítica e podem ser classificadas como pertencentes ao grupo de alto titânio (ATi) da PMP. Além disso, as diferenças geoquímicas também indicam que as intrusivas pertencem aos subgrupos (magmas-tipo) Paranapanema (PAR) e Pitanga (PIT), sendo que a distribuição destes magmas pela área estudada não se faz ao acaso. O tipo PAR ocorre nos sills da região de Campinas, enquanto que PIT nas regiões de Cajuru, Leme, e Iracemápolis. Para comparação geoquímica, os derrames próximos também foram investigados, onde se observou que as amostras coletadas nas regiões de Franca, Igarapava e Rifaina em São Paulo, e São Sebastião do Paraíso, em Minas Gerais, são pertencentes ao magma-tipo Urubici (URU), já aquelas da região de Brotas e Ribeirão Preto são do tipo PIT. Contudo, as concentrações de ETRs, para as amostras representativas dos três magmas-tipo, mostraram que, embora as rochas intrusivas (PAR e PIT) e derrames (URU) possam ter uma mesma fonte mantélica, foram submetidas a processos de evolução magmática distintos. / Petrographic and geochemical investigations of the intrusive rocks related to the Magmatic Paraná Province (PMP), in the east portion of Paraná Basin, São Paulo State, have showed that the studied rocks are constituted mainly by plagioclase, augite, magnetite and pigeonite. That characterize intergranular, subophitic and ophitic diabases. Geochemical data have indicated that rocks are basic, with toleiitic affinity and can be included to the high titanium (HTi) of the PMP group. Moreover, the geochemical differences point out that the intrusive rocks belong to Paranapanema (PAR) and Pitanga (PIT) sub-groups (magmas-type), and the magma type distribution is well sectioned. The Paranapanema Magma type occurs like sills, in the neighborhood of Campinas, meanwhile Pitanga Type occurs in Cajuru, Leme, and Iracemápolis neighborhood. In order to compare, the nearby flows were also investigated, and was observed that samples collected in Franca, Rifaina e Igarapava in São Paulo, and São Sebastião do Paraíso, in Minas Gerais, neighborhoods belong to the Urubici magma-type (URU). Magmas from Brotas and Ribeirão Preto are PIT type. The concentrations of ETRs to the representative samples of three magmas-type have showed that intrusivas rocks (PAR and PIT) and lavas (URU) possibly were submitted to magmatic evolution different processes. / Mestre

Context: In recent years, households have been increasing energy consumption to very high levels, where it is no longer sustainable. There has been a dire need to find a way to use energy more sustainably due to the increase in the usage of energy consumption. One of the main causes of this unsustainable usage of energy consumption is that the user is not much acquainted with the energy consumed by the smart appliances (dishwasher, refrigerator, washing machine etc) in their households. By letting the household users know the energy usage consumed by the smart appliances. For the energy analytics companies, they must analyze the energy consumed by the smart appliances present in a house. To achieve this Kelly et. al. [7] have performed the task of energy disaggregation by using deep neural networks and producing good results. Zhang et. al. [7] has gone even a step further in improving the deep neural networks proposed by Kelly et. al., The task was performed by Non-intrusive load monitoring (NILM) technique. Objectives: The thesis aims to assess the performance of the deep neural networks which are proposed by Kelly et.al. [7], and Zhang et. al. [8]. We use deep neural networks for disaggregation of the dishwasher energy consumption, in the presence of vampire loads such as electric heaters, in a Swedish household setting. We also try to identify the training time of the proposed deep neural networks.  Methods: An intensive literature review is done to identify state-of-the-art deep neural network techniques used for energy disaggregation.  All the experiments are being performed on the dataset provided by the energy analytics company Eliq AB. The data is collected from 4 households in Sweden. All the households consist of vampire loads, an electrical heater, whose power consumption can be seen in the main power sensor. A separate smart plug is used to collect the dishwasher power consumption data. Each algorithm training is done on 2 houses with data provided by all the houses except two, which will be used for testing. The metrics used for analyzing the algorithms are Accuracy, Recall, Precision, Root mean square error (RMSE), and F1 measure. These software metrics would help us identify the best suitable algorithm for the disaggregation of dishwasher energy in our case.  Results: The results from our study have proved that Gated recurrent unit (GRU) performed best when compared to the other neural networks in our study like Simple recurrent neural network (SRN), Convolutional Neural Network (CNN), Long short-Term memory (LSTM) and Recurrent convolution neural network (RCNN). The Accuracy, RMSE and the F1 score of the GRU algorithm are higher when compared with the other algorithms. Also, if the user does not consider F1 score and RMSE as an evaluation metric and considers training time as his or her metric, then Simple recurrent neural network outperforms all the other neural nets with an average training time of 19.34 minutes.

