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Pinto J.P.,INESC TEC | Viana P.,Polytechnic Institute of Porto
ACM International Conference Proceeding Series

This short paper presents a game for collecting metadata to describe video content. Tags, introduced by registered players on a given timecode of the video, are collected and validated based on a collaborative scoring mechanism that excludes erratic annotations. The system follows a gamification approach for motivating users and includes processes for semantically relating concepts. © 2015 ACM. Source

Lima S.L.,Federal University of Maranhao | Saavedra O.R.,Federal University of Maranhao | Miranda V.,INESC TEC | Miranda V.,University of Porto
IEEE Transactions on Power Delivery

Power transformers are important equipment of a substation from the generation, transmission, and distribution of electricity to end users. The costs associated with purchasing a power transformer in the voltage class of 500 kV (100 MVA) are a few million. The fines imposed by regulatory agencies are significant when there is unavailability of equipment for any defect or failure. Therefore, energy companies have been struggling in preventive and predictive maintenance in order to maintain this equipment in an effective maintenance program, avoiding the occurrence of failures. There are various techniques that are utilized for diagnosis and analysis of transformer failure, but little has been discussed about mechanisms that assist in decision making when it is necessary to overload the transformer, especially in emergency situations. In this paper, we present a framework that unifies the step of fault diagnosis of power transformers with the process of decision making, considering the current operating conditions as well as the life of the equipment. The assistance to the decision-making methodology is based on risk analysis with indicators derived from the failure rate and Arrhenius theory. The validation of the method is performed with a case study using data from a utility. © 1986-2012 IEEE. Source

Carvalho L.,University of Porto | Campos R.,INESC TEC | Ricardo M.,University of Porto
2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013

Typical sensor networks are formed by low-end, battery operated devices, which rely on low-energy communication technologies, such as Bluetooth, Zigbee and ANT+, due to their energy efficiency. On the other hand, sensor networks increasingly need to be connected to the Internet, which implies adaptations of the TCP/IP stack to fit such wireless technologies. These adaptations bring additional complexity and imply new hardware, thus deployments are cumbersome and sub-optimal. Conversely, Wi-Fi is ubiquitous, can be seamlessly integrated with TCP/IP, and is energy-efficient with the right configurations; yet, its usage is still uncommon in e-health scenarios. For these reasons, we argue that a TCP/IP over Wi-Fi approach should be followed in e-health sensor networks. We propose a novel cross-layer, context-aware network configuration mechanism, which monitors the user and networking contexts and optimizes the configuration of the TCP/IP protocol stack accordingly. Our approach enables seamless integration between e-health wireless sensor networks and the TCP/IP backbone, while improving energy efficiency and reliability. © 2013 IEEE. Source

Cota M.P.,University of Vigo | Thomaschewski J.,Emden Leer University of Applied Sciences | Schrepp M.,SAP | Goncalves R.,INESC TEC
Procedia Computer Science

For the international use of software products it is important to know the culture, language and behavior of the citizens. Means: internationalization and location. To be able to evaluate these products it is significant to know how people from a country behave and express their feelings. This article presents a questionnaire which was initially developed in German, English and Spanish and is now available after a complex transformation in Portuguese. © 2013 The Authors. Published by Elsevier B.V. Source

Miranda V.,INESC TEC | Miranda V.,University of Porto | Castro A.R.G.,Federal University of Para | Lima S.,Federal University of Maranhao
IEEE Transactions on Power Delivery

This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy of 100% is achieved with this architecture, in a validation data set using all real information available. © 2012 IEEE. Source

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