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Vrba P.,Czech Technical University | Marik V.,Czech Technical University | Siano P.,University of Salerno | Leitao P.,Polytechnic Institute of Braganca | And 5 more authors.
IEEE Transactions on Industrial Informatics | Year: 2014

The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed. © 2005-2012 IEEE.

Da Silva G.A.,University of Porto | Nogueira P.A.,Artificial Intelligence and Computer Science Laboratory LIACC | Rodrigues R.,INESC Porto
CHI PLAY 2014 - Proceedings of the 2014 Annual Symposium on Computer-Human Interaction in Play | Year: 2014

"Generic Shooter 3000" is a First-Person shooter with semi-realistic interaction, where actions such as firing a gun or diving through underwater sections are performed with your own body- through the use of biofeedback technology. This prototype is the idealised version of a research game developed for a master's thesis project on "biofeedback interaction in video games". © 2014 ACM.

Oliveira J.L.,Artificial Intelligence and Computer Science Laboratory LIACC | Nakamura K.,Honda Corporation | Langlois T.,University of Lisbon | Gouyon F.,INESC Porto | And 5 more authors.
IEEE International Conference on Intelligent Robots and Systems | Year: 2014

In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic environments. In addition, we compare different preprocessing robot audition variants (single channel and separated signal from multiple channels) and test different acoustic models, learned a priori, to tackle multiple noise conditions of increasing complexity in the presence of noises of different natures (e.g., robot motion, speech). The results with six different musical genres suggest improved results, in the order of 43.6pp for the most complex conditions, when recurring to Sound Source Separation and acoustic models trained in similar conditions to the testing scenarios. A robot dance demonstration session confirms the applicability of the proposed integration for genre-adaptive dancing robots in real-world noisy environments. © 2014 IEEE.

Oliveira J.L.,Artificial Intelligence and Computer Science Laboratory LIACC | Oliveira J.L.,INESC Porto | Oliveira J.L.,Kyoto University | Ince G.,Honda Corporation | And 6 more authors.
IEEE International Conference on Intelligent Robots and Systems | Year: 2012

In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a staterecovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e.g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e.g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition. © 2012 IEEE.

Oliveira J.L.,INESC Porto | Oliveira J.L.,Artificial Intelligence and Computer Science Laboratory LIACC | Gouyon F.,INESC Porto | Martins L.G.,Research Center for Science and Technology in Art | Reis L.P.,Artificial Intelligence and Computer Science Laboratory LIACC
Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010 | Year: 2010

This paper describes a tempo induction and beat tracking system based on the efficient strategy (initially introduced in the BeatRoot system [Dixon S., "Automatic extraction of tempo and beat from expressive performances." Journal of New Music Research, 30(1):39-58, 2001]) of competing agents processing musical input sequentially and considering parallel hypotheses regarding tempo and beats. In this paper, we propose to extend this strategy to the causal processing of continuous input data. The main reasons for this are threefold: providing more robustness to potentially noisy input data, permitting the parallel consideration of a number of low-level frame-based features as input, and opening the way to real-time uses of the system (as e.g. for a mobile robotic platform). The system is implemented in C++, permitting faster than real-time processing of audio data. It is integrated in the MARSYAS framework, and is therefore available under GPL for users and/or researchers. Detailed evaluation of the causal and non-causal versions of the system on common benchmark datasets show performances reaching those of state-of-the-art beat trackers. We propose a series of lines for future work based on careful analysis of the results. © 2010 International Society for Music Information Retrieval.

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