Eduardo Giraldo

Eduardo Giraldo
Universidad Tecnológica de Pereira | UTP · Faculty of Electric Engineering, Electronics and Computer Systems

PhD

About

89
Publications
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Introduction
Skills and Expertise

Publications

Publications (89)
Article
Full-text available
In the last years, control theory has applied fractional calculus, and the results are unquestionable. The classic PI controllers have been modified with fractional calculus creating the Fractional Order Proportional-Integral (FOPI). The FOPI controllers have performed better than conventional PI because they provide more flexibility in the control...
Article
Full-text available
In this work, a novel real-time decentralized control approach of a Hardware-In-the-Loop (HIL) microgrid is presented. A state-space feedback control signal is designed based on an optimal Linear Quadratic Regulator control. The states of the microgrid are estimated using a Kalman-Bucy observer. In order to evaluate the performance of the proposed...
Article
This article presents an optimal tuning method for coupled Fuzzy multivariable controllers. The procedure is based on optimization using genetic algorithms of a square error cost function. The controllers tuning method can be applied to the design of systems required to meet design criteria, taking into account the system’s inherent dynamics. The p...
Article
Full-text available
Detection of risky driving events using smartphone-based sensing is a growing technology devoted to impact positively driving behaviors. This technology might improve traffic and reduce the number of car accidents. However, data measured from inbuilt smartphone sensors, represented as a multivariate time series, commonly contains strong temporal dy...
Article
Full-text available
In this paper, a novel methodology that automatically identifies segments of EEG recordings with epileptic activity based on multi-rate adaptive filter banks is presented. As an advantage, the proposed approach accurately tracks parameters variability in the specific frequency band of each filter according to its energy in the spectrogram. To this...
Article
Full-text available
In this article a novel adaptive control approach of microgrids is presented, where a robust estimation is performed based on a linear ARMAX model. In addition, an adaptive Linear Quadratic Regulator is used for optimal control based on an extended state space approach to compute the control signal. It is noticeable that the dynamical models of the...
Article
Full-text available
Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information t...
Article
In this paper, a novel method for parallel dynamic state estimation of large scale systems is presented. Since this task requires a high amount of computational resources, a novel solution is presented based on a minimization problem, including spatial and temporal constraints solved with a parallel proximal dual approach. In order to evaluate the...
Article
In this article is presented a novel strategy for multi-objective optimal control of resources in a power distribution system for service restoration. The distribution system is modeled as a discrete Markov chain in state space by considering the probability of failure and operation of a node. A multi-objective cost function is proposed by consider...
Article
This paper proposes a comparative analysis between regular and parallel versions of FISTA and Tikhonov-like optimizations for solving the EEG brain mapping problem. Such comparison is performed in terms of computational time reduction and estimation error achieved by the parallelized methods. Two brain models (high- and low-resolution) are used to...
Conference Paper
Full-text available
Brain-computer interface (BCI) applications are characterized by real-time feature extraction and classification. Thus, brain activity reconstruction is not generally performed due to the long computation times required to estimate brain activity. Here, we present a method for applying brain mapping solutions to BCI applications, based on a reduced...
Article
Full-text available
This paper presents and discusses the challenge of mode mixing when using the Empirical Mode Decomposition (EMD) to identify intrinsic modes from EEG signals used for neural activity reconstruction. The standard version of the EMD poses some challenges when decomposing signals having intermittency and close spectral proximity in their bands. This i...
Chapter
Full-text available
The underlying activity in the brain can be estimated using methods based on discrete physiological models of the neural activity. These models involve parameters for weighting the estimated source activity of previous samples, however, those parameters are subject- and task-dependent. This paper introduces a dynamical non-linear regularized observ...
Article
Full-text available
In this work, a novel identification method of relevant Intrinsic Mode Functions, obtained from Electroencephalographic signals, by using an entropy criteria is proposed. The idea is to reduce the number of Intrinsic Mode Functions that are necessary for the electroencephalographic source reconstruction. An entropy cost function is applied on the I...
Conference Paper
Full-text available
This paper shows a method to locate actives sources from pre-processed electroencephalographic signals. These signals are processed using multivariate empirical mode decomposition (MEMD). The intrinsic mode functions are analyzed through the Hilbert-Huang spectral entropy. A cost function is proposed to automatically select the intrinsic mode funct...
Chapter
Full-text available
The brain is a complex system and the activity inside can describe non-linear behaviors where the signals of the EEG which are taken from the scalp represent the mixture of the activity in each distributed source inside the brain. This activity can be represented by non-linear models and the inverse problem for source activity estimation can consid...
