Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2017-04-10T18:58:09Z No. of bitstreams: 2 Dissertação - Diogo Pereira Veloso - 2017.pdf: 3906692 bytes, checksum: 8d731c4df11a717552eb2bbe8b58f5ea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-04-11T12:52:13Z (GMT) No. of bitstreams: 2 Dissertação - Diogo Pereira Veloso - 2017.pdf: 3906692 bytes, checksum: 8d731c4df11a717552eb2bbe8b58f5ea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-04-11T12:52:13Z (GMT). No. of bitstreams: 2 Dissertação - Diogo Pereira Veloso - 2017.pdf: 3906692 bytes, checksum: 8d731c4df11a717552eb2bbe8b58f5ea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-17 / Power transformers are essential equipment in eletric systems. Considered as highly expensive equipment it requires greater attention regarding the occurrence of failures. A failure on equipment can cause serious trouble to the concessionaire responsible for the generation, transmission and distribution of energy. In the present scenario, where legislation has increased the severity with which oversees these failures and demanding a higher efficiency of the same, it is necessary to create appropriate policies to investigas possible equipment failure. These failures can be caused by several factors, among them, the gases dissolved in the oil due to its degeneration. One of the most used techniques for the diagnosis of power transformers from these dissolved gases is the Duval Triangle, a method that has a precision and considerably high reliability. However, for proper planning of maintenance, avoiding a possible sudden withdrawal and supply cut, it is necessary to provide further information, including a quantitative analysis of the causes of failure and a history of the evolution of the operating status of the machine. To this end it is proposed to use a fuzzy logic by the method of Duval to provide an analysis of the transitions between the diagnostic originally presented to enable monitor the level of relevance of each possible cause for the diagnosis equipment allowing perform scheduled and preventive maintenance. The proposed methodology was validated using real data equipment in operation in the brazilian electric system. / Transformadores de potência são equipamentos essenciais em um sistema elétrico. São considerados equipamentos de alto custo que requerem uma maior atenção no que tange a ocorrência de falhas. Uma falha em um equipamento pode causar sérios prejuízos à concessionária responsável pela geração, transmissão e distribuição de energia. No cenário atual, onde a legislação vem sempre aumentado o rigor com que fiscaliza essas falhas e cobrando uma maior eficiência das mesmas, faz-se necessário criar políticas adequadas para investigas possíveis falhas nos equipamentos. Falhas essas que podem ser originadas por diversos fatores, dentre eles, pelos gases dissolvidos no óleo devido a sua degeneração. Um dos métodos mais utilizados para diagnóstico de transformadores de potência é a análise de gases dissolvidos utilizando o Triângulo de Duval, que apresenta uma precisão e confiabilidade consideravelmente altas. No entanto, para um planejamento adequado de manutenção, evitando uma possível retirada brusca do equipamento e corte no fornecimento, é necessário prover mais informações, incluindo uma análise quantitativa das causas das falhas e um histórico da evolução do estado operativo do equipamento. Para tanto propõe-se a utilização de uma lógica fuzzy junto ao método de Duval para fornecer uma análise das transições entre os diagnósticos apresentados originalmente de forma a permitir acompanhar a evolução do nível de pertinência de cada possível causa no diagnóstico do equipamento, possibilitando realizar manutenções programadas e preventivas. A metodologia proposta foi validada utilizando dados reais de equipamentos em operação no sistema slétrico brasileiro.

Examination of the paper insulation and copper stress braiding during stripdown of a number of Current Transformers (FMK type 400kV) has revealed the presence of dark deposits. Copper foils are often interspersed within layers of paper insulation and mineral oil found in transformer windings. The dark deposits were often found in association with these foils, affecting several layers of paper in addition to the layer in contact with the copper foil. This thesis describes the research undertaken to identify these deposits and establish a mechanism for the transportation through the paper layers. Preliminary investigation using scanning electron microscopy (SEM) in conjunction with energy dispersive X-ray analysis (EDX) has shown these dark deposits to be copper based. X-ray photoelectron spectroscopy was used to show that the transport of the copper deposit through the paper insulation was working under the influence of a diffusion controlled process, related to Fick's law. Laboratory studies in support of work designed to eliminate the problem have shown that corrosion of copper occurs in mineral oils containing a trace of oxygen. This corrosion is non protective in character and leads to migration of copper into adjacent layers of paper. It has been shown that the transport of copper through several layers of paper can be measured by XPS and that the concentration from one paper winding to the next declines in accord with Fick's law for non-steady state diffusion. Measurements of surface concentrations by XPS correlate well with measurements made with atomic absorption spectroscopy on solutions of extracts of the contaminated paper. The laboratory measurements have allowed determination of the diffusion coefficients and activation energy for the transport process and thus give a basis for interpretation of the diffusion profiles found in the transformer in terms of time and temperature of operation. The diffusion process is temperature dependant. The results have been used to produce long term prediction curves.

