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showing 10 items of 2078 documents
Pricing of Forwards and Options in a Multivariate Non-Gaussian Stochastic Volatility Model for Energy Markets
2013
In Benth and Vos (2013) we introduced a multivariate spot price model with stochastic volatility for energy markets which captures characteristic features, such as price spikes, mean reversion, stochastic volatility, and inverse leverage effect as well as dependencies between commodities. In this paper we derive the forward price dynamics based on our multivariate spot price model, providing a very flexible structure for the forward curves, including contango, backwardation, and hump shape. Moreover, a Fourier transform-based method to price options on the forward is described.
Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods
2021
Abstract Data-based methods are useful for accurate modelling of solar thermal systems. In this work, several artificial neural network (ANN) techniques are proposed to predict the thermal performance of an all-glass straight through evacuated tube solar collector. These are compared to support vector regression analysis. Extensive experimental data sets were collected for training the ANN models. Solar radiation intensity, ambient temperature, wind speed, mass flow rate and collector inlet temperature were selected as the input layer to predict the thermal efficiency of the solar collector. The prediction precision of the ANN models was compared to the multiple linear regression and suppor…
A novel dynamic multi-model relevance feedback procedure for content-based image retrieval
2016
This paper deals with the problem of image retrieval in large databases with a big semantic gap by a relevance feedback procedure. We present a novel algorithm for modelling the users's preferences in the content-based image retrieval system.The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, and estimates the parameters of several local logistic regression models whose inputs are the low-level image features. A Principal Component Analysis method is applied to the original vector to reduce its high dimensionality. The relevance probabilities predicted by these local models are combined by means of a weighted average. These weights …
A central nervous system-focused treatment approach for people with frozen shoulder: protocol for a randomized clinical trial
2019
Background: Frozen shoulder (FS) is a musculoskeletal condition of poorly understood etiology that results in shoulder pain and large mobility deficits. Despite some physical therapy interventions, such as joint mobilization and exercise, having shown therapeutic benefit, a definitive treatment does not currently exist. The aim of this study will be to compare the effectiveness of a central nervous system (CNS)-directed treatment program versus a standard medical and physical therapy care program on outcomes in participants with FS. Methods/design: The study is a two-group, randomized clinical trial with blinding of participants and assessors. Participants will be recruited via referrals fr…
Application of non-invasive technologies in dry-cured ham: An overview
2019
Background: Dry-cured ham is one of the most valued food products by Mediterranean consumers. In this sense, the appropriate development of its different production stages is essential to ensure the quality requirements. For this reason, non-invasive technologies have gained popularity and have been reported as useful not only to ensure the food safety of different products, but also to monitor fundamental stages in the production process, such as the salting stage, to analyze the content of different compounds without sample losses, and to correct possible defects in the final product. Scope and approach: This work has been focused on summarizing the studies that describe and have successf…
Una tabla inédita de Fernando Yáñez y nueva luz sobre su estancia en Almedina (1518-1525)
2020
The contribution presents a new acquisition for the catalog of Fernando Yáñez, a Holy Family dating back to 1523, shedding new light on the artist?s production in the third decade of the sixteenth century during his return to Almedina, his birthplace, in a time frame that divides the works for Valencia, like the retablo mayor for the city?s cathedral, from his altarpieces for Cuenca.
Precision electroweak measurements on the Z resonance
2005
We report on the final electroweak measurements performed with data taken at the Z resonance by the experiments operating at the electron-positron colliders SLC and LEP. The data consist of 17 million Z decays accumulated by the ALEPH, DELPHI, L3 and OPAL experiments at LEP, and 600 thousand Z decays by the SLD experiment using a polarised beam at SLC. The measurements include cross-sections, forward-backward asymmetries and polarised asymmetries. The mass and width of the Z boson, $\MZ$ and $\GZ$, and its couplings to fermions, for example the $\rho$ parameter and the effective electroweak mixing angle, are precisely measured. The number of light neutrino species is determined to be 2.9840…
Combined Forward-Backward Asymmetry Measurements in Top-Antitop Quark Production at the Tevatron
2018
The CDF and D0 experiments at the Fermilab Tevatron have measured the asymmetry between yields of forward- and backward-produced top and antitop quarks based on their rapidity difference and the asymmetry between their decay leptons. These measurements use the full data sets collected in proton-antiproton collisions at a center-of-mass energy of √s=1.96 TeV. We report the results of combinations of the inclusive asymmetries and their differential dependencies on relevant kinematic quantities. The combined inclusive asymmetry is At¯tFB=0.128±0.025. The combined inclusive and differential asymmetries are consistent with recent standard model predictions.
Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance
2016
This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.
Multilayer neural networks: an experimental evaluation of on-line training methods
2004
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…