Nueva técnica de fusión de clasificadores aplicada a la mejora de la segmentación de audio

Este artículo presenta un nuevo algoritmo de fusión de clasificadores a partir de su matriz de confusión de la que se extraen los valores de precisión (precision) y cobertura (recall) de cada uno de ellos. Los únicos datos requeridos para poder aplicar este nuevo método de fusión son las clases o etiquetas asignadas por cada uno de los sistemas y las clases de referencia en la parte de desarrollo de la base de datos. Se describe el algoritmo propuesto y se recogen los resultados obtenidos en la combinación de las salidas de dos sistemas participantes en la campaña de evaluación de segmentación de audio Albayzin 2012. Se ha comprobado la robustez del algoritmo, obteniendo una reducción relativa del error de segmentación del 6.28% utilizando para realizar la fusión el sistema con menor y mayor tasa de error de los presentados a la evaluación.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Optimal Design of a Hybrid Membrane System Combining Reverse and Forward Osmosis for Seawater Desalination

In this work we study Forward Osmosis (FO) as an emerging desalination technology, and its capability to replace totally or partially Reverse Osmosis (RO) in order to reduce the great amount of energy required in the current desalination plants. For this purpose, we propose a superstructure that includes both membrane based desalination technologies, allowing the selection of only one of the technologies or a combination of both of them seeking for the optimal configuration of the network. The optimization problem is solved for a seawater desalination plant with a given fresh water production. The results obtained show that the optimal solution combines both desalination technologies to reduce not only the energy consumption but also the total cost of the desalination process in comparison with the same plant but operating only with RO.

Mechanisms that Trigger a Good Health-Care Response to Intimate Partner Violence in Spain. Combining Realist Evaluation and Qualitative Comparative Analysis Approaches

Background. Health care professionals, especially those working in primary health-care services, can play a key role in preventing and responding to intimate partner violence. However, there are huge variations in the way health care professionals and primary health care teams respond to intimate partner violence. In this study we tested a previously developed programme theory on 15 primary health care center teams located in four different Spanish regions: Murcia, C Valenciana, Castilla-León and Cantabria. The aim was to identify the key combinations of contextual factors and mechanisms that trigger a good primary health care center team response to intimate partner violence. Methods. A multiple case-study design was used. Qualitative and quantitative information was collected from each of the 15 centers (cases). In order to handle the large amount of information without losing familiarity with each case, qualitative comparative analysis was undertaken. Conditions (context and mechanisms) and outcomes, were identified and assessed for each of the 15 cases, and solution formulae were calculated using qualitative comparative analysis software. Results. The emerging programme theory highlighted the importance of the combination of each team’s self-efficacy, perceived preparation and women-centredness in generating a good team response to intimate partner violence. The use of the protocol and accumulated experience in primary health care were the most relevant contextual/intervention conditions to trigger a good response. However in order to achieve this, they must be combined with other conditions, such as an enabling team climate, having a champion social worker and having staff with training in intimate partner violence. Conclusions. Interventions to improve primary health care teams’ response to intimate partner violence should focus on strengthening team’s self-efficacy, perceived preparation and the implementation of a woman-centred approach. The use of the protocol combined with a large working experience in primary health care, and other factors such as training, a good team climate, and having a champion social worker on the team, also played a key role. Measures to sustain such interventions and promote these contextual factors should be encouraged.