N. Ruiz Reyes

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).

A Multiple-F0 Estimation Approach Based on Gaussian Spectral Modelling for Polyphonic Music Transcription

Abstract This paper proposes a multiple-F0 estimation algorithm for automatic polyphonic music transcription. The proposed algorithm operates at frame level, searching for the set of fundamental frequencies that minimizes a spectral distance measure at each audio frame. The spectral distance measure is defined under the assumption that a polyphonic sound can be modelled by a weighted sum of Gaussian spectral models. Due to the fact that in polyphonic music signals the spectral content at the current audio frame depends to a large extent on the immediately previous ones, the defined spectral distance measure takes into account not only information from the current audio frame but also from some previous ones. An additional performance improvement is achieved by using a Hidden Markov Model (HMM) at the end of the algorithm. The proposed algorithm is tested using real-world polyphonic music recordings taken from the RWC music database. Accuracy rates are reported when our algorithm is performed under different conditions. Classification of the total error into the three categories of errors (substitutions, misses and false alarms) is also reported. Comparison with five recent state-of-the art transcription systems is finally shown.

Adaptive Signal Modeling Based on Sparse Approximations for Scalable Parametric Audio Coding

This paper deals with the application of adaptive signal models for parametric audio coding. A fully parametric audio coder, which decomposes the audio signal into sinusoids, transients and noise, is here proposed. Adaptive signal models for sinusoidal, transient, and noise modeling are therefore included in the parametric scheme in order to achieve high-quality and low bit-rate audio coding. In this paper, a new sinusoidal modeling method based on a perceptual distortion measure is proposed. For transient modeling, a fast and effective method based on matching pursuit with a mixed dictionary is chosen. The residue of the previous models is analyzed as a noise-like signal. The proposed parametric audio coder allows high quality audio coding for one-channel audio signals at 16 kbits/s (average bit rate). A bit-rate scalable version of the parametric audio coder is also proposed in this work. Bit-rate scalability is intended for audio streaming applications, which are highly demanded nowadays. The performance of the proposed parametric audio coders (nonscalable and scalable coders) is assessed in comparison to widely used audio coders operating at similar bit rates.

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.

Improving multiple-F0 estimation by onset detection for polyphonic music transcription

In a monaural polyphonic context, music transcription and specifically, multiple-F0 estimation systems have achieved promising results in the last decade. However, most of these systems present intermittent misses of pitch within a note or inaccurate definitions about onsets and offsets due to frame-by-frame analysis. In this paper, we propose a multiple-F0 estimation system which extracts a set of active pitches at each frame (analysis frame) but note tracking is performed defining temporal intervals by an accurate onset detector. Our system shows promising results, in terms of onset and multiple-F0 estimation, to be evaluated using real-world and synthesized polyphonic music recordings taken from MAPS music database.

Fast communication: New algorithm based on spectral distance maximization to deal with the overlapping partial problem in note-event detection

Harmonic matching pursuit (HMP) is an interesting signal processing tool for note–event detection in polyphonic music transcription. HMP decomposes an audio signal into harmonic atoms. Audio signals are well represented by harmonic atoms due to their strong harmonic content. However, HMP provides an inaccurate decomposition when musical notes with rational fundamental frequency relation are simultaneously played (the overlapping partial problem). In this paper, a signal processing algorithm dealing with this problem is proposed. The algorithm is based on maximizing a smoothness-based criterion of the spectral envelope for each harmonic atom resulting from the HMP decomposition. In this way, we obtain an improved harmonic decomposition, which achieves high accuracy rates in note–event detection when dealing with harmonically related simultaneous notes. & 2009 Elsevier B.V. All rights reserved.

Profitability Comparison Between Gas Turbines and Gas Engine in Biomass-Based Power Plants Using Binary Particle Swarm Optimization

This paper employs a binary discrete version of the classical Particle Swarm Optimization to compare the maximum net present value achieved by a gas turbines biomass plant and a gas engine biomass plant. The proposed algorithm determines the optimal location for biomass turbines plant and biomass gas engine plant in order to choose the most profitable between them. Forest residues are converted into biogas . The fitness function for the binary optimization algorithm is the net present value. The problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. Computer simulations have been performed using 20 particles in the swarm and 50 iterations for each kind of power plant. Simulation results indicate that Particle Swarm Optimization is a useful tool to choose successful among different types of biomass plant technologies. In addition, the comparison is made with reduced computation time (more than 800 times lower than that required for exhaustive search).

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.