Paradigmas de aprendizado de máquina aplicados à previsão de carga de baterias para veículos híbridos

Orientador : Prof. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 30/08/2017Inclui referências : p. 85-90Resumo: Uma das alternativas para a diminuição da poluição, seja esta sonora ou por gases e particulados, são os veículos híbridos, que combinam a autonomia do motor à combustão com os benefícios dos motores elétricos. Entretanto, por estes veículos contarem com baterias, o gerenciamento de energia destes veículos se torna determinante para uma adequada autonomia. Por outro lado, aprendizado de máquina é uma abordagem que trata do projeto e desenvolvimento de algoritmos que melhoram automaticamente com a experiência inspirado no comportamento de aprendizado de humanos. Tal comportamento pode ser obtido por meio do ajuste de parâmetros com base nos dados de entrada que são apresentados e, em alguns casos, nas informações de saída desejadas. Um dos focos da pesquisa em Aprendizado de Máquina é aprender automaticamente a reconhecer padrões complexos e tomar decisões com base em dados. Esta dissertação tem como objetivo principal a modelagem do estado de carga das baterias, usando diferentes técnicas de Aprendizado de Máquina para tarefa de identificação de sistemas, tais como máquinas de vetores de suporte, redes neuro-nebulosas (neuro-fuzzy), bagging, boosting, subspace e redes com estado de eco. Para avaliar o desempenho dos modelos matemáticos de Aprendizado de Máquina foram adotados dois índices de desempenho: (i) o erro quadrático médio (Mean Squared Error, MSE) e (ii) o coeficiente de determinação (R2). Pelos resultados obtidos, observou-se que alguns métodos de Aprendizado de Máquina apresentaram uma aproximação de boa qualidade quando comparada a saída real das cargas das baterias, isto evidenciado pelos valores de MSE e R2. Palavras-chave: Aprendizado de máquina; Identificação de sistemas; Máquinas de vetores de suporte; Comitê de máquinas; Previsão de carga de baterias; Veículos híbridos.Abstract: One of the alternatives for the pollution reduction, the hybrid vehicles combines the autonomy of the engine to the combustion with the benefits of the electric motors. However, because these vehicles have batteries, the energy management of these vehicles becomes decisive for an adequate autonomy. On the other hand, machine learning is an approach that deals with the design and development of algorithms that automatically improve with the experience inspired by human learning behavior. Such behavior can be achieved by adjusting parameters based on the input data that is presented and in some cases on the desired output information. One of the focuses of Machine Learning research is to automatically learn to recognize complex patterns and make decisions based on data. This work has as main objective the modeling of the batteries state of charge using different Machine Learning techniques applied to system identification task, such as support vector machines, neuro-fuzzy networks, bagging, boosting, subspace and echo state networks. In order to evaluate the performance of Machine Learning mathematical models, two performance indices were adopted: (i) the mean squared error (MSE) and (ii) the coefficient of determination (R2). From the results obtained, it was observed that some methods of Machine Learning presented a good quality approximation when compared to the actual output of the loads of the batteries, evidenced by the MSE and R2 values. Keywords: Machine learning; System identification; Support vector machine; Ensemble learning; Battery charge forecasting; Hybrid vehicles

Galaxy Evolution and Star Formation Efficiency at 0.2 < z < 0.6

We present the results of a CO line survey of 30 galaxies at moderate redshift (z \sim 0.2-0.6), with the IRAM 30m telescope, with the goal to follow galaxy evolution and in particular the star formation efficiency (SFE) as defined by the ratio between far-infrared luminosity and molecular gas mass (LFIR/M(H2)). The sources are selected to be ultra-luminous infrared galaxies (ULIRGs), with LFIR larger than 2.8 10^{12} Lsol, experiencing starbursts; adopting a low ULIRG CO-to-H2 conversion factor, their gas consumption time-scale is lower than 10^8 yr. To date only very few CO observations exist in this redshift range that spans nearly 25% of the universe's age. Considerable evolution of the star formation rate is already observed during this period. 18 galaxies out of our sample of 30 are detected (of which 16 are new detections), corresponding to a detection rate of 60%. The average CO luminosity for the 18 galaxies detected is L'CO = 2 10^{10} K km/s pc^2, corresponding to an average H2 mass of 1.6 10^{10} Msol. The FIR luminosity correlates well with the CO luminosity, in agreement with the correlation found for low and high redshift ULIRGs. Although the conversion factor between CO luminosity and H2 mass is uncertain, we find that the maximum amount of gas available for a single galaxy is quickly increasing as a function of redshift. Using the same conversion factor, the SFEs for z\sim 0.2-0.6 ULIRGs are found to be significantly higher, by a factor 3, than for local ULIRGs, and are comparable to high redshift ones. We compare this evolution to the expected cosmic H2 abundance and the cosmic star formation history.Comment: 11 pages, 13 figures, accepted in A&

Feasibility study of small anaerobic co-digestion at urban district level

International audienc

Hyperbolic normal stochastic volatility model

For option pricing models and heavy-tailed distributions, this study proposes a continuous-time stochastic volatility model based on an arithmetic Brownian motion: a one-parameter extension of the normal stochastic alpha-beta-rho (SABR) model. Using two generalized Bougerol&apos;s identities in the literature, the study shows that our model has a closed-form Monte Carlo simulation scheme and that the transition probability for one special case follows Johnson&apos;s SU distribution-a popular heavy-tailed distribution originally proposed without stochastic process. It is argued that the SU distribution serves as an analytically superior alternative to the normal SABR model because the two distributions are empirically similar

Short-term responses of unicellular planktonic eukaryotes to increases in temperature and UVB radiation

Abstract Background Small size eukaryotes play a fundamental role in the functioning of coastal ecosystems, however, the way in which these micro-organisms respond to combined effects of water temperature, UVB radiations (UVBR) and nutrient availability is still poorly investigated. Results We coupled molecular tools (18S rRNA gene sequencing and fingerprinting) with microscope-based identification and counting to experimentally investigate the short-term responses of small eukaryotes ( Conclusions This multifactorial experiment highlights the potential impacts, over short time scales, of changing environmental factors on the structure of various functional groups like small primary producers, parasites and saprotrophs which, in response, can modify energy flow in the planktonic food webs.</p