Made available in DSpace on 2014-12-17T14:56:08Z (GMT). No. of bitstreams: 1 AlynneCSQ_DISSERT.pdf: 5674522 bytes, checksum: 276b6f887cbd025afcc9fc319a3dbc2e (MD5) Previous issue date: 2012-09-24 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The opening of the Brazilian market of electricity and competitiveness between companies in the energy sector make the search for useful information and tools that will assist in decision making activities, increase by the concessionaires. An important source of knowledge for these utilities is the time series of energy demand. The identification of behavior patterns and description of events become important for the planning execution, seeking improvements in service quality and financial benefits. This dissertation presents a methodology based on mining and representation tools of time series, in order to extract knowledge that relate series of electricity demand in various substations connected of a electric utility. The method exploits the relationship of duration, coincidence and partial order of events in multi-dimensionals time series. To represent the knowledge is used the language proposed by M?rchen (2005) called Time Series Knowledge Representation (TSKR). We conducted a case study using time series of energy demand of 8 substations interconnected by a ring system, which feeds the metropolitan area of Goi?nia-GO, provided by CELG (Companhia Energ?tica de Goi?s), responsible for the service of power distribution in the state of Goi?s (Brazil). Using the proposed methodology were extracted three levels of knowledge that describe the behavior of the system studied, representing clearly the system dynamics, becoming a tool to assist planning activities / A abertura do mercado brasileiro de energia el?trica e a competitividade entre as empresas do setor energ?tico fazem com que a busca por informa??es ?teis e ferramentas que venham a auxiliar na tomada de decis?es, aumente por parte das concession?rias. Uma importante fonte de conhecimento para essas concession?rias s?o as s?ries temporais de consumo de energia. A identifica??o de padr?es de comportamento e a descri??o de eventos se tornam necess?rias para a execu??o de atividades de planejamento, buscando melhorias na qualidade de atendimento e vantagens financeiras. A presente disserta??o apresenta uma metodologia baseada em ferramentas de minera??o e representa??o de s?ries temporais, com o objetivo de extrair conhecimento que relacionam s?ries de demanda de energia el?trica de diversas subesta??es interligadas de uma concession?ria. O m?todo utilizado explora rela??es de dura??o, coincid?ncia e ordem parcial de eventos em s?ries temporais multidimensionais. Para a representa??o do conhecimento ser? utilizada a linguagem proposta por M?rchen (2005) chamada Time Series Knowledge Representation (TSKR). Foi realizado um estudo de caso usando s?ries temporais de demanda de energia de 8 subesta??es interligadas por um sistema em anel, que alimenta a regi?o metropolitana de Goi?nia-GO, cedidas pela CELG (Companhia Energ?tica de Goi?s), permission?ria do servi?o de distribui??o de energia no estado de Goi?s (Brasil). Utilizando a metodologia proposta foram extra?dos tr?s n?veis de conhecimento que descrevem o comportamento do sistema estudado, representando a din?mica do sistema de forma clara, constituindo-se em uma ferramenta para auxiliar em atividades de planejamento

Made available in DSpace on 2014-12-17T14:56:17Z (GMT). No. of bitstreams: 1 EdsonAMRB_DISSERT.pdf: 7502344 bytes, checksum: 78d70443ae2fd9033fe78b23c5cbd811 (MD5) Previous issue date: 2013-07-30 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Complex network analysis is a powerful tool into research of complex systems like brain networks. This work aims to describe the topological changes in neural functional connectivity networks of neocortex and hippocampus during slow-wave sleep (SWS) in animals submited to a novel experience exposure. Slow-wave sleep is an important sleep stage where occurs reverberations of electrical activities patterns of wakeness, playing a fundamental role in memory consolidation. Although its importance there s a lack of studies that characterize the topological dynamical of functional connectivity networks during that sleep stage. There s no studies that describe the topological modifications that novel exposure leads to this networks. We have observed that several topological