10 resultados para Model calibration
em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"
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Pós-graduação em Geociências e Meio Ambiente - IGCE
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The CERES-Maize model was used to estimate the spatial variability in corn (Zea mays L.) yield for 1995 and 1996 using data measured on soil profiles located on a 30.5 m grid within a 3.9 ha field in Michigan. The model was calibrated for one grid profile for the 1995 and then used to simulate corn yield for all grid points for the 2 yrs. For the calibration for 1995, the model predicted corn yield within 2%. For 1995, the model predicted yield variability very well (r(2) = 0.85), producing similar yield maps with differences generally within +/- 300 kg ha(-1). For 1996, the model predicted low grain yields (1167 kg ha(-1)) compared with measured (8928 kg ha(-1)) because the model does not account for horizontal water movement within the landscape or water contributions from a water table. Under nonlimiting water conditions, the model performed well (average of 8717 vs. 8948 kg ha(-1)) but under-estimated the measured yield variability.
Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean
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Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color-based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BCGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of similar to 1000 C-14 measurements spanning more than a decade (1983-1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PR specifically yielding too few low PP (< 0.2 g Cm-2 d(-1)) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomassnormalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140 degrees W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison 6 years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill. (C) 2008 Elsevier BY. All rights reserved.
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Temporal and spatial acoustic intensity (SATA) of therapeutic ultrasound (US) equipment should be monitored periodically. In order to evaluate the conditions of US equipment in use in the city of Piracicaba-Sao Paulo, Brazil, 31 machines - representing all Brazilian manufacturers - were analysed under continuous and pulsed conditions at a frequency of 1 MHz. Data about temporal and spatial acoustic intensity were collected and the use of equipment was surveyed. Intensities of 0.1, 0.2, 0.5, 0.8, 1.0, 1.5, 2.0, 2.5 and 3.0 Wcm -2, indicated on the equipment panel were analysed using a previously calibrated digital radiation pressure scale, model UPM-DT-1 (Ohmic Instruments Co). The acoustic intensity (I) results were expressed as superior and inferior quartile ranges for transducers with metal surfaces of 9 cm 2 and an effective radiation area (ERA) Of 4 cm 2. The results under continuous conditions were: I 0.1 = -20.0% and -96%. I 0.2 = -3.1% and -83.7%. I 0.5 = -35.0% and -86.5%. I 0.8 = -37.5% and -71.0%. I 2.5 = -49.0% and -69.5%. I 3.0 = -58.1% and -77.6%. For pulsed conditions, intensities were: I 0.1 = -40.0% and -86.2%. I 1.0 = -50.0% and -86.5%. I 1.5 = -62.5% and -82.5%. I 2.0 = -62.5% and -81.6%. I 2.5 = -64.7% and -88.8%. I 3.0 = -87.1% and -94.8%. In reply to the questionnaire drawn up to check the conditions of use of equipment, all users reported the use of hydrosoluble gel as a coupling medium and none had carried out previous calibrations. Most users used intensities in the range of 0.4. to 1.0 Wcm -2 and used machines for 300 to 400 minutes per week. The majority of machines had been bought during the previous seven years and weekly use ranged from less than 100 minutes to 700 minutes (11 hours 40 minutes). Findings confirm previous observations of discrepancy between the intensity indicated on the equipment panel and that emitted by the transducer and highlight the necessity for periodic evaluations of US equipment.
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The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.
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Aerodynamic balances are employed in wind tunnels to estimate the forces and moments acting on the model under test. This paper proposes a methodology for the assessment of uncertainty in the calibration of an internal multi-component aerodynamic balance. In order to obtain a suitable model to provide aerodynamic loads from the balance sensor responses, a calibration is performed prior to the tests by applying known weights to the balance. A multivariate polynomial fitting by the least squares method is used to interpolate the calibration data points. The uncertainties of both the applied loads and the readings of the sensors are considered in the regression. The data reduction includes the estimation of the calibration coefficients, the predicted values of the load components and their corresponding uncertainties, as well as the goodness of fit.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)