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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.
- Evaluation of the influence of large temperature variations on the grey level content of B-mode imagesPublication . Alvarenga, A. V.; Teixeira, César; Ruano, M. Graça; Pereira, WagnerIn this work, the variation of the grey-level content of B-Mode images is assessed, when the medium is subjected to large temperature variations. The goal is to understand how the features obtained from the grey-level pattern can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). Herein, B-Mode images were collected from a tissue mimic phantom heated in a water bath. Entropy was extracted from image Grey-Level Co-occurrence Matrix, and then assessed for non-invasive temperature estimation. During the heating period, the average temperature varies from 27oC to 44oC, and entropy values were capable of identifying variations of 2.0oC. Besides, it was possible to quantify variations in the range from normal human body temperature (37oC) to critical values, as 41oC. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.
- A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequencesPublication . Teixeira, Cesar A.; Mendes, Luis; Graca Ruano, Maria; Pereira, Wagner C. A.In this paper, we propose a new method for sub-sample computation of time displacements between two sampled signals. The new algorithm is based on sampled auto- and cross-correlation sequences and takes into account only the sampled signals without the need for the customary interpolation and fitting procedures. The proposed method was evaluated and compared with other methods, in simulated and real signals. Four other methods were used for comparison: two based on cross-correlation plus fitting, one method based on spline fitting over the input signals, and another based on phase demodulation. With simulated signals, the proposed approach presented similar or better performance, concerning bias and variance, in almost all the tested conditions. The exception was signals with very low SNRs (<10 dB), for which the methods based on phase demodulation and spline fitting presented lower variances. Considering only the two methods based on cross-correlation, our approach presented improved results with signals with high and moderate noise levels. The proposed approach and other three out of the four methods used for comparison are robust in real data. The exception is the phase demodulation method, which may fail when applied to signals collected from real-world scenarios because it is very sensitive to phase changes caused by other oscillations not related to the main echoes. This paper introduced a new class of methods, demonstrating that it is possible to estimate sub-sample delay, based on discrete cross-correlations sequences without the need for interpolation or fitting over the original sampled signals. The proposed approach was robust when applied to real-world signals and presented a moderated computational complexity when compared to the other tested algorithms. Although the new method was tested using ultrasound signals, it can be applied to any time-series with observable events. (C) 2016 Elsevier Ltd. All rights reserved.
- 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.
- 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.
- 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.
- On the possibility of non-invasive multilayer temperature estimation using soft-computing methodsPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, Antonio; Ruano, M. GraçaObjective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics e.g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. Novelty aspects: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. Materials and methods: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar– agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities ðISATAÞ: 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2:0W=cm2. Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. Results and discussion: At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 C. The best-fitted estimator presents a MAE of only 0.4 C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time.
- On the use of artificial neural networks for biomedical applicationsPublication . Ruano, M. Graça; Ruano, AntonioArtificial Neural Networks (ANN) are being extensively used in many application areas due to their ability to learn and generalize from data, similarly to a human reaction. This paper reports the use of ANN as a classifier, dynamic model, and diagnosis tool. The examples presented include blood flow emboli classification based on transcranial ultrasound signals, tissue temperature modeling based on imaging transducer’s raw data and identification of ischemic cerebral vascular accident areas based on computer tomography images. In all case studies the performance of ANN proves to produce very accurate results, encouraging the more frequent use of these computational intelligent techniques on medical applications.
- On-line operation of an intelligent seismic detectorPublication . Madureira, G.; Ruano, Antonio; Ruano, M. GraçaThis study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA / LTA ratio- based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting 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 SVM input 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. After having been trained, the proposed system was experimented 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 same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM.
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