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

Publicações por Davide Rua Carneiro

2013

A Statistical Classifier for Assessing the Level of Stress from the Analysis of Interaction Patterns in a Touch Screen

Autores
Carneiro, D; Novais, P; Gomes, M; Oliveira, PM; Neves, J;

Publicação
SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS

Abstract
This paper describes an approach for assessing the level of stress of users of mobile devices with tactile screens by analysing their touch patterns. Two features are extracted from touches: duration and intensity. These features allow to analyse the intensity curve of each touch. We use decision trees (J48) and support vector machines (SMO) to train a stress detection classifier using additional data collected in previous experiments. This data includes the amount of movement, acceleration on the device, cognitive performance, among others. In previous work we have shown the co-relation between these parameters and stress. Both algorithms show around 80% of correctly classified instances. The decision tree can be used to classify, in real time, the touches of the users, serving as an input to the assessment of the stress level.

2019

Preface

Autores
Novais, P; Jung, JJ; Villarrubia, G; Fernández Caballero, A; Navarro, E; González, P; Carneiro, D; Pinto, A; Campbell, AT; Duraes, D;

Publicação
Advances in Intelligent Systems and Computing

Abstract

2018

A Decision-Support System for Preventive Maintenance in Street Lighting Networks

Autores
Carneiro, D; Nunes, D; Sousa, C;

Publicação
Hybrid Intelligent Systems - 18th International Conference on Hybrid Intelligent Systems, HIS 2018, Porto, Portugal, December 13-15, 2018

Abstract
An holistic approach to decision support systems for intelligent public lighting control, must address both energy efficiency and maintenance. Currently, it is possible to remotely control and adjust luminaries behaviour, which poses new challenges at the maintenance level. The luminary efficiency depends on several efficiency factors, either related to the luminaries or the surrounding conditions. Those factors are hard to measure without understanding the luminary operating boundaries in a real context. For this early stage on preventive maintenance design, we propose an approach based on the combination of two models of the network, wherein each is representing a different but complementary perspective on the classifying of the operating conditions of the luminary as normal or abnormal. The results show that, despite the expected and normal differences, both models have a high degree of concordance in their predictions. © 2020, Springer Nature Switzerland AG.

2018

The influence of external factors on the energy efficiency of public lighting

Autores
Carneiro, D; Sousa, C;

Publicação
Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao

Abstract
LED-based technology is transforming public lighting networks, favouring smart city innovations. Beyond energy efficiency benefits, LED-based luminaries provide real time stateful data. However, most of the municipalities manage all their luminaries equally, independently of its state or the environmental conditions. Some existing approaches to street lighting management are already considering elementary features such as on-off control and individual dimming based on movement or ambient light. Nevertheless, our vision on public (street) lighting management, goes beyond basic consumption monitoring and dimming control, encompassing: a) adaptive lighting, by considering other potential influence factors such as work temperature of the luminaries or the arrangement of the luminaries on the street; b) Colour tuning, for public safety purposes and; c) emergency behaviour control. This paper addresses the first component (adaptive lighting) influence factors, in the scope of a real scenario in a Portuguese municipality.

2020

Decision Intelligence in Street Lighting Management

Autores
Nunes, D; Teixeira, D; Carneiro, D; Sousa, C; Novais, P;

Publicação
Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020, Budva, Montenegro, 7-10 April 2020.

Abstract
The European Union has been making efforts to increase energy efficiency within its member states, in line with most of the industrialized countries. In these efforts, the energy consumed by public lighting networks is a key target as it represents approximately 50% of the electricity consumption of European cities. In this paper we propose an approach for the autonomous management of public lighting networks in which each luminary is managed individually and that takes into account both their individual characteristics as well as ambient data. The approach is compared against a traditional management scheme, leading to a reduction in energy consumption of 28%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

2022

Knowledge-based decision intelligence in street lighting management

Autores
Sousa, C; Teixeira, D; Carneiro, D; Nunes, D; Novais, P;

Publicação
INTEGRATED COMPUTER-AIDED ENGINEERING

Abstract
As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

  • 1
  • 22