Orientadores: Ernesto Ruppert Filho, Fujio Sato / Tese (outorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T07:31:34Z (GMT). No. of bitstreams: 1 Soares_AlexandreBatistadeJesus.pdf: 4145945 bytes, checksum: 842edd8e3f4d7ce1970318c4cd2dbeb8 (MD5) Previous issue date: 2012 / Resumo: Este trabalho apresenta uma ferramenta computacional denominada Gerenciador de Paralelismo de Alimentadores Radiais de Distribuição (GPARD), que foi desenvolvida para o estudo de manobras de paralelismo entre alimentadores radiais de distribuição de energia elétrica, localizados na mesma subestação ou em subestações diferentes, derivados do mesmo transformador ou de transformadores diferentes. O GPARD e uma ferramenta computacional que contribui para a operacao de sistemas de distribuição auxiliando os operadores do sistema na tomada de decisão para a realização de manobras de paralelismo durante a operacao ou em estudos prévios, fundamentada na analisem gráfica de resultados de simulação e de medidas remotas, resultando em flexibilidade operacional para o sistema e segurança para os consumidores. Tal ferramenta computacional poderá ser bastante utilizada em um ambiente de rede inteligente onde ações rápidas envolvendo medições, transmissão de dados e controle é necessária durante a operacao do sistema elétrico / Abstract: This work presents a computational tool named Management of Parallel Distribution Radial Feeders (GPARD), dedicated to the study of radial distribution feeders parallelism maneuvers, located in the same substation or in different substations, supplied by the same transformer or by different transformers. The GPARD is a computational tool that helps the operation of distribution systems helping system operators in making decisions for the maneuvers of parallelism during the operation or in previous studies, based on graphical analysis of simulation results and remote measurements resulting in operational flexibility to the system and safety to consumers. This computational tool can be utilized in a smart grid environment where fast actions involving measurements, data transmission and control are necessary for the operation of the electric system / Tese (outorado) - Universidade / Energia Eletrica / Doutor em Engenharia Elétrica

Este trabalho tem por meta focalizar a adaptação necessária na metodologia de planejamento atual, norteada segundo uma ótica determinística e conservadora, afim de permitir a elaboração de Programas de Obras de Transmissão/Subtransmissão, condicionados por limitações de teto no montante de investimento a curto e médio prazos. Para tanto, como tema central, busca-se desenvolver uma base conceitual e metodológica, para permitir uma exploração maximizada das possibilidades de carregamento de equipamentos e instalações do sistema de transmissão, contemplando a especificação das ferramentas computacionais necessárias para dar consequência prática à proposta. Considerando o potencial de análise proporcionado por métodos e técnicas de natureza probabilística, aplicados ao planejamento de Sistemas Elétricos, a espinha dorsal da evolução metodológica delineada no texto repousa sobre a avaliação de confiabilidade global e quantificação de riscos operativos. O texto foi estruturado em 8 capítulos onde, a partir de um capítulo introdutório, enfoca-se sequencialmente os temas: * Conceitos e Metodologias para Maximização do Carregamento de Transformadores de Potência. * Conceitos e Metodologias para Maximização do Carregamento de Linhas aéreas de Transmissão. * Impacto das Restrições Financeiras no planejamento e possibilidade de atuação a nível técnico para administrar o conflito entre qualidade de serviço e recursos limitados. * Conceitos e Metodologias para relaxação dos Critérios limitantes para o carregamento máximo de componentes do sistema, em ambiente de severas restrições financeiras, no horizonte de curto prazo. * Resultados obtidos em aplicações concretas a situações do Setor Elétrico, visando caracterizar o alcance do ferramental preconizado nos capítulos anteriores. * Critérios e Procedimentos para Padronização de Subestações e Linhas de Transmissão , coerentes com os conceitos de maximização de carregamento introduzidos nos dois primeiros capítulos. Ao final do texto, incluiu-se um capítulo de \"Conclusões\", com o objetivo de sumarizar os aspectos mais relevantes abordados no texto, assim como indicar linhas de ação e novos desenvolvimentos, necessários para permitir a efetivação prática da metodologia proposta. / An important problem posed to power system planners in developing countries is now becoming commonplace, even in developed countries, and concerns on how to fit an expansion plan into a tight budget, with the least reduction in reliability levels. This problem is known as power system planning under financial constraints and a series of methods is now available to deal with it. So, this work addresses the necessary adjustments in order to permit the Transmission / Sub-transmission Expansion Program establishment when there are investments constraints in a medium/short term range. The main emphasis is on the development of a methodological and conceptual framework, aiming at a maximizes exploration of the loading capability of transformers and transmission lines of the power network. In this context, a new methodological framework for transmission system planning in a financial constrained environment is presented. The proposed approach combines transmission projects priority evaluation and ranking as well as criteria flexibilization, being particularly suitable for short-term planning. The problem to be faced is to operate the system after a project postponement, while the service quality is kept so adequate as it is possible. One of the main issues to be focused is the transmission system uprating since a maximized equipment utilization can contribute to avoid a more significant restriction to the costumers. Therefore, a detailed description of the models for transmission lines and transformers loading limits assessment is presented, with emphasis on the main concepts and the application of probabilistic tools to circumvent the inherent conservatism of deterministic methods. A summary of the contents is presented in what follows: * Concepts and Methodology for Transformers loading maximization. * Concepts and Methodology for Transmission Lines loading maximization. * Financial Restrictions impacts on Expansion Planning Process and the possibilities to manager the conflict between service quality and investment constraints. * Concepts and Methodologies to the relaxation of Criteria that limits the loading capability of transmission lines, aiming at short term Planning under severe financial restrictions, * Obtained results in concrete applications of the proposed methodology and computational tools to Brazilian Electric Power System. * Methods and Criteria to standardization of Substations and Transmission Lines, according the main issues formerly introduced. * Conclusion and Future Research.

