Spatial load forecasting using a demand propagation approach

A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Propagation of ship waves on a sloping bottom

The numerical model FUNWAVE was adapted in order to simulate the generation and propagation of ship waves to shore, including phenomena such as refraction, diffraction, currents and breaking of waves. Results are shown for Froude numbers equal to 0.8, 1.0 and 1.1, in order to verify the refraction of the wave pattern, identify breaking conditions and to investigate the wave generation scheme as a moving pressure at the free surface. © 2009 World Scientific Publishing Co. Pte. Ltd.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Modeling the formation and propagation of desiccation cracks in soils

The problem of desiccation cracks in soils has received increasing attention in the last few years, in both experimental investigations and modeling. Experimental research has been mainly focused on the behavior of slurries subjected to drying in plates of different shapes, sizes and thickness. The main objectives of these studies were to learn about the process of crack formation under controlled environmental conditions, and also to better understand the effect of different factors (e.g. soil type, boundary conditions, soil thickness) on the morphology of the crack network. As for the numerical modeling, different approaches have been suggested lately to describe the behavior of drying cracks in soils. One aspect that it is still difficult to describe properly is the crack pattern observed in desiccated soils. This work presents a novel technique to model the behavior of drying soils. The crack patter observed in desiccation tests on circular plates are simulated with the main objective of predicting the effect of soil thickness on crack pattern. Good agreement between experimental results and model prediction are observed.