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- NILM techniques for intelligent home energy management and ambient assisted living: a reviewPublication . Ruano, Antonio; Hernandez, Alvaro; Ureña, Jesus; Ruano, Maria; Garcia, JuanThe ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
- Estimation and prediction of cloudiness from ground-based all-sky hemispherical digital imagesPublication . Martins, I.; Ferreira, P. M.; Ruano, AntonioCloudiness is the environmental factor most affecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work aims contributing to the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky, and by using radial basis function neural networks to model and predict the estimated cloudiness time-series. The general approach for cloudiness estimation, common to many image processing applications, consists in finding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. The neural network models, selected by means of multiobjective genetic algorithms, are trained as one-step-ahead predictors and used iteratively in order to predict the cloudiness time-series up to a required prediction horizon. In order to allow the evaluation and comparison of image thresholding methods as well as forming a timeseries suitable to train and evaluate neural network models, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures are extracted from the images constituting a feature space of potential inputs. As well as for the cloudiness predictive models, the actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.
- A support vector machine seismic detector for early-warning applicationsPublication . Ruano, Antonio; Madureira, G.; Barros, O.; Khosravani, Hamid Reza; Ruano, M. Graça; Ferreira, P. M.This paper extends a Support Vector Machine (SVM) approach for the detection of seismic events, at the level of a seismic station. In previous works, it was shown that this approach produced excellent results, in terms of the Recall and Specificity measures, whether applied off-line or in a continuous scheme. The drawback was the time taken for achieving the detection, too large to be applied in a Early-Warning System (EWS). This paper shows that, by using alternative input features, a similar performance can be obtained, with a significant reduction in detection time. Additionally, it is experimentally proved that, whether off-line or in continuous operation, the best results are obtained when the SVM detector is trained with data originated from the respective seismic station.
- PVM-based intelligent predictive control of HVAC systemsPublication . Ruano, Antonio; Pesteh, S.; Silva, S.; Duarte, H.; Mestre, G.; Ferreira, P. M.; Khosravani, Hamid Reza; Horta, R.This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in terms of economical savings and thermal comfort, are compared with standard, temperature regulated control.(1) (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.
- Seismic detection using support vector machinesPublication . Ruano, Antonio; Madureira, G.; Barros, O.; Khosravani, Hamid Reza; Ruano, M. Graça; Ferreira, P. M.This study describes research to design a seismic detection system to act at the level of a seismic station, providing a similar role to that of STA/LTA ratio-based detection algorithms. In a first step, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), trained in supervised mode, were tested. The sample data consisted of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network’s stations of the Institute of Meteorology of Portugal (IM). Records’ spectral variations in time and characteristics were reflected in the input ANN patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The proposed system best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favourably with the ones obtained by the existing detection system, 50%, and with other approaches found in the literature. Subsequently, the system was tested in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The classifier presented 88.4% and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. Due to the input features used, the average time taken for detection with this approach is in the order of 100 s. This is too long to be used in an early-warning system. In order to decrease this time, an alternative set of input features was tested. A similar performance was obtained, with a significant reduction in the average detection time (around 1.3 s). Additionally, it was experimentally proved that, whether off-line or in continuous operation, the best results are obtained when the SVM detector is trained with data originated from the respective seismic station.
- Intelligent non-invasive modeling of ultrasound-induced temperature in tissue phantomsPublication . Ferreira, R.; Ruano, M G; Ruano, AntonioRaising temperature of human cells (hyperthermia) is an ancient tool for tumor masses reduction and extinction, actually even before the existence of a molecular understanding of cancer cells. Hyperthermia is being increasingly used for patients' rehabilitation and oncological diseases' treatment but still constitutes a major driver for researching more efficient and reliable therapeutic usage aiming at outstanding patients wellbeing and socio-economic benefits. Efficient hyperthermia practice demands knowledge about the exact amount of heating required at a particular tissue location, as well as information concerning the spatial heating distribution. Both of these processes require accurate characterization. Until now, ultrasound heating treatments are being monitored by magnetic resonance imaging (MRI), recognized as being capable of achieving a 0.5 degrees C/cm(3) temperature resolution [1], thereby imposing a gold standard in this field. However, one can notice that MRI-based techniques, besides the inconvenient instrumental cost, obliges the presence of a team of expert clinicians and limits the hyperthermia ultrasound treatment area due to the space restrictions of an MRI examination procedure. This article introduces a novel noninvasive modelling approach of ultrasound-induced temperature propagation in tissues, to be used as a cost effective alternative to MRI monitoring of ultrasound therapeutic techniques, achieving a maximum temperature resolution of 0.26 degrees C/cm(3), clearly inferior to the MRI gold standard resolution of 0.5 degrees C/cm(3). In order to derive the model, and avoiding painful invasive in-vivo sampling, a phantom was employed, whose composition respects the human tissues' reaction to ultrasound beams. In contrast with previous works of the authors, in the present paper we study the possibility of using b-spline neural networks (BSNN) as reliable noninvasive estimator of temperature propagation in phantoms [2,3]. The proposed methodology achieves better results than previous approaches, does not require the use of an Imaging Ultrasound transducer and, as the proposed models are piecewise polynomial models, they can be easily inverted and used in closed-loop control of therapeutic ultrasound instruments. (C) 2016 Elsevier Ltd. All rights reserved.
