GREC - Grup de Recerca en Enginyeria del Coneixement
El Grup de Recerca en Enginyeria del Coneixement (GREC) fonamenta la seva activitat en la recerca i desenvolupament de tecnologies dins de l'àrea de la Intel·ligència Artificial, centrant-se en aspectes relatius al raonament qualitatiu, l'aprenentatge automàtic i els sistemes de decisió multicritèria. La finalitat del GREC és l'estudi i la recerca sobre sistemes basats en el coneixement i la seva natura qualitativa, tàcita o intangible.
El grup GREC impulsa una recerca interdisciplinària que incorpora àrees d'aplicació de rellevància social que suposin un augment de la qualitat de vida d'éssers, entorns i comunitats. En formació i difusió de coneixement, el GREC participa en programes de màster, formació doctoral, organització i participació en congressos, i publicació de resultats científics.
La nostra visió s'assoleix a través de continuar formant investigadors a nivell internacional en entorns de treball creatius i innovadors.
The Knowledge Engineering Research Group (GREC) was established in 1991 as a highly multidisciplinary research team. Most of the GREC¿s researchers work at two universities, the Technical University of Catalonia and Ramon Llull University, although they collaborate actively with researchers from the LAAS-CNRS and IIIA-CSIC research groups. The group¿s main fields of study are the theory and application of learning systems and qualitative reasoning. The group is involved in researching and developing ubiquitous soft computing technologies in areas of application that lead to the improvement of the quality of life of people and communities. The group¿s specific objectives are the following: 1. The study and development of computational paradigms with the aim of merging soft computing and pervasive computing paradigms. Soft computing techniques have proved useful in several fields, but the focus has not yet been on problems in which the improvement of people¿s lives is the greatest concern. It is in this respect that a combination of soft computing and pervasive computing techniques might prove particularly useful, because of the latter¿s distributed and intangible nature. 2. The application of technologies. Only by merging software and hardware technologies is it possible to develop the aforementioned paradigms. The GREC aims to analyse how to merge these technologies in order to design computational intelligence. Possible areas of application are those in which a community or environment requires solutions to improve quality of life, such as, for example, smart buildings, environmental control, the improvement of sustainability indicators, market research, etc. 3. Teaching and the dissemination of knowledge in the university community and society in general. To this end, the group collaborates on Master¿s and PhD courses with Ramon Llull University. 4. Technology transfer. The main objective of contact with industry is transferring the knowledge generated by the group¿s research. A further aim in developing computational intelligence paradigms is the design of electronic and computing devices that do not intrude on people¿s everyday lives and the environment. The user¿s social environment is also considered, as the approach to computational intelligence in a technological society must be both adaptive and adaptable.
The Knowledge Engineering Research Group (GREC) was established in 1991 as a highly multidisciplinary research team. Most of the GREC¿s researchers work at two universities, the Technical University of Catalonia and Ramon Llull University, although they collaborate actively with researchers from the LAAS-CNRS and IIIA-CSIC research groups. The group¿s main fields of study are the theory and application of learning systems and qualitative reasoning. The group is involved in researching and developing ubiquitous soft computing technologies in areas of application that lead to the improvement of the quality of life of people and communities. The group¿s specific objectives are the following: 1. The study and development of computational paradigms with the aim of merging soft computing and pervasive computing paradigms. Soft computing techniques have proved useful in several fields, but the focus has not yet been on problems in which the improvement of people¿s lives is the greatest concern. It is in this respect that a combination of soft computing and pervasive computing techniques might prove particularly useful, because of the latter¿s distributed and intangible nature. 2. The application of technologies. Only by merging software and hardware technologies is it possible to develop the aforementioned paradigms. The GREC aims to analyse how to merge these technologies in order to design computational intelligence. Possible areas of application are those in which a community or environment requires solutions to improve quality of life, such as, for example, smart buildings, environmental control, the improvement of sustainability indicators, market research, etc. 3. Teaching and the dissemination of knowledge in the university community and society in general. To this end, the group collaborates on Master¿s and PhD courses with Ramon Llull University. 4. Technology transfer. The main objective of contact with industry is transferring the knowledge generated by the group¿s research. A further aim in developing computational intelligence paradigms is the design of electronic and computing devices that do not intrude on people¿s everyday lives and the environment. The user¿s social environment is also considered, as the approach to computational intelligence in a technological society must be both adaptive and adaptable.
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Articles de revista [95]
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Llibres [2]
Recent Submissions
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A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: an application to assess entrepreneurial competencies in secondary schools
(2021-11-01)
Article
Restricted access - confidentiality agreementAdvances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed ... -
Finding the most sustainable wind farm sites with a hierarchical outranking decision aiding method
(2022-05-02)
Article
Open AccessThis paper considers the problem of finding suitable sites for wind farms in a region of Catalonia (Spain). The evaluation criteria are structured into a hierarchy that identifies several intermediate sub-goals dealing ... -
Hesitancy and consensus measures to understand ratings: an application to hotel recommendations
(2018)
Conference lecture
Restricted access - publisher's policyWhen searching for recommendations online, users are often presented with similar items all having the same ratings. It is left to the user to discern which rated item is best from other information. In order to facilitate ... -
Cognitive human factors in the artificial intelligence of things
(2022)
Conference report
Open AccessInternet of Things (IoT) systems are increasingly becoming complex. Heterogeneity in terms of hardware, software, computing capacity and connectivity is a source of complexity. The conversion of IoT systems into cyber-physical ... -
Initial test of "BabyRobot" behaviour on a teleoperated toy substitution: improving the motor skills of toddlers
(2022)
Conference report
Open AccessThis article introduces “Baby Robot”, a robot designed to improve infants’ and toddlers’ motor skills. This robot is a car-like toy that moves autonomously by using reinforcement learning and computer vision. Its behaviour ... -
Autoencoders for semi-supervised water level modeling in sewer pipes with sparse labeled data
(2022-01-24)
Article
Open AccessMore frequent and thorough inspection of sewer pipes has the potential to save billions in utilities. However, the amount and quality of inspection are impeded by an imprecise and highly subjective manual process. It ... -
Operational modes detection in industrial gas turbines using an ensemble of clustering methods
(2021-12-01)
Article
Open AccessOperational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the ... -
Machine-learning-based condition assessment of gas turbine: a review
(2021-12-15)
Article
Open AccessCondition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the ... -
Generative adversarial networks for anonymized healthcare of lung cancer patients
(2021-09-01)
Article
Open AccessThe digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination ... -
Usability study through a human-robot collaborative workspace experience
(Multidisciplinary Digital Publishing Institute, 2021-05-28)
Article
Open AccessThe use of collaborative robots (cobots) in industrial and academic settings facilitates physical and cognitive interaction with operators. This framework is a challenge to determine how measures on concepts, such as ... -
Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task
(2021-05-31)
Article
Open AccessIn human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, ... -
Condition assessment of industrial gas turbine compressor using a drift soft sensor based in autoencoder
(Multidisciplinary Digital Publishing Institute (MDPI), 2021-04-12)
Article
Open AccessMaintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks ...