Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

2017

Special Issue on Autonomous Driving and Driver Assistance Systems

Autores
Santos, V; Sappa, AD; Oliveira, M;

Publicação
ROBOTICS AND AUTONOMOUS SYSTEMS

Abstract

2017

Model-independent comparison of simulation output

Autores
Fachada, N; Lopes, VV; Martins, RC; Rosa, AC;

Publicação
SIMULATION MODELLING PRACTICE AND THEORY

Abstract
Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers' work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model.

2017

FEUP at TREC 2017 OpenSearch Track Graph-Based Models for Entity-Oriented

Autores
Devezas, JL; Lopes, CT; Nunes, S;

Publicação
Proceedings of The Twenty-Sixth Text REtrieval Conference, TREC 2017, Gaithersburg, Maryland, USA, November 15-17, 2017

Abstract

2017

Autonomous Systems in remote areas of the Ocean using BLUECOM plus communication network

Autores
Ferreira, H; Silva, F; Sousa, P; Matias, B; Faria, A; Oliveira, J; Almeida, JM; Martins, A; Silva, E;

Publicação
OCEANS 2017 - ANCHORAGE

Abstract
The authors present a series of sea trails with autonomous systems using a long-range communication network. The continuous monitoring of the oceans and realtime data gathering/monitoring is a key issue in future marine challenges. To have long range communication, between land and ships at tens of kilometers', the authors used the BlueCom+ project research trials and tested their robotic systems. Bluecom+ project intends to fill the gap of long range communication with high bandwidth. It was demonstrated the usefulness of the system using autonomous systems, such as a small unmanned vehicle (ROAZ USV) for bathymetric mapping and tested an underwater acoustic positioning and communications system. © 2017 Marine Technology Society.

2017

COUPLED HIDDEN MARKOV MODEL FOR AUTOMATIC ECG AND PCG SEGMENTATION

Autores
Oliveira, J; Sousa, C; Coimbra, MT;

Publicação
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

Abstract
Automatic and simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) segmentation is a good example of current challenges when designing multi-channel decision support systems for healthcare. In this paper, we implemented and tested a Montazeri coupled hidden Markov model (CHMM), where two HMM's cooperate to recreate the "true" state sequence. To evaluate its performance, we tested different settings (two fully connected and two partially connected channels) on a real dataset annotated by an expert. The fully connected model achieved 71% of positive predictability (P+) on the ECG channel and 67% of P+ on the PCG channel. The partially connected model achieved 90% of P+ on the ECG channel and 80% of P+ in the PCG channel. These results validate the potential of our approach for real world multichannel application systems.

2017

Scalable computational framework using intelligent optimization: Microgrids dispatch and electricity market joint simulation

Autores
Soares, J; Pinto, T; Sousa, F; Borges, N; Vale, Z; Michiorri, A;

Publicação
IFAC PAPERSONLINE

Abstract
Worldwide microgrid capacity is expected to reach 7 GW and a market value of $35 billion dollars in the next few years. The decentralization of the generation dispatch role and different ownership models concerning microgrids, will contribute to increase the complexity of the future power systems. Analyzing new policies and strategies as well as evaluating those impacts is only possible with the use of sophisticated simulation tools. This paper presents a scalable computational simulation to address microgrid dispatch and the impact in the electricity market. Computational intelligence techniques are integrated to improve the effectiveness of the simulation tool. These techniques include CPLEX; differential search algorithm and quantum particle swaiin optimization. Each microgrid player is able to solve a day-ahead scheduling problem and submit bids to the electricity market agent (spot market), which calculates the market clearing price. The developed case study with a large number of players totaling about 150,000 consumers suggest the relevance of the developed computational framework.

  • 1917
  • 4198