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Intrusive thoughts are aversive, private thoughts that are unwanted but intrude into consciousness, and are a ubiquitous phenomenon that approximately 93% of the population experiences (Radomsky et. al., 2014). Obsessional thoughts are a key etiological component of obsessive-compulsive disorder (OCD). Cognitive behavioral models of OCD conceptualize intrusive thoughts and obsessive thoughts as the same phenomenon occurring on a spectrum, with obsessional thoughts being a particular type of intrusion that is integral to the development and maintenance of OCD (Moulding, 2014). However, there is little evidence to demonstrate this relationship. This study examined intrusive thoughts across stratified groups based on intrusion frequency using ecological momentary assessment. This exploratory study examined potential idiographic differences in reported experiences of people ranging from low to high levels of intrusive thought frequency. Personalized contemporaneous networks were constructed from participant data and examined for differences in topography, measures of centrality, and magnitude of relationships between nodes. These networks are visually distinct, providing a glimpse into a wide variety of ways in which participants experience and relate to their intrusive thoughts.

The aim of the research was to develop the non-intrusive physiological measure of using human facial skin temperature change as an indicator of mental workload. The forehead and nose temperature were obtained via thermography from the participants who drove in a simulator driving environment and/or in instrumented car experiments. The NASA TLX and the Modified Cooper-Harper metrics were adopted to assess the subjective workload for the validation of the physiological measure. Three driving experiments were conducted in order to acquire the physiological response and the workload score for the performed tasks. Forehead temperature was very stable throughout the experiments. Nose temperature dropped significantly after the experimental drive for all conditions in simulator test. Experiment 1 (NASA TLX Group: N=10; MCH Group: N=14) used simulator driving with different terrains as loading tasks. Neither the significant difference of the subjective workload nor the temperature drop was detected between different terrain conditions. In experiment 2 (N=33), mental workload was increased in a controlled manner by the introduction of mental arithmetic tests to the primary simulated drive. The mental arithmetic test conditions provoked a significantly greater nose temperature drop and also a higher perceived workload than the conditions without the arithmetic test. A weak correlation between the nose temperature drop and the subjective workload metric was yielded from the experiments. In Experiment 3 (N=13), facial temperature response and subjective workload score were compared between the simulator test and on-road driving. Driving in the simulator resulted in higher subjective workload and greater nose temperature drop than in real-car driving. When participants perceived a higher workload for a task, their nose temperature exhibited a greater drop. A significant correlation between the nose temperature change and the subjective workload score was found. Actual or potential applications of this research include real-time and unobtrusive mental workload assessment for human-system interaction development.

No description available.

In recent decades, computational models have become critical to how engineers and mathematicians understand nature; as a result they have become an integral part of the design process in most engineering disciplines. Moore's law anticipates computing power doubling every two years; a prediction that has historically been realized. As modern computing power increases, problems that were previously too complex to solve by hand or by previous computing abilities become tractable. This has resulted in the development of increasingly complex computational models simulating increasingly complex dynamics. Unfortunately, this has also resulted in increased challenges in fields related to model development, such as model validation and model based control, which are needed to make models useful in the real world. Much of the validation literature to date has focused on spatial and spatiotemporal simulations; validation approaches are well defined for such models. For most time series simulations, simulated and experimental trajectories can be directly compared negating the need for specialized validation tools. In the study of some ship motion behavior, chaos exists, which results in chaotic time series simulations. This presents novel challenges for validation; direct comparison may not be the most apt approach. For these applications, there is a need to develop appropriate metrics for model validation. A major thrust of the current work seeks to develop a set of validation metrics for such chaotic time series data. A complementary but separate portion of work investigates Non-Intrusive Polynomial Chaos as an approach to reduce the computational costs associated with uncertainty analysis and other stochastic investigations into the behavior of nonlinear, chaotic models. A final major thrust of this work focuses on contributing to the control of nonlinear marine systems, specifically the autonomous recovery of an unmanned surface vehicle utilizing motion prediction information. The same complexity and chaotic nature that makes the validation of ship motion models difficult can also make the development of reliable, robust controllers difficult as well. This body of work seeks to address several facets of this broad need that has developed due to our increased computational abilities by providing validation metrics and robust control laws. / Ph. D.

