96,880 research outputs found
A normative approach to multi-agent systems for intelligent buildings
Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to
provide high-quality building services, and reduce the running cost of the building. However, most BMS are
functionality-oriented and do not consider user personalization. The aim of this research is to capture and
represent building management rules using organizational semiotics methods. We implement Semantic
Analysis, which determines semantic units in building management and their relationship patterns of
behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these
management actions occur. Finally, we propose a multi-agent framework for norm based building
management. This framework contributes to the design domain of intelligent building management system
by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building
Development of an integrated low-power RF partial discharge detector
This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined
Intelligent energy buildings based on RES and Nanotechnology
The paper presents the design features, the energy modelling and optical performance details of two pilot Intelligent Energy Buildings, (IEB). Both are evolution of the Zero Energy Building (ZEB) concept. RES innovations backed up by signal processing, simulation models and ICT tools were embedded into the building structures in order to implement a new predictive energy management concept. In addition, nano-coatings, produced by TiO2 and ITO nano-particles, were deposited on the IEB structural elements and especially on the window panes and the PV glass covers. They exhibited promising SSP values which lowered the cooling loads and increased the PV modules yield. Both pilot IEB units were equipped with an on-line dynamic hourly solar radiation prediction model, implemented by sensors and the related software to manage effectively the energy source, the loads and the storage or the backup system. The IEB energy sources covered the thermal loads via a south façade embedded in the wall and a solar roof which consists of a specially designed solar collector type, while a PV generator is part of the solar roof, like a compact BIPV in hybrid configuration to a small wind turbine
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
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