Modelos de representación de imprecisión e incertidumbre en fusión de alto nivel

Actas de: XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2014). Zaragoza, 5-7 de febrero de 2014.Las técnicas de fusión de datos e información procedente de redes de sensores necesitan manejar información incierta e imprecisa, puesto que es habitual enfrentarse a problemas en los que el conocimiento disponible es vago o insuficiente y/o los aparatos de medición están sujetos a fallos. Con el reciente auge de la denominada "fusión de alto nivel", que tiene como objetivo reconocer la situación observada e identificar posibles riesgos, este problema se ha acentuado, ya que los formalismos que se utilizan habitualmente para construir un modelo simbólico del escenario, como la lógica de primer orden y las ontologías, no proporcionan soporte para este tipo de conocimiento. En este trabajo repasamos varias propuestas recientes para representación y razonamiento con información incierta e imprecisa en fusión de alto nivel. Nos centramos en dos tipos: (a) las que incorporan estos mecanismos en los propios modelos de representación, como las ontologías probabilísticas y difusas y las redes lógicas de Markov; (b) las que extienden el proceso de fusión con una capa de gestión de incertidumbre adicional, como las basadas en argumentación probabilística.Este trabajo ha sido financiado por la Junta de Andalucía (P11-TIC-7460), la Comunidad de Madrid (S2009/TIC- 1485) y el Ministerio de Economía y Competitividad de España (TEC2012-37832-C02-01, TEC2011-28626-C02- 02, TIN2012-30939).Publicad

Building Ontologies at the Knowledge Level using the Ontology Design Environment

This paper discusses how ontologies can be specified at the knowledge level using the set of intermediate representations (Gómez-Pérez, Fernández & de Vicente 1996) proposed by METHONTOLOGY (Fernández, Gómez-Pérez & Juristo 1997; and Gómez-Pérez 1998). These intermediate representations bridge the gap between how people think about a domain and the languages in which ontologies are formalized. Thus, METHONTOLOGY enables experts and ontology makers unfamiliar with implementation environments to build ontologies from scratch. In this paper, we also present the ODE (Ontology Design Environment) as a software tool to specify ontologies at the knowledge level. ODE allows developers to specify their ontology by filling in tables and drawing graphs. Its multilingual generator module automatically translates the specification of the ontology into target languages

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Computer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008–07029-C02–02.Publicad

Ontological representation of context knowledge for visual data fusion

8 pages, 4 figures.-- Contributed to: 12th International Conference on Information Fusion, 2009 (FUSION '09, Seattle, Washington, US, Jul 6-9, 2009).Context knowledge is essential to achieve successful information fusion, especially at high JDL levels. Context can be used to interpret the perceived situation, which is required for accurate assessment. Both types of knowledge, contextual and perceptual, can be represented with formal languages such as ontologies, which support the creation of readable representations and reasoning with them. In this paper, we present an ontology-based model compliant with JDL to represent knowledge in cognitive visual data fusion systems. We depict the use of the model with an example on surveillance. We show that such a model promotes system extensibility and facilitates the incorporation of humans in the fusion loop.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.Publicad

Dredging impact on the benthic community of an unaltered inlet in southern Spain

The impact of dredging on macrobenthic communities was studied in an unaltered zone, the Getares inlet of Algeciras Bay (SW Spain). The data obtained before, during and after dredging in a time series spanning 5 years revealed the re-establishment of directly affected communities and of physicochemical substrate characteristics within 1 month of the end of dredging, although 2 years later there was a confusing biological impoverishment of the whole inlet. After 4 years, there was a high degree of population re-establishment, both on the bottoms directly affected by the works and on neighbouring areas, that was partly due to the hydrodynamic conditions. Before this type of activity is undertaken, each case should be studied regarding viability, the environmental medium where it will take place, the best time of year, and the type of dredging to be used

Communication in distributed tracking systems: an ontology-based approach to improve cooperation

Current Computer Vision systems are expected to allow for the management of data acquired by physically distributed cameras. This is especially the case for modern surveillance systems, which require communication between components and a combination of their outputs in order to obtain a complete view of the scene. Information fusion techniques have been successfully applied in this area, but several problems remain unsolved. One of them is the increasing need for coordination and cooperation between independent and heterogeneous cameras. A solution to achieve an understanding between them is to use a common and well-defined message content vocabulary. In this research work, we present a formal ontology aimed at the symbolic representation of visual data, mainly detected tracks corresponding to real-world moving objects. Such an ontological representation provides support for spontaneous communication and component interoperability, increases system scalability and facilitates the development of high-level fusion procedures. The ontology is used by the agents of Cooperative Surveillance Multi-Agent System, our multi-agent framework for multi-camera surveillance systems.This work was supported in part by Projects CICYT TIN2008-06742-C02-02=TSI, CICYT TEC2008-06732-C02-02=TEC, CAM CONTEXTS (S2009=TIC-1485) and DPS2008-07029-C02-02.Publicad

Integration of proteomics with transcriptomics and metallomics for the study of environmental issues

Comunicaciones a congreso

Characterization of the bile and gall bladder microbiota of healthy pigs

MicrobiologyOpen published by John Wiley & Sons Ltd. Bile is a biological fluid synthesized in the liver, stored and concentrated in the gall bladder (interdigestive), and released into the duodenum after food intake. The microbial populations of different parts of mammal's gastrointestinal tract (stomach, small and large intestine) have been extensively studied; however, the characterization of bile microbiota had not been tackled until now. We have studied, by culture-dependent techniques and a 16S rRNA gene-based analysis, the microbiota present in the bile, gall bladder mucus, and biopsies of healthy sows. Also, we have identified the most abundant bacterial proteins in the bile samples. Our data show that the gall bladder ecosystem is mainly populated by members of the phyla Proteobacteria, Firmicutes, and Bacteroidetes. Furthermore, fluorescent in situ hybridization (FISH) and transmission electron microscopy (TEM) allowed us to visualize the presence of individual bacteria of different morphological types, in close association with either the epithelium or the erythrocytes, or inside the epithelial cells. Our work has generated new knowledge of bile microbial profiles and functions and might provide the basis for future studies on the relationship between bile microbiota, gut microbiota, and health. © 2014 The Authors.This work was supported by AGL2013-44761-P and AGL2013-41980-P projects from the Ministerio de Ciencia e Innovación (Spain). Borja Sánchez was the recipient of a Ramón y Cajal postdoctoral contract from MINECO.Peer Reviewe

High-Level Information Fusion in Visual Sensor Networks

Information fusion techniques combine data from multiple sensors, along with additional information and knowledge, to obtain better estimates of the observed scenario than could be achieved by the use of single sensors or information sources alone. According to the JDL fusion process model, high-level information fusion is concerned with the computation of a scene representation in terms of abstract entities such as activities and threats, as well as estimating the relationships among these entities. Recent experiences confirm that context knowledge plays a key role in the new-generation high-level fusion systems, especially in those involving complex scenarios that cause the failure of classical statistical techniques –as it happens in visual sensor networks. In this chapter, we study the architectural and functional issues of applying context information to improve high-level fusion procedures, with a particular focus on visual data applications. The use of formal knowledge representations (e.g. ontologies) is a promising advance in this direction, but there are still some unresolved questions that must be more extensively researched.The UC3M Team gratefully acknowledges that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02