Cette thèse présente une méthode pour désagréger les appareils électriques dans le profil des bâtiments résidentiels de charge. Au cours des dernières années, la surveillance de l’énergie a obtenu beaucoup de popularité dans un environnement privé et industriel. Avec des algorithmes de la désagrégation, les données mesurées à partir de soi-disant compteurs intelligents peuvent être utilisés pour fournir de plus amples informations de la consommation d’énergie. Une méthode pour recevoir ces données est appelé non-intrusifs charge identification. La majeure partie de la thèse peut être divisée en trois parties. Dans un premier temps, un système de mesure propre a été développé et vérifié. Avec ce système, les ensembles de données réelles peuvent être générés pour le développement et la vérification des algorithmes de désagrégation. La deuxième partie décrit le développement d’un détecteur de flanc. Différentes méthodes sont présentées et évaluées, avec lequel les temps de commutation des appareils peuvent être détectés dans le profil de la charge. La dernière partie décrit un procédé de classification. Différents critères sont utilisés pour la classification. Le classificateur reconnaît et étiquette les appareils individuels de la courbe de charge. Pour les classifications différentes structures de réseaux de neurones artificiels sont comparés. / This thesis presents a method to disaggregate electrical appliances in the load profile of residential buildings. In recent years, energy monitoring has obtained significantly popularity in private and industrial environment. With algorithms of the disaggregation, the measured data from so-called smart meters can be used to provide more information of the energy usage. One method to receive these data is called non-intrusive appliance load monitoring.The main part of the thesis can be divided into three parts. At first, an own measurement system was developed and verified. With that system, real data sets can be generated for the development and verification of the disaggregation algorithms. The second part describes the development of an event detector. Different methods are presented and evaluated, with which the switching times of the appliances can be detected in the load profile. The last part describes a classification method. Different features are used for the classification. The classifier recognizes and labels the individual appliances in the load profile. For the classification different structures of artificial neural network (ANN) are compared.

Intrusive memories are memories for negative autobiographical events that come to mind without being deliberately recalled (Hackmann, Ehlers, Speckens, & Clark, 2004). Previous research has demonstrated that intrusive memories, and negative reactions to intrusive memories, are an important feature of depression (Starr & Moulds, 2006; Williams & Moulds, 2008b) and contribute to the maintenance of depression longitudinally (Newby & Moulds, 2011c). It has also been established that intrusive memory experience is related to individual differences in cognitive control, specifically in proactive interference resolution (Verwoerd, Wessel, & de Jong, 2009; Verwoerd, Wessel, de Jong, Nieuwenhuis, & Huntjens, 2011). The present thesis aimed to extend upon these existing findings, using the Dual Mechanism of Control theory, which distinguishes between proactive and reactive modes of control (Braver, Gray, & Burgess, 2007). Furthermore, in view of some recent ambiguity of the defining feature of an intrusive memory, as compared to a negative involuntary memory more generally (Kvavilashvili, 2014; Moulds & Krans, 2015), another goal of the thesis was to incorporate a wider focus of involuntary memories, rather than focusing only on traditionally studied intrusive memories. Participants from the student population and local community were tested. Overall, results provide some evidence for a negative involuntary memory related deficit in proactive control, on both a classic and an emotional version of the AX-Continuous Performance Task (AX-CPT). Conversely, there was no link between intrusive or involuntary memory experience and reactive control. A brief mindfulness and self-compassion based intervention, designed to reduce negative reactions to intrusive memories, was also tested. The intervention was successful in reducing intrusive-memory distress, and recommendations for the future development of the intervention are presented.

The dissertation thesis deals with the application of absorption methods for the separation of gaseous pollutants from polluted gaseous mixtures using a pilot plant scrubber. The efficiency of the gaseous pollutant removal was determined based on the hydrodynamic distribution of fluids inside the spray chamber. A TF-28 150 spiral nozzle, which was used to spray the absorption liquid, was experimentally characterized. Specifically, the pressure impact pattern of the liquid produced by the nozzle was investigated using intrusive methods and the effective spray angles, the modes of primary and secondary atomization, the droplet size and liquid velocity distributions were determined using non-intrusive optical methods. Numerical simulations of flow of model gas phase inside the sprinkler head and spraying of the liquid through the spiral nozzle were also carried out. The obtained data can be applied to estimate the interface area and together with the determination of the coefficient of total mass transfer thus define the kinetics of chemisorption for a given absorbent/absorbate combination.