2018
Authors
Felix, C; Soares, C; Jorge, A; Ferreira, H;
Publication
Lecture Notes in Computational Vision and Biomechanics
Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters. © 2018, Springer International Publishing AG.
2017
Authors
Ferreira, LL; Albano, M; Silva, J; Martinho, D; Marreiros, G; Orio, GD; Maló, P; Ferreira, HM;
Publication
IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS
Abstract
The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution-embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods- A re already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: The integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system. © 2017 IEEE.
2001
Authors
Hugo Miguel Mendes Ferreira; João José Pinto Ferreira
Publication
CARS&FOF'2001 - 17th ISPE/IFAC International Conference on CAD/CAM, Robotics and Factories of the Future, Durban, South Africa
Abstract
2007
Authors
Hugo Ferreira; António Lucas Soares
Publication
in proceedings of EPIA 2007 - 13th EPIA International Conference - Portuguese Conference on Artifical Intelligence, Guimarães, Portugal
Abstract
Enterprise sponsored virtual communities (ESVC) are emerging as
a serious business schemes fostering, at the intra and
inter-organizational level, collaboration and knowledge sharing. This
community paradigm is taking place among or complementing the more
established forms of inter-organizational interaction such as chains or
networks. ESVCs are complex socio-technical systems, difficult to design
and maintain, needing multi-disciplinary approaches for their
development. This paper presents a case in the development of a
knowledge community support system in the context of an Industrial
Association Group (IAG) in the construction sector. This article
discusses the various complex issues encountered during its design and
development. Hopefully this discussion will be of use to those that are
not only embarking on such projects but also those who research and
develop the tools and frameworks required for the development of
real-life semantic web applications.
2006
Authors
Hugo Ferreira; Diogo Ferreira
Publication
IJCIS - International Journal of Computational Intelligence Systems, vol.15, no.4, pp.485
Abstract
The ability to describe business processes as executable models has always been one of the fundamental premises of workflow management. Yet, the tacit nature of human knowledge is often an obstacle to eliciting accurate process models. On the other hand, the result of process modeling is a static plan of action, which is difficult to adapt to changing procedures or to different business goals. In this article, we attempt to address these problems by approaching workflow management with a combination of learning and planning techniques. Assuming that processes cannot be fully described at build-time, we make use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities and to describe them as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan b
2011
Authors
Hugo Miguel Ferreira
Publication
PhD Thesisvol.0
Abstract
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