Antonio Ruano

Universidade do Algarve | UALG · Departamento de Engenharia Eletrónica e Informática

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of r...

The 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 a...

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target’s...

In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by...

In any online adaptation scheme, two important phenomena should be taken into consideration; parameter shadowing and parameter interference. To alleviate these problems, in this paper a sliding window based online adaptation method for fixed-structure Radial Basis Function Neural Networks (RBFNNs) is proposed. The method is capable of updating the...

Electricity disaggregation is the process of separating the total electrical load of a single household into appliance specific loads. This can be achieved either by intrusive monitoring of each appliance via individual device load meters or by employing Non-Intrusive Load Monitoring (NILM) techniques, that perform a detailed analysis of the curren...

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despi...

In this work the control of HVAC systems based on PMV index, in university buildings with complex topology, is numerically developed. A numerical software that simulates the building thermal behavior and the occupant thermal comfort, in transient thermal conditions, is applied. This numerical software is based in energy and mass balance integral eq...

In any online adaptation scheme, two important phenomena should be taken into consideration; parameter shadowing and parameter interference. To alleviate these problems, in this paper a convex hull, sliding window based online adaptation method for fixed-structure Neural Networks is proposed. The method is capable of dealing with the two phenomena,...

GPR is an electromagnetic remote sensing technique, used for detection of relatively small objects in high noise environments. Data inversion requires a fitting procedure of hyperbola signatures, which represent the target reflections, sometimes producing bad results due to high resolution of GPR images. The idea proposed in this paper consists of...

Model Based Predictive Control (MBPC) is perhaps the most proposed technique for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers in this topic during the last years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and,...

A better management of the Heating, Ventilating and Air Conditioning (HVAC) systems and the integration of renewable energies are two ways to get a Net Zero Energy Buildings (NZEB). Thus, methods to predict the Electrical Load Demand (ELD) for the HVAC system are extremely important, to reach this goal. This paper describes the development and asse...

In this paper, we propose to employ a radial basis function network, designed by a multi-objective genetic algorithm, to solve a two-class classification of Ground penetrating radar signatures problem. The features used in this study are high order statistics that are widely used in the biomedical field. The proposed approach gives promising result...

In any online adaptation scheme, two important phenomena should be taken into consideration; parameter shadowing and parameter interference. To alleviate these problems, in this paper a sliding window based online adaptation method for fixed-structure Radial Basis Function Neural Networks (RBFNNs) is proposed. The method is capable of dealing with...

The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of four unsupervised data selection methods including random, convex hull based, entropy based and a hybrid data selection method. These...

The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of four unsupervised data selection methods including random, convex hull based, entropy based and a hybrid data selection method. These...

Searching for similarity between time series plays an important role when large amounts of information need to be clustered to integrate intelligent supported personal health care diagnosis systems. The performance of classification, clustering and disease prediction are influenced by the prior stage where similarity between time series is performe...

Objective This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determin...

The goal of maintaining users’ thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users’ productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of...

The authors would like to acknowledge the support of QREN SIDT 38798 project, University of Algarve grant no 032/2015 and FCT through IDMEC, under LAETA grant UID/EMS/50022/2013.

Efficient hyperthermia therapy session requires knowledge of the exact amount of heating needed at a particular tissue location and how it propagates around the area. Until now, ultrasound heating treatments are being monitored by Magnetic Resonance Imaging (MRI) which, besides raising the treatment instrumental cost, requires the presence of a tea...

The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models, covering the whole input range in which they will be employed. The convex hull algorithm can be applied as a method for data selection; howe...

Recent studies into the estimation and control of an activated sludge process (ASP) at a wastewater treatment plant suggest that artificial-intelligence methods, such as neural networks, fuzzy systems and genetic algorithms, can improve the plant performance in terms of reduced operation costs and improved effluent quality. In this paper, a neural-...

A 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 knowle...

This 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...

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy con...

The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models that covers the whole input ranges in which they will be employed. The convex hull algorithm is applied as a method for data selection; howev...

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent,...

Existence of Cerebral Vascular Accident (CVA) can affect the symmetrical property of human brain, observable on Computer Tomography (CT) images. Analysis of symmetry features is hereby proved to promote the accuracy of classifiers designed for automatic detection of CVA. We attained a reduction up to 40.4% of the number of false detections consider...

Design of a neural network classifier involves selection of input features and a network structure from a very large search space, preferably respecting the problem's constraints. Most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at the...

This paper improves an existing Model Based Predictive Control Approach (MBPC), applied for Heating Ventilation and Air Conditioning (HVAC) control in buildings. The existing approach uses the Predictive Mean Vote (PMV) to assess thermal comfort. It has been found that PMV estimation and forecasts deteriorate when the room is occupied. In order to...

Accurate measurements of global solar radiation and atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intellige...

This position statement discusses the properties that an intelligent system should exhibit, and points out possible directions for intelligent control, coming from cognitive psychology and neuroscience.

This paper addresses the problem of controlling Heating Ventilation and Air Conditioning (HVAC) systems with the purpose of maintaining a desired thermal comfort level, whilst minimizing the electrical energy required. Using a pilot installation, in the University of Algarve, Portugal, a Model Based Predictive Control (MBPC) strategy is used to con...

This milestone report addresses first the position of the areas of computers, computational intelligence and communications within IFAC. Subsequently, it addresses the role of computational intelligence in control. It focuses on four topics within the Computational intelligence area: neural network control, fuzzy control, reinforcement learning and...

