977 resultados para Input Distance Function


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Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

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Measurement of arterial input function is a restrictive aspect for quantitative (18)F-FDG PET studies in rodents because of their small total blood volume and the related difficulties in withdrawing blood.

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This study provides detailed information on the ability of healthy ears to generate distortion product otoacoustic emissions (DPOAEs).

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A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

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The influence of curing tip distance and storage time in the kinetics of water diffusion (water sorption-W SP, solubility-W SB, and net water uptake) and color stability of a composite were evaluated. Composite samples were polymerized at different distances (5, 10, and 15 mm) and compared to a control group (0 mm). After desiccation, the specimens were stored in distilled water to evaluate the water diffusion over a 120-day period. Net water uptake was calculated (sum of WSP and WSB). The color stability after immersion in a grape juice was compared to distilled water. Data were submitted to three-way ANOVA/Tukey's test (α = 5%). The higher distances caused higher net water uptake (p < 0.05). The immersion in the juice caused significantly higher color change as a function of curing tip distance and the time (p < 0.05). The distance of photoactivation and storage time provide the color alteration and increased net water uptake of the resin composite tested.

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The estimation of the average travel distance in a low-level picker-to-part order picking system can be done by analytical methods in most cases. Often a uniform distribution of the access frequency over all bin locations is assumed in the storage system. This only applies if the bin location assignment is done randomly. If the access frequency of the articles is considered in the bin location assignment to reduce the average total travel distance of the picker, the access frequency over the bin locations of one aisle can be approximated by an exponential density function or any similar density function. All known calculation methods assume that the average number of orderlines per order is greater than the number of aisles of the storage system. In case of small orders this assumption is often invalid. This paper shows a new approach for calculating the average total travel distance taking into account that the average number of orderlines per order is lower than the total number of aisles in the storage system and the access frequency over the bin locations of an aisle can be approximated by any density function.

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DCE-MRI is an important technique in the study of small animal cancer models because its sensitivity to vascular changes opens the possibility of quantitative assessment of early therapeutic response. However, extraction of physiologically descriptive parameters from DCE-MRI data relies upon measurement of the vascular input function (VIF), which represents the contrast agent concentration time course in the blood plasma. This is difficult in small animal models due to artifacts associated with partial volume, inflow enhancement, and the limited temporal resolution achievable with MR imaging. In this work, the development of a suite of techniques for high temporal resolution, artifact resistant measurement of the VIF in mice is described. One obstacle in VIF measurement is inflow enhancement, which decreases the sensitivity of the MR signal to the presence of contrast agent. Because the traditional techniques used to suppress inflow enhancement degrade the achievable spatiotemporal resolution of the pulse sequence, improvements can be achieved by reducing the time required for the suppression. Thus, a novel RF pulse which provides spatial presaturation contemporaneously with the RF excitation was implemented and evaluated. This maximizes the achievable temporal resolution by removing the additional RF and gradient pulses typically required for suppression of inflow enhancement. A second challenge is achieving the temporal resolution required for accurate characterization of the VIF, which exceeds what can be achieved with conventional imaging techniques while maintaining adequate spatial resolution and tumor coverage. Thus, an anatomically constrained reconstruction strategy was developed that allows for sampling of the VIF at extremely high acceleration factors, permitting capture of the initial pass of the contrast agent in mice. Simulation, phantom, and in vivo validation of all components were performed. Finally, the two components were used to perform VIF measurement in the murine heart. An in vivo study of the VIF reproducibility was performed, and an improvement in the measured injection-to-injection variation was observed. This will lead to improvements in the reliability of quantitative DCE-MRI measurements and increase their sensitivity.

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Context. The ESA Rosetta spacecraft, currently orbiting around cornet 67P/Churyumov-Gerasimenko, has already provided in situ measurements of the dust grain properties from several instruments, particularly OSIRIS and GIADA. We propose adding value to those measurements by combining them with ground-based observations of the dust tail to monitor the overall, time-dependent dust-production rate and size distribution. Aims. To constrain the dust grain properties, we take Rosetta OSIRIS and GIADA results into account, and combine OSIRIS data during the approach phase (from late April to early June 2014) with a large data set of ground-based images that were acquired with the ESO Very Large Telescope (VLT) from February to November 2014. Methods. A Monte Carlo dust tail code, which has already been used to characterise the dust environments of several comets and active asteroids, has been applied to retrieve the dust parameters. Key properties of the grains (density, velocity, and size distribution) were obtained from. Rosetta observations: these parameters were used as input of the code to considerably reduce the number of free parameters. In this way, the overall dust mass-loss rate and its dependence on the heliocentric distance could be obtained accurately. Results. The dust parameters derived from the inner coma measurements by OSIRIS and GIADA and from distant imaging using VLT data are consistent, except for the power index of the size-distribution function, which is alpha = -3, instead of alpha = -2, for grains smaller than 1 mm. This is possibly linked to the presence of fluffy aggregates in the coma. The onset of cometary activity occurs at approximately 4.3 AU, with a dust production rate of 0.5 kg/s, increasing up to 15 kg/s at 2.9 AU. This implies a dust-to-gas mass ratio varying between 3.8 and 6.5 for the best-fit model when combined with water-production rates from the MIRO experiment.