This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.

In a world of globalization, where offers continues to grow, the ability to direct people to their specific need is essential. After being key differentiating factors for Netflix and Amazon, Recommender Systems in general are no where near a downfall. Still, one downside of the basic recommender systems is that they are mainly based on indirect feedback (our behaviour, mainly form the past) as opposed to explicit demand at a specific time. Recent development in machine learning brings us closer to the possibility for a user to express it’s specific needs in natural language and get a machine generated reply. This is what Conversational Recommendation is about. Conversational recommendation encapsulates several machine learning sub-tasks. In this work, we focus our study on methods for the task of item (in our case, movie) recommendation from conversation. To explore this setting, we use, adapt and extend state of the art transformer based neural language modeling techniques to the task of recommendation from dialogue. We study the performance of different methods using the ReDial dataset [24], a conversational- recommendation dataset for movies. We also make use of a knowledge base of movies and measure their ability to improve performance for cold-start users, items, and/or both. This master thesis is divided as follows. First, we review all the basics concepts and the previous work necessary to to this lecture. When then dive deep into the specifics our data management, the different models we tested, the set-up of our experiments and the results we got. Follows the original a paper we submitted at RecSys 2020 Conference. Note that their is a minor inconsistency since throughout the thesis, we use v to represent items but in the paper, we used i. Overall, we find that pre-trained transformer models outperform baselines even if the baselines have access to the user preferences manually extracted from their utterances. / Dans un monde de mondialisation, où les offres continuent de croître, la capacité de référer les gens vers leurs besoins spécifiques est essentiel. Après avoir été un facteur de différenciation clé pour Netflix et Amazon, les systèmes de recommandation en général ne sont pas près de disparaître. Néanmoins, l’un des leurs inconvénients est qu’ils sont principalement basés sur des informations indirects (notre comportement, principalement du passé) par opposition à une demande explicite à un moment donné. Le développement récent de l’apprentissage automatique nous rapproche de la possibilité d’exprimer nos besoins spécifiques en langage naturel et d’obtenir une réponse générée par la machine. C’est ce en quoi consiste la recommandation conversationnelle. La recommandation conversationnelle englobe plusieurs sous-tâches d’apprentissage automatique. Dans ce travail, nous concentrons notre étude sur les méthodes entourant la tâche de recommandation d’item (dans notre cas, un film) à partir d’un dialogue. Pour explorer cette avenue, nous adaptons et étendons les techniques de modélisation du langage basées sur les transformeurs à la tâche de recommandation à partir du dialogue. Nous étudions les performances de différentes méthodes à l’aide de l’ensemble de données ReDial [24], un ensemble de données de recommandation conversationnelle pour les films. Nous utilisons également une base de connaissances de films et mesurons sa capacité à améliorer les performances lorsque peu d’information sur les utilisateurs/éléments est disponible. Ce mémoire par article est divisé comme suit. Tout d’abord, nous passons en revue tous les concepts de base et les travaux antérieurs nécessaires à cette lecture. Ensuite, nous élaborons les spécificités de notre gestion des données, les différents modèles que nous avons testés, la mise en place de nos expériences et les résultats que nous avons obtenus. Suit l’article original que nous avons soumis à la conférence RecSys 2020. Notez qu’il y a une incohérence mineure puisque tout au long du mémoire, nous utilisons v pour représenter les éléments mais dans l’article, nous avons utilisé i. Dans l’ensemble, nous constatons que les modèles de transformeurs pré-entraînés surpassent les modèles de bases même si les modèles de base ont accès aux préférences utilisateur extraites manuellement des dialogues.

