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The diploma thesis deals with the design of the connection of the Svratecko area with the distribution network of the company E.ON Distribuce, a.s.. The theoretical part describes the energy legislation and the individual standards on which the thesis is based. It also describes the concept of a 22 kV high voltage network. The practical part of the thesis is focused on the creation of line models in the program E-vlivy, which will supply the Svratecko area and the model of the monitored area. Outputs from the thesis will be used by E.ON Distribuce, a.s. to evaluate the connection of the Svratecko area.

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<div> <div> <div> <p>As the demand and complexity of air traffic increase, it becomes crucial to maintain the safety and efficiency of the operations in airspaces, which, however, could lead to an increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making processes. Although terminal airspaces are highly structured with the flight procedures such as standard terminal arrival routes and standard instrument departures, the aircraft are frequently instructed to deviate from such procedures by ATCs to accommodate given traffic situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to meet scheduling requirements. Such deviation, called vectoring, could even increase the delays and workload of ATCs. This thesis focuses on developing a framework for trajectory pattern identification and classification that can provide ATCs, in vectored airspace, with real-time information of which possible vectoring pattern a new incoming aircraft could take so that such delays and workload could be reduced. This thesis consists of two parts, trajectory pattern identification and trajectory pattern classification. </p> <p>In the first part, a framework for trajectory pattern identification is proposed based on agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean distance as the dissimilarity measure between trajectories. Binary trees with fixes that are provided in the aeronautical information publication data are proposed in order to catego- rize the trajectory patterns. In the second part, multiple recurrent neural network based binary classification models are trained and utilized at the nodes of the binary trees to compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi- cation framework and the classification models are illustrated with the automatic dependent surveillance-broadcast data that were recorded between January and December 2019 in In- cheon international airport, South Korea . </p> </div> </div> </div>

Les machines à commutation de flux cinq-phases présentent une tolérance aux pannes et une robustesse qui les rendent très intéressantes dans un point de vue de la fiabilité, comme montré dans le premier chapitre.Dans ces travaux de thèse nous avons explorés la possibilité de détecter les défauts qui affectent cette machine par la signature des vibrations générées dans la machine.En utilisant les outils physiques et mathématiques présentés dans le deuxième chapitre, nous avons construit deux modèles multiphysiques, un modèle aux les éléments finis développé dans le troisième chapitre et un modèle analytique, appelé aux réseaux de perméances, dans le quatrième chapitre.Le comportement vibratoire de la machine a été étudié à l'aide de ces deux modèles, en régimesain et en défaut afin de connaitre comment ce comportement est influencé par les grandeurs électriques et magnétiques de la machine.Par ailleurs nous avons étudié la possibilité de détecter et discriminer les différents types de défauts.Le modèle analytique se présente comme un bon estimateur du comportement en défaut de la machine, malgré ses écarts avec la simulation.Dans le cinquième chapitre, les deux modèles multiphysiques ont été validés par des essais expérimentaux et nous avons pu expliquer le comportement en défaut d’un point de vue mécanique plutôt que magnétique.Enfin, dans le sixième chapitre, nous avons utilisé les deux modèles pour étudier le comportement en défaut de la machine, à des vitesses au-dessus de la limite expérimentale (3100 tr/min). / Five-phase flux switching machines have a fault tolerance and robustness that makes them very interesting from the point of view of reliability, as shown in chapter one of this work. In our studies we have explored the possibility of detecting faults that affect this type of machine using the signature of stator vibrations.Using the physical and mathematical tools presented in chapter two, we improved two multyphisics models, one based on finite elements method that it's presented in chapter three and the seconde one analitycal model, called permeance networks, in chapter four. The vibratory behavior of the machine was studied using these two models, under healthy and faulty conditions, in order to know how this behavior is influenced by the electrical and magnetic magnitudes of the machine. In addition, we have studied the possibility of detecting and discriminating different types of faults. Analytical model is a good estimator of fault behavior of the machine, despite its differences with the simulation.In chapter five, the two multiphysical models have been validated by experimental tests and we have been able to explain fault behavior by mechanical origin rather than magnetic origin.Finally, in chapter six, we used both models to study the fault behavior of the machine, at speeds above the experimental limit (3100 rpm).

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19794号 / 人博第765号 / 新制||人||184(附属図書館) / 27||人博||765(吉田南総合図書館) / 32830 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 神﨑 素樹, 教授 森谷 敏夫, 教授 石原 昭彦 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

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A novel method of analysis of macromolecules has been worked upon through this research. In an effort to understand the dynamics of macromolecules and to further our knowledge, pertaining specifically to the low frequency domain and also to elucidate certain important biological functions associated with it, a theoretical technique of chemical information based Normal Mode Analysis has been developed. These simulations render users with the ability to generate animations of modeshapes as well as key insight on the associated vibration frequencies. Harmonic analysis using atomistic details is performed taking into account appropriate values of masses of constituent atoms of a given macromolecule. In order to substantiate the applicability of such a technique, simple linear molecules were first worked upon. Subsequently, this technique has been applied to relatively more complex structures like amino acids, namely Cysteine. Consequently, this approach was extended to large macromolecules like Lactoferrin. Animations of modeshapes from simulations suggest a one to one correspondence with other computational techniques reported by other researchers. Computed β-factors are also in close agreement with the experimentally observed values of the same. Hence, as opposed to a simple Cα coarse grained model, our method with right masses and reasonable force fields yields not only the correct modeshapes but also provides us with useful information on wavenumbers that can be used to extract useful information about the frequency domain. Moreover, as opposed to conventional Molecular Dynamics’ simulations and Laser spectroscopy, the proposed methodology is significantly faster, cheaper and efficient.

Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.

<p dir="ltr">The research focuses on improving natural language processing (NLP) models by integrating the hierarchical structure of language, which is essential for understanding and generating human language. The main contributions of the study are:</p><ol><li><b>Hierarchical RNN Model:</b> Development of a deep Recurrent Neural Network model that captures both explicit and implicit hierarchical structures in language.</li><li><b>Hierarchical Attention Mechanism:</b> Use of a multi-level attention mechanism to help the model prioritize relevant information at different levels of the hierarchy.</li><li><b>Latent Indicators and Efficient Training:</b> Integration of latent indicators using the Expectation-Maximization algorithm and reduction of computational complexity with Bootstrap sampling and layered training strategies.</li><li><b>Sequence-to-Sequence Model for Translation:</b> Extension of the model to translation tasks, including a novel pre-training technique and a hierarchical decoding strategy to stabilize latent indicators during generation.</li></ol><p dir="ltr">The study claims enhanced performance in various NLP tasks with results comparable to larger models, with the added benefit of increased interpretability.</p>