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Improving the representation of the hydrological cycle in Atmospheric General Circulation Models (AGCMs) is one of the main challenges in modeling the Earth's climate system. One way to evaluate model performance is to simulate the transport of water isotopes. Among those available, tritium (HTO) is an extremely valuable tracer, because its content in the different reservoirs involved in the water cycle (stratosphere, troposphere, ocean) varies by order of magnitude. Previous work incorporated natural tritium into LMDZ-iso, a version of the LMDZ general circulation model enhanced by water isotope diagnostics. Here for the first time, the anthropogenic tritium injected by each of the atmospheric nuclear-bomb tests between 1945 and 1980 has been first estimated and further implemented in the model; it creates an opportunity to evaluate certain aspects of LDMZ over several decades by following the bomb-tritium transient signal through the hydrological cycle. Simulations of tritium in water vapor and precipitation for the period 1950-2008, with both natural and anthropogenic components, are presented in this study. LMDZ-iso satisfactorily reproduces the general shape of the temporal evolution of tritium. However, LMDZ-iso simulates too high a bomb-tritium peak followed by too strong a decrease of tritium in precipitation. The too diffusive vertical advection in AGCMs crucially affects the residence time of tritium in the stratosphere. This insight into model performance demonstrates that the implementation of tritium in an AGCM provides a new and valuable test of the modeled atmospheric transport, complementing water stable isotope modeling.

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La machine à vecteurs de support à une classe est un algorithme non-supervisé qui est capable d’apprendre une fonction de décision à partir de données d’une seule classe pour la détection d’anomalie. Avec les données d’entraînement d’une seule classe, elle peut identifier si une nouvelle donnée est similaire à l’ensemble d’entraînement. Dans ce mémoire, nous nous intéressons à la reconnaissance de forme de dynamique de frappe par la machine à vecteurs de support à une classe, pour l’authentification d’étudiants dans un système d’évaluation sommative à distance à l’Université Laval. Comme chaque étudiant à l’Université Laval possède un identifiant court, unique qu’il utilise pour tout accès sécurisé aux ressources informatiques, nous avons choisi cette chaîne de caractères comme support à la saisie de dynamique de frappe d’utilisateur pour construire notre propre base de données. Après avoir entraîné un modèle pour chaque étudiant avec ses données de dynamique de frappe, on veut pouvoir l’identifier et éventuellement détecter des imposteurs. Trois méthodes pour la classification ont été testées et discutées. Ainsi, nous avons pu constater les faiblesses de chaque méthode dans ce système. L’évaluation des taux de reconnaissance a permis de mettre en évidence leur dépendance au nombre de signatures ainsi qu’au nombre de caractères utilisés pour construire les signatures. Enfin, nous avons montré qu’il existe des corrélations entre le taux de reconnaissance et la dispersion dans les distributions des caractéristiques des signatures de dynamique de frappe.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Context. Observations in the cosmological domain are heavily dependent on the validity of the cosmic distance-duality (DD) relation, eta = D(L)(z)(1+ z)(2)/D(A)(z) = 1, an exact result required by the Etherington reciprocity theorem where D(L)(z) and D(A)(z) are, respectively, the luminosity and angular diameter distances. In the limit of very small redshifts D(A)(z) = D(L)(z) and this ratio is trivially satisfied. Measurements of Sunyaev-Zeldovich effect (SZE) and X-rays combined with the DD relation have been used to determine D(A)(z) from galaxy clusters. This combination offers the possibility of testing the validity of the DD relation, as well as determining which physical processes occur in galaxy clusters via their shapes. Aims. We use WMAP (7 years) results by fixing the conventional Lambda CDM model to verify the consistence between the validity of DD relation and different assumptions about galaxy cluster geometries usually adopted in the literature. Methods. We assume that. is a function of the redshift parametrized by two different relations: eta(z) = 1+eta(0)z, and eta(z) = 1+eta(0)z/(1+z), where eta(0) is a constant parameter quantifying the possible departure from the strict validity of the DD relation. In order to determine the probability density function (PDF) of eta(0), we consider the angular diameter distances from galaxy clusters recently studied by two different groups by assuming elliptical (isothermal) and spherical (non-isothermal) beta models. The strict validity of the DD relation will occur only if the maximum value of eta(0) PDF is centered on eta(0) = 0. Results. It was found that the elliptical beta model is in good agreement with the data, showing no violation of the DD relation (PDF peaked close to eta(0) = 0 at 1 sigma), while the spherical (non-isothermal) one is only marginally compatible at 3 sigma. Conclusions. The present results derived by combining the SZE and X-ray surface brightness data from galaxy clusters with the latest WMAP results (7-years) favors the elliptical geometry for galaxy clusters. It is remarkable that a local property like the geometry of galaxy clusters might be constrained by a global argument provided by the cosmic DD relation.