properties have been modified after novel exposure and this modification remains for a long time. Major part of this changes in topological properties by novel exposure are related to fault tolerance / A an?lise da topologia de redes ? uma poderosa ferramenta no estudo de sistemas complexos tal como as redes cerebrais. Este trabalho procura descrever as mudan?as na topologia de redes de conex?o funcional em neur?nios do c?rtex sensorial e do hipocampo durante o sono de ondas lentas (SWS) em animais expostos ? novidade. O sono de ondas lentas ? um importante estado do sono onde h? reverbera??o de padr?es de atividade el?trica ocorridos na vig?lia, tendo com isso papel fundamental na consolida??o de mem?ria. Apesar de sua import?ncia ainda n?o h? estudos que caracterizam a din?mica da topologia de redes de conex?o funcional durante este estado. Tampouco h? estudos que descrevem as modifica??es topol?gicas que a exposi??o ? novidade traz a essas redes. Observamos que v?rias propriedades topol?gicas s?o modificadas ap?s a exposi??o ? novidade e que tais modifica??es se mant?m por um longo per?odo de tempo. A maior parte das propriedades modificadas pela exposi??o ? novidade est? relacionada ? toler?ncia ? falha

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-07-26T19:48:07Z No. of bitstreams: 1 CARLOS ALBERTO DOS SANTOS_DIS.pdf: 1271475 bytes, checksum: 856ae87ad633d3c772b413816caa43d1 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-08-01T13:39:36Z (GMT) No. of bitstreams: 1 CARLOS ALBERTO DOS SANTOS_DIS.pdf: 1271475 bytes, checksum: 856ae87ad633d3c772b413816caa43d1 (MD5) / Made available in DSpace on 2018-08-01T14:31:21Z (GMT). No. of bitstreams: 1 CARLOS ALBERTO DOS SANTOS_DIS.pdf: 1271475 bytes, checksum: 856ae87ad633d3c772b413816caa43d1 (MD5) Previous issue date: 2018-04-27 / It is known that linguistic processing of corpora demands high computational effort because of the complexity of its algorithms, but despite this, the results reached are better than that generated by the statistical processing, where the computational demand is lower. This dissertation describes a comparative analysis between the process linguistic and statistical of term extraction. Experiments were carried out through four corpora in English idiom, built from scientific papers, on which terms extractions were carried out using the approaches. The resulting terms lists were refined with use of relevance metrics and stop list, and then compared with the reference lists of the corpora across the recall technical. These lists, in its turn, were built from the context these corpora, whith help of Internet searches. The results shown that the statistical extraction combined with the stop list and relevance metrics can produce superior results to linguistic process extraction using the same metrics. It?s concluded that statistical approach composed by these metrics can be ideal option to relevance terms extraction, by requiring few computational resources and by to show superior results that found in the linguistic processing. / Sabe-se que o processamento lingu?stico de corpora demanda grande esfor?o computacional devido ? complexidade dos seus algoritmos, mas que, apesar disso, os resultados alcan?ados s?o melhores que aqueles gerados pelo processamento estat?stico, onde a demanda computacional ? menor. Esta disserta??o descreve uma an?lise comparativa entre os processos lingu?stico e estat?stico de extra??o de termos. Foram realizados experimentos atrav?s de quatro corpora em l?ngua inglesa, constru?dos a partir de artigos cient?ficos, sobre os quais foram executadas extra??es de termos utilizando essas abordagens. As listas de termos resultantes foram refinadas com o uso de m?tricas de relev?ncia e stop list, e em seguida comparadas com as listas de refer?ncia dos corpora atrav?s da t?cnica do recall. Essas listas, por sua vez, foram constru?das a partir do contexto desses corpora e com ajuda de pesquisas na Internet. Os resultados mostraram que a extra??o estat?stica combinada com as t?cnicas da stop list e as m?tricas de relev?ncia pode produzir resultados superiores ao processo de extra??o lingu?stico refinado pelas mesmas m?tricas. Concluiu se que a abordagem estat?stica composta por essas t?cnicas pode ser a op??o ideal para extra??o de termos relevantes, por exigir poucos recursos computacionais e por apresentar resultados superiores ?queles encontrados no processamento lingu?stico.