[pt] Transformadores de guia de onda são amplamente empregados no projeto de componentes em onda guiada e são encontrados em praticamente todas as cadeias alimentadoras de antenas e demais estruturas de onda guiada na faixa de microondas. Embora a teoria de transformadores seja conhecida, os requisitos de ordem sistêmica têm levado os projetos de transformadores de guia de onda ao seu limite. Para tal nível de exigência, e considerando o número de variáveis no projeto de transformadores, técnicas numéricas de análise (tais como FDTD e expansão modal dentre outros), e otimização têm sido obrigatoriamente empregadas. Por outro lado, o número de variáveis de um transformador, acaba sendo um processo de alto consumo de tempo computacional, incoerente com o porte e objetivo de custo desses transformadores. Este trabalho propõe uma possibilidade alternativa para a análise mais rápida para essas estruturas, através do emprego de formulações fechadas derivadas de métodos varacionais. Um modelo heurístico é proposto para o caso de descontinuidades em dois planos, sejam para o caso de descontinuidades homogêneas ou para não-homogêneas. / [en] Waveguide transformers are widely used on antenna’s feeder chains and other microwave devices. Although the theory of quarter wavelength transformers is well known, the current electrical performance of such microwave devices has been pushing the waveguide transformers design to its limit. For attending such level of requirements, and considering the number of existing variables on a waveguide transformer design, very accurate numerical techniques has been applied on its analyses, (such as FDTD, mode matching, etc), and optimization techniques as well. On the other hand, such numerical techniques are very memory and/or CPU/time consuming, which do not match with the cost objective of those simple concept transformers. This work proposes an alternative technique, based on close-form models derived from varational theory. A heuristic model is also proposed for attending the two plane transformer case, which can be easily applied for both homogeneous and inhomogeneous structures. Keywords Waveguide;

<div>Textual emotion classification is a task in affective AI that branches from sentiment analysis and focuses on identifying emotions expressed in a given text excerpt. </div><div>It has a wide variety of applications that improve human-computer interactions, particularly to empower computers to understand subjective human language better. </div><div>Significant research has been done on this task, but very little of that research leverages one of the most emotion-bearing symbols we have used in modern communication: Emojis.</div><div>In this thesis, we propose several transformer-based models for emotion classification that processes emojis as input tokens and leverages pretrained models and uses them</div><div>, a model that processes Emojis as textual inputs and leverages DeepMoji to generate affective feature vectors used as reference when aggregating different modalities of text encoding. </div><div>To evaluate ReferEmo, we experimented on the SemEval 2018 and GoEmotions datasets, two benchmark datasets for emotion classification, and achieved competitive performance compared to state-of-the-art models tested on these datasets. Notably, our model performs better on the underrepresented classes of each dataset.</div>

In the digital age where video content is abundant, this thesis investigates the efficient adaptation of an existing video-language model (VLM) to new data. The research leverages CLIP, a robust language-vision model, for various video-related tasks including video retrieval. The study explores using pre-trained VLMs to extract video embeddings without the need for extensive retraining. The effectiveness of a smaller model using aggregation is compared with larger models and the application of logistic regression for few-shot learning on video embeddings is examined. The aggregation was done using both non-learning through mean-pooling and also by utilizing a transformer. The video-retrieval models were evaluated on the ActivityNet Captions dataset which contains long videos with dense descriptions while the linear probes were evaluated on ActivityNet200 a video classification dataset.  The study's findings suggest that most models improved when additional frames were employed through aggregation, leading to improved performance. A model trained with fewer frames was able to surpass those trained with two or four times more frames by instead using aggregation. The incorporation of patch dropout and the freezing of embeddings proved advantageous by enhancing performance and conserving training resources. Furthermore, using a linear probe showed that the extracted features were of high quality requiring only 2-4 samples per class to match the zero-shot performance.

This thesis investigates how various driver signals, signals that a truck driver can interact with, influences fuel consumption and what are the optimal values of these signals in various traffic conditions. More specifically, the objective is to estimate good driver behavior in various traffic conditions and compare bad driver behavior in similar situations to see how performing a specific driver action, changing a driver signal from the bad driver value to the corresponding good driver value impacts the fuel consumption. The result is an AI-based algorithm that utilizes the transformer model architecture to estimate good driver behavior, based on environmental describing signals, as well as fuel consumption. Utilizing these, causal inference is used to estimate how much fuel can be saved by switching a driver signal from a bad driver value to a good driver value.