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In this Letter, we propose a new and model-independent cosmological test for the distance-duality (DD) relation, eta = D(L)(z)(1 + z)(-2)/D(A)(z) = 1, where D(L) and D(A) are, respectively, the luminosity and angular diameter distances. For D(L) we consider two sub-samples of Type Ia supernovae (SNe Ia) taken from Constitution data whereas D(A) distances are provided by two samples of galaxy clusters compiled by De Filippis et al. and Bonamente et al. by combining Sunyaev-Zeldovich effect and X-ray surface brightness. The SNe Ia redshifts of each sub-sample were carefully chosen to coincide with the ones of the associated galaxy cluster sample (Delta z < 0.005), thereby allowing a direct test of the DD relation. Since for very low redshifts, D(A)(z) approximate to D(L)(z), we have tested the DD relation by assuming that. is a function of the redshift parameterized by two different expressions: eta(z) = 1 + eta(0)z and eta(z) = 1 +eta(0)z/(1 + z), where eta(0) is a constant parameter quantifying a possible departure from the strict validity of the reciprocity relation (eta(0) = 0). In the best scenario (linear parameterization), we obtain eta(0) = -0.28(-0.44)(+0.44) (2 sigma, statistical + systematic errors) for the De Filippis et al. sample (elliptical geometry), a result only marginally compatible with the DD relation. However, for the Bonamente et al. sample (spherical geometry) the constraint is eta(0) = -0.42(-0.34)(+0.34) (3 sigma, statistical + systematic errors), which is clearly incompatible with the duality-distance relation.

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The VISTA near infrared survey of the Magellanic System (VMC) will provide deep YJK(s) photometry reaching stars in the oldest turn-off point throughout the Magellanic Clouds (MCs). As part of the preparation for the survey, we aim to access the accuracy in the star formation history (SFH) that can be expected from VMC data, in particular for the Large Magellanic Cloud (LMC). To this aim, we first simulate VMC images containing not only the LMC stellar populations but also the foreground Milky Way (MW) stars and background galaxies. The simulations cover the whole range of density of LMC field stars. We then perform aperture photometry over these simulated images, access the expected levels of photometric errors and incompleteness, and apply the classical technique of SFH-recovery based on the reconstruction of colour-magnitude diagrams (CMD) via the minimisation of a chi-squared-like statistics. We verify that the foreground MW stars are accurately recovered by the minimisation algorithms, whereas the background galaxies can be largely eliminated from the CMD analysis due to their particular colours and morphologies. We then evaluate the expected errors in the recovered star formation rate as a function of stellar age, SFR(t), starting from models with a known age-metallicity relation (AMR). It turns out that, for a given sky area, the random errors for ages older than similar to 0.4 Gyr seem to be independent of the crowding. This can be explained by a counterbalancing effect between the loss of stars from a decrease in the completeness and the gain of stars from an increase in the stellar density. For a spatial resolution of similar to 0.1 deg(2), the random errors in SFR(t) will be below 20% for this wide range of ages. On the other hand, due to the lower stellar statistics for stars younger than similar to 0.4 Gyr, the outer LMC regions will require larger areas to achieve the same level of accuracy in the SFR( t). If we consider the AMR as unknown, the SFH-recovery algorithm is able to accurately recover the input AMR, at the price of an increase of random errors in the SFR(t) by a factor of about 2.5. Experiments of SFH-recovery performed for varying distance modulus and reddening indicate that these parameters can be determined with (relative) accuracies of Delta(m-M)(0) similar to 0.02 mag and Delta E(B-V) similar to 0.01 mag, for each individual field over the LMC. The propagation of these errors in the SFR(t) implies systematic errors below 30%. This level of accuracy in the SFR(t) can reveal significant imprints in the dynamical evolution of this unique and nearby stellar system, as well as possible signatures of the past interaction between the MCs and the MW.