S. García Galán

Particle swarm optimization for biomass-fuelled systems with technical constraints

This paper introduces a binary particle swarm optimization-based method to accomplish optimal location of biomass-fuelled systems for distributed power generation. The approach also provides the supply area for the biomass plant and takes technical constraints into account. This issue can be formulated as a nonlinear optimization problem. In rural or radial distribution networks the main technical constraint is the impact on the voltage profile. Biomass is one of the most promising renewable energy sources in Europe, but more research is required to prove that power generation from biomass is both technically and economically viable. Forest residues are here considered as biomass source, and the fitness function to be optimized is the profitability index. A fair comparison between the proposed algorithm and genetic algorithms (GAs) is performed. For such goal, convergence curves of the average profitability index versus number of iterations are computed. The proposed algorithm reaches a better solution than GAs when considering similar computational cost (similar number of evaluations).

A Method for Particle Swarm Optimization and its Application in Location of Biomass Power Plants

This work introduces a binary particle swarm optimization based approach to locate the optimal location for biomass-based power plants. The proposed algorithm also offers the supply area for the biomass plant. The optimal location can be addressed as a nonlinear optimization problem. The profitability index is the fitness function for the binary optimization algorithm. It is defined as the ratio between the net present value and the initial investment. The constraints for simulations are: the biomass power plant must be inside the supply area; the electric power generated by the plant is limited to 10 MW. Computer simulations have been performed using 15 particles in the swarm and 50 iterations. Simulation results show that the proposed approach provides high-quality solutions (the profitability index is about 1.8) with reduced computation time (about 170 times lower than that required for exhaustive search).

Comparing open-source e-learning platforms from adaptivity point of view

The success of the e-learning paradigm observed in recent times has created a growing demand for e-learning systems in universities and other educational institutions, which has itself led to the development of a number of either commercial or open-source Learning Management Systems (LMS). While the usage of these systems gains recognition and acceptance amongst institutions, there are new problems arising that need to be solved. Because of multiplicity of platforms and approaches for systems implementation, it becomes increasingly difficult to manage or compare them. Each new LMS presents its own learning model. How to compare different e-learning platforms, and on what basis to choose the most adequate one, is a task of ever increasing importance. This paper describes and compares some widely used open-source e-learning platforms (Docebo, Moodle, Dokeos, Claroline, Atutor and Ilias) from the point of their adaptivity.

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.

A Method to Minimize Distributed PSO Algorithm Execution Time in Grid Computer Environment

This paper introduces a method to minimize distributed PSO algorithm execution time in a grid computer environment, based on a reduction in the information interchanged among the demes involved in the process of finding the best global fitness solution. Demes usually interchange the best global fitness solution they found at each iteration. Instead of this, we propose to interchange information only after an specified number of iterations are concluded. By applying this technique, it is possible to get a very significant execution time decrease without any loss of solution quality.

Classifying a New Descriptor Based on Marr's Visual Theory

Descriptors are a powerful tool in digital image analysis. Performance of tasks such as image matching and object recognition is strongly dependent on the visual descriptors that are used. The dimension of the descriptor has a direct impact on the time the analysis take, and less dimensions are desirable for fast matching. In this paper we use a type of region called curvilinear region. This approach is based on Marrs visual theory. Marr supposed that every object can be divided in its constituent parts, being this parts cylinders. So, we suppose also that in every image there must be curvilinear regions that are easy to detect. We propose a very short descriptor to use with these curvilinear regions in order to classify these regions for higher visual tasks.

Speech/Music Classification Based on Distributed Evolutionary Fuzzy Logic for Intelligent Audio Coding

Automatic Speech/Music Discrimination (SMD) has become a research topic of interest in the last years. This paper present a new approach for such goal, which is mainly based on a distributed expert system that incorporates fuzzy rules into its knowledge base. The proposed SMD scheme consists of two stages: 1) features extraction, 2) classification of parameters. Classification is performed by cascading a GMM-based classifier with an Evolutionary Fuzzy Expert (EFE) system. The EFE system improves the accuracy rate provided by the GMM-based classifier taking into account information of current and past audio frames. Testing the kindness of new fuzzy rules for the expert system has a high computacional cost. For that reason, a distributed learning approach based on web services has been implemented.