This Milestone Report addresses first the position of the areas of computers, computational intelligence and communications within IFAC. Subsequently, it addresses the role of computational intelligence in control. It focuses on four topics within the Computational Intelligence area: neural network control, fuzzy control, reinforcement learning and...

This paper addresses the problem of controlling Heating Ventilation and Air Conditioning (HVAC) systems with the purpose of maintaining a desired thermal comfort level, whilst minimizing the electrical energy required. Using a pilot installation, in the University of Algarve, Portugal, a Model Based Predictive Control (MBPC) strategy is used to con...

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. C...

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 ach...

The subject of this paper is the multi-step prediction of the Portuguese electricity consumption profile up to a 48-hour prediction horizon. In previous work on this subject, the authors have identified a radial basis function neural network one-step-ahead predictive model, which provides very good prediction accuracy and is currently in use at the...

The final goal of this work is to create an intelligent support system which assists neuroradiologists to identify Cerebral Vascular Accidents in less time, more precisely. For this purpose, the first step was the creation of a web based tool for registering pathological areas in CT images, which will allow to collect required data for training and...

The authors would like to correct the acknowledgements of this article [1] as follows:

Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are importantfor different areas of applications, such as agriculture, renewable energy and energymanagement, or thermal comfort in buildings. For this reason, an intelligent, light-weightand porta...

The paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. Thi...

Heating, Ventilating and Air Conditioning (HVAC) systems are used to provide adequate comfort to occupants of spaces within buildings. One important aspect of comfort, the thermal sensation, is commonly assessed by computation of the Predicted Mean Vote (PMV) index. Model-based predictive control may be applied to HVAC systems in existing buildings...

The paper addresses the problem of controlling an heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the...

When used for function approximation purposes, neural networks belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of parameter separability can also be applied when the training problem is formulated as the minimization of the integral of the (funct...

When used for function approximation purposes, neural networks belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of parameter separability can also be applied when the training problem is formulated as the minimization of the integral of the (funct...

The problem of controlling a heating ventilating and air conditioning system in a single zone of a building is addressed. Its formulation is done in order to maintain acceptable thermal comfort for the occupants and to spend the least possible energy to achieve that. In most operating conditions these are conflicting goals, which require some sort...

Artificial 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 ultra...

This 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...

When used for function approximation purposes, neural networks and neuro-fuzzy systems belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of parameter separability can also be applied when the training problem is formulated as the minimization of th...

This study discusses site-specific system optimization efforts related to the capability of a coastal video station to monitor intertidal topography. The system consists of two video cameras connected to a PC, and is operating at the meso-tidal, reflective Faro Beach (Algarve coast, S. Portugal). Measurements from the period February 4, 2009 to May...

The 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...

In previous work the authors successfully identified a radial basis function neural network to forecast the Portuguese electricity consumption profile within a 48 hour predictive horizon. As the model is a static mapping employing external dynamics and the electricity consumption trends and dynamics are varying with time, its predictive performance...

This paper investigates the application of a novel approach for the parameter estimation of a Radial Basis Function (RBF) network model. The new concept (denoted as functional training) minimizes the integral of the analytical error between the process output and the model output [1]. In this paper, the analytical expressions needed to use this app...

When used for function approximation purposes, neural networks belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. In this work we extend this concept to the case where the training problem is formulated as the minimization of the integral of the squared error, a...

In the system identification context, neural networks are black-box models, meaning that both their parameters and structure need to be determined from data. Their identification is often done iteratively in an ad-hoc fashion focusing the first aspect. Frequently the selection of inputs, model structure, and model order are underlooked subjects by...

The current volume “New Advances in Intelligent Signal Processing” contains extended works based on a careful selection of papers presented originally at the jubilee sixth IEEE International Symposium on Intelligent Signal Processing (WISP’2009), held in Budapest Hungary, August 26-28, 2009 - celebrating the 10years anniversary of the WISP event se...

B-spline Neural Networks (BSNNs) belong to the class of networks termed grid or lattice-based associative memories networks (AMN). The grid is a key feature since it allows these networks to exhibit relevant properties which make them efficient in solving problems namely, functional approximation, non-linear system identification, and on-line contr...

The Portuguese power grid company wants to improve the accuracy of the electricity load demand forecast within an horizon of 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present updated results on the identi�cation of radial basis function neural network load demand predictive models. Th...

Cloudiness is the non-predictable factor most a�ecting 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 represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digi...

In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope with the possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy model based operation proved to be very advantageous in power plant control where the high complexity, nonlinearity, and possible par...

Cloudiness 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 imag...

The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. One of these applications is predictive HVAC control, which aims to maintain thermal comfort while simultaneously minimizing the energy spent, within a specified prediction horizon. Therm...

Online learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by using a sliding-window policy that enforces the n...

This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted f...

Objective 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 br...

This volume contains the proceedings of the Fourth Colloquium on International Engineering Education, which took place in May 20, 2009 at San Diego State University, California. The transatlantic consortium “International Cooperation in Ambient Computing Education” (ICACE) organized the colloquium. The consortium, which was established to create a...

In this paper a temperature control model used in heating, Ventilating and air-conditioning (HVAC) systems in school spaces, in Mediterranean climate, is developed. This empirical model considers the indoor preferred environmental temperature, the outdoor environmental temperature and the adaptation to the seasons of the year and to the spaces.In t...

In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient-based learning...