- A simple algorithm for convex hull determination in high dimensionsPublication . Khosravani, Hamid Reza; Ruano, Antonio; Ferreira, P. M.Selecting suitable data for neural network training, out of a larger set, is an important task. For approximation problems, as the role of the model is a nonlinear interpolator, the training data should cover the whole range where the model must be used, i.e., the samples belonging to the convex hull of the data should belong to the training set. Convex hull is also widely applied in reducing training data for SVM classification. The determination of the samples in the convex-hull of a set of high dimensions, however, is a time-complex task. In this paper, a simple algorithm for this problem is proposed.
- Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperaturePublication . Gomes, João M.; Ferreira, P. M.; Ruano, AntonioThe aim of this study was to develop an intelligent sensor for acquiring temperature, solar radiation data and estimate cloudiness indexes, and use these measured values to predict temperature and solar radiation in a close future. The prototype produced can ultimately be used in systems related to thermal comfort in buildings and to the efficient and intelligent use of solar energy. To incorporate these functionalities, a small and portable prototype was developed, which consisted in: a CCTV camera with a fish-eye lens, for sky images acquisition; a computer of format mini-itx with a Linux operative system, for data acquisition and processing; a GPS, to enable automatic use, independent of the system’s geographical position; a pyranometer, for regular measurements of solar radiation; a temperature probe, for regular measurements of outdoor temperature; a shadow band, to eliminate the sun’s flare effect on sky images; Arduino, an open source electronics prototyping platform that acquires data from the temperature and solar radiation sensors, as well as processing the data provided by the GPS and controlling the shadow band; neural networks of the type NARX, which use the acquired data to forecast the cloudiness index, solar radiation and temperature, in the next four hours period. The system was programmed to acquire data, both from the sensors and the camera, every five minutes.
- The IMBPC HVAC system: a complete MBPC solution for existing HVAC systemsPublication . Ruano, Antonio; Pesteh, Shabnam; Silva, Sergio; Duarte, Helder; Mestre, Gonçalo; Ferreira, Pedro M.; Khosravani, Hamid Reza; Horta, RicardoThis paper introduces the Intelligent MBPC (IMBPC) HVAC system, a complete solution to enable Model Based Predictive Control (MBPC) of existing HVAC installations in a building. The IMPBC HVAC minimizes the economic cost needed to maintain controlled rooms in thermal comfort during the periods of occupation. The hardware and software components of the IMBPC system are described, with a focus on the MBPC algorithm employed.The installation of IMBPC HVAC solution in a University building is described, and the results obtained in terms of economical savings and thermal comfort obtained are compared with standard, temperature regulated control. (C) 2016 Elsevier B.V. All rights reserved.
- Towards ultrasound hyperthermia safe treatments using computational intelligence techniquesPublication . Ruano, M. Graça; Ruano, AntonioA key feature for safe application of hyperthermia treatments is the efficient delimitation of the treatment region avoiding collateral damages. The efficacy of treatment depends on an ultrasound power intensity profile to accomplish the temperature clinically required. Many hyperthermia procedures proposed in the literature rely on a-priori knowledge of the physical properties of tissue. The soft computing models presented in this article are only based on measured data, collected from tissue phantoms reflecting the reactions of human tissues to ultrasounds. From homogeneous to heterogeneous tissues, different soft computing techniques were developed accordingly to experimental constraints. The present state of development is nearly approaching the identification of a computational model to be safety applied in in-vivo hyperthermia sessions.