Fractal analysis of weld defect patterns obtained by radiographic tests

This paper presents a fractal analysis of radiographic patterns obtained from specimens with three types of inserted welding defects: lack of fusion, lack of penetration, and porosity. The study focused on patterns of carbon steel beads from radiographs of the International Institute of Welding (IIW). The radiographs were scanned using a greyscale with 256 levels, and the fractal features of the surfaces constructed from the radiographic images were characterized by means of Hurst, detrended-fluctuation, and minimal-cover analyses. A Karhunen-Loeve transformation was then used to classify the curves obtained from the fractal analyses of the various images, and a study of the classification errors was performed. The obtained results indicate that fractal analyses can be an effective additional tool for pattern recognition of weld defects in radiographic tests.Comment: 7 pages, 2 figures. To appear AIP Conference Proceedings - QNDE 200

Vanishing Viscosity Limits and Boundary Layers for Circularly Symmetric 2D Flows

We continue the work of Lopes Filho, Mazzucato and Nussenzveig Lopes [LMN], on the vanishing viscosity limit of circularly symmetric viscous flow in a disk with rotating boundary, shown there to converge to the inviscid limit in $L^2$-norm as long as the prescribed angular velocity $\alpha(t)$ of the boundary has bounded total variation. Here we establish convergence in stronger $L^2$ and $L^p$-Sobolev spaces, allow for more singular angular velocities $\alpha$, and address the issue of analyzing the behavior of the boundary layer. This includes an analysis of concentration of vorticity in the vanishing viscosity limit. We also consider such flows on an annulus, whose two boundary components rotate independently. [LMN] Lopes Filho, M. C., Mazzucato, A. L. and Nussenzveig Lopes, H. J., Vanishing viscosity limit for incompressible flow inside a rotating circle, preprint 2006

Optimizing Opponents Selection in Bilateral Contracts Negotiation with Particle Swarm

This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); from the CONTEST project - SAICT-POL/23575/2016; and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Knowledge and attitude towards the gradual reduction of salt in bread – an online survey

Aim: Assess knowledge and attitude towards the gradual reduction of salt in bread and the potential impact on eating habits of children (6-18 years) and their families, as part as a Health Impact Assessment pilot study.N/

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, $\alpha$, that determines the strength of the correlation of the noise. To predict $\alpha$ the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the $\alpha$ value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same $\alpha$ parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community

Evaluating clustering methods on topographic and hidrological features on lidar data at forest environment.

The acquisition of high resolution geographic data through laser technology has recently being expanded due to the development of LiDAR (Light Detection and Ranging) system. This technology?s growth is relying on its great ability to acquire information in large quantity and short time. The geographic data provided from laser scanning is capable of raising information for coast planning, assess flooding risk, power transmission network and telecommunication, forests, agriculture, oil, transportation, urban planning, mining, among others (GIONGO et al., 2010). LiDAR technology follows the same principles as the RADAR system, with the difference of using laser pulses to locate features, instead of radio waves. Not only for its ability to deal with large amounts of information in such a short period of time, LiDAR has the advantage upon the classic passive sensors (aerial photographs and satellite images) of not depending on a source of light, and so its data will never present shadows from clouds or neighboring features (GIONGO et al., 2010). Data from LiDAR sensor is distributed in a point cloud where each point has at least three-dimensional spatial coordinates (latitude, longitude and height) that correspond to a particular point on the Earth?s surface from which the laser pulse was reflected. Once LiDAR data is acquired the next step is use algorithms that separate points (also referred to as returns) on the point cloud that represents the ground and the ones above the ground level, those algorithms can then process series of interpolation that allows the operator to generate Digital Elevation Models (DEMs). In order to add information for the points within the DEM, labeling those returns following a pattern and then grouping them on clusters is useful as one of the steps in exploratory data analysis. Several methodologies were developed to organize a pattern of points in a multidimensional space into clusters based on similarity. Points belonging to the same cluster are given the same label and present a pattern where they are more similar to each other than they are to a pattern belonging to a different cluster (JAIN et al., 1999). One example to apply this technology on forestry activities is the application of silvicultural treatment to improve the forest?s productivity, where the decision is taken considering characteristics from the site and sites with similar characteristics may have the same silvicultural system. The variety of techniques for grouping data elements has produced a rich and often confusing assortment of clustering methods. Furthermore, there is a lack of studies grouping topologic and hydrologic variables at forested environments. The goal of this survey is to evaluate k-means and CLARA clustering techniques on a LiDAR-derived DEM from southern Amazonia, in the municipality of Cotriguaçu, Mato Grosso, Brazil