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En este trabajo de investigación se realizó un estudio del comportamiento de la distorsión armónica de corriente en un nodo de carga alimentado por la red pública de energía eléctrica y por inversores interconectados al mismo nodo. El análisis se desarrolló a través de un diseño experimental, empleando herramientas estadísticas como el análisis de la varianza y arreglos factoriales completos, aplicados a datos experimentales obtenidos mediante la configuración de un banco de pruebas con un punto de acoplamiento común con la carga, donde se integran las potencias de la red y de los sistemas renovables (fotovoltaicos), a través de inversores electrónicos. Los resultados obtenidos muestran que el impacto en la distorsión armónica de corriente en el nodo de carga depende mayormente del tipo de inversor utilizado y de la carga conectada al nodo. Además, los flujos de armónicas aparecen en mayor medida en la red del suministrador en comparación con las armónicas presentes en las cargas conectadas al nodo bajo estudio.

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user’s electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Cardiopulmonary auscultation is a diagnostic procedure that has a challenging task since the components of heart rate and lung sounds overlap. There were many approaches to quantify the characteristics of these signals, and one of the newest is the voice activity detection (VAD) and the Gaussian Mixture Models (GMM). Considering the lung and heart sounds as acoustic events, this paper proposes a novel assessment methodology of these diagnostic indicators. Here, VAD-GMM was applied to detect and extract the main events in lung sound and heart sounds. VAD- GMM results were compared with other VAD methodology based on statistical approach, and it was found that VAD-GMM give more definite results. Since Mel Frequency Cepstral coefficients (MFCC) and Quartiles feature vectors, were already successful in pattern recognition, VAD-GMM was carried out using this kind of acoustic vectors. Therefore, this method could add in a transition from qualitative traditional auscultation to quantitative assessment and assisted computerized diagnosis by identifying abnormal acoustic indicators. Diagnosis by computerized detection promises to be a more efficient method than traditional methods, which are limited by the auditory capability and experience of a medical professional.

Voice corpus is an essential element for automatic speaker recognition systems. In order for a corpus to be useful in recognition tasks, it must contain recordings from several speakers pronouncing phonetically balanced utterances; recorded through several sessions using different recording media. This work shows the methodology, development and evaluation of a Mexican Spanish Corpus referred as to VoCMex, which is aimed to support research on speaker recognition. It contains telephone and microphone recordings of 20 male and 13 female speakers, obtained through three sessions. In order to validate the usefulness of the corpus, a speaker identification system was developed and the recognition results were similar compared against those obtained using a known voice corpus.