987 resultados para Sobral de Monte Agraço
Resumo:
Julkaisussa: Cosmographia : impressum Ulme opera et expensis justi de Albano de Venetiis per provisorem
Resumo:
Julkaisussa: Cosmographia : hic finit cosmographia Ptolemei impressa opa dominici de lapis ciuis Bononiensis
Resumo:
Kartta kuuluu A. E. Nordenskiöldin kokoelmaan
Resumo:
This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.
Resumo:
A farinha da entrecasca de melancia (FEM) foi obtida, sua composição determinada e utilizada em formulação de bolos. Elaboraram-se bolos sem a FEM (controle) e contendo 7 e 30% de FEM em substituição à farinha de trigo (experimentais). A composição química, características físicas, físico-químicas foram determinadas. Na avaliação sensorial, cem provadores não treinados receberam amostras em blocos balanceados e realizaram testes sensoriais, utilizando escala hedônica de 9 pontos e comparação múltipla. Os dados obtidos foram avaliados por estatísticas descritivas, ANOVA, testes de Tukey e Dunnet. Os resultados revelaram que 100 g da FEM continham 9,06 g de umidade, 12,72 g de cinzas, 0,7 g de lipídios, 1,20 g de proteínas, 31,01 g de fibras insolúveis, 45,21 g de glicídios totais e 192,75 kcal. Nos bolos experimentais, os pesos, alturas, diâmetros e rendimentos foram maiores e o índice de expansão menor, bem como, o volume aparente do bolo com 30% FEM foi menor. Os bolos experimentais apresentaram menor pH, maior acidez titulável, maiores teores de fibra e umidade, menores de glicídios totais e reduzido valor energético. Os bolos obtiveram boa aceitação e mais de 60% dos provadores comprariam os bolos. O bolo com 7% FEM foi ligeiramente melhor que o controle, diferindo no aroma e sabor do que continha 30% FEM. Portanto, o uso de FEM para produção de bolos, nas condições dessa pesquisa, é viável do ponto de vista tecnológico, nutricional e sensorial.
Resumo:
1857/04/30 (N2).
Resumo:
1858/02/04 (N42).
Resumo:
1857/07/16 (N13).
Resumo:
1857/06/25 (N10).
Resumo:
1857/12/10 (N34).
Resumo:
1857/11/19 (N31).
Resumo:
1857/08/13 (N17).
Resumo:
1858/01/07 (N38).
Resumo:
1857/07/23 (N14).
Resumo:
1857/12/17 (N35).