Article
Full-text available
The applications of Empirical Mode Decomposition (EMD) in Biomedical Signal analysis have increased and is common now to find publications that use EMD to identify behaviors in the brain or heart. EMD has shown excellent results in the identification of behaviours from the use of electroencephalogram (EEG) signals. In addition, some advances in the...
Chapter
In this paper an improvement of the dynamic inverse problem solution is proposed by using constraints in the space-time-frequency domain. The method is based on multi-rate filter banks for frequency selection of the EEG signals and a cost function that includes spatial and temporal constraints. As a result, an iterative method which includes Freque...
Chapter
In this paper a novel methodology for assessing source connectivity applied to emotional states discrimination is proposed. The method involves (i) designing the set of Regions-of-interest (ROIs) over the cortical surface, (ii) estimating the ROI time-courses using a dynamic inverse problem formulation, (iii) estimating the pairwise functional conn...
Article
Full-text available
The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude o...
Conference Paper
Full-text available
Esta ponencia presenta la propuesta de un modelo de control por optimización para la atención de fallas en un sistema de distribución de energía eléctrica. Los resultados se validan mediante simulación a través de una metodología que define la función objetivo teniendo en cuenta factores como la ubicación de los puntos de falla, el tipo de falla, e...
Conference Paper
Full-text available
Empirical Mode Decomposition (EMD) is an adaptive time-frequency analysis method, which is very useful for extracting information from noisy nonlinear or nonstationary data. The applications of this technique in Biomedical Signal analysis has increased and is now common to find publications that use EMD to identify behaviors in the brain or heart....
Conference Paper
Full-text available
In this work a novel non-linear iterative regularization algorithm applied to the reconstruction of neuronal activity is presented. A physiologically-based non-linear spatio-temporal constraint is used for solving the dynamic inverse problem associated to the reconstruction of neural activity of the distributed sources. The proposed method includes...
Poster
Full-text available
Empirical Mode Decomposition (EMD) is an adaptive time-frequency analysis method, which is very useful for extracting information from noisy nonlinear or nonstationary data. The applications of this technique to Biomedical Signal analysis has increased and is now common to find publications that use EMD to identify behaviors in the brain or heart....
Conference Paper
Full-text available
In this paper, a novel method for neural activity reconstruction based on an adaptive non-linear regularized observer is proposed. The regularized observer is based on a discrete nonlinear state space system that describe homogeneous activity into the brain by considering a physiologically meaningful model. In order to obtain an adequately performa...
Conference Paper
Full-text available
Empirical Mode Decomposition (EMD) is anemerging tool in signal analysis, specifically in systems withNonlinear and/or nonstationary properties such as EEG signals.Its use is motivated by the fact that EMD can give an effectiveand meaningful time-frequency information about the signal. TheEMD decomposes the signal in intrinsic mode functions (IMF)t...
Chapter
Data assimilation techniques, like the Ensemble Kalman filter (EnKF), have been successfully used for weather forecasting or in general for state space estimation tasks that involve large scale nonlinear complex dynamical models. In this paper a novel application of the EnKF is presented for estimation of neural activity into the brain considering...
Chapter
Full-text available
A novel joint iterative dynamic inverse problem solution for brain mapping based on electroencephalographic (EEG) signals is presented. Proposed approach considers linear and nonlinear time-varying state space models of the brain as dynamic constraints in the solution of the dynamic inverse problem where the brain mapping and the neural activity mo...
Chapter
In this paper a new methodology for time-course reconstruction of neural activity is proposed. Proposed approach allows finding the locations and waveforms (time-course activity reconstruction) of inner brain sources, that could be used for source-based noninvasive brain computer interface technologies. Proposed framework for source-reconstruction...
Chapter
This paper shows signals of brain activity per zones, which were obtained through a dynamic forward model that considers non- homogeneous temporal activity in a group of regions. This activity could be observed in a multichannel electroencephalography. The forward model combines the reduction of source space and a model of spatio-temporal propagati...
Article
Full-text available
p>En este artículo se describe el modelo dinámico de un vehículo aéreo no tripulado tipo QuadCopter, se linealiza alrededor de un punto de operación y se aplica un controlador por realimentación de estados que logra llevar el sistema a un estado estable en 100ms. Se aplica un algoritmo de identificación MIMO por mínimos cuadrados al modelo no linea...
Article
The use of wind energy for power electric systems attempts to reduce the dependence on fuel-based energy. With the aim of generating electrical power based on wind energy, it becomes necessary to model and predict wind speed. Wind speed observations are packed with outliers what makes it difficult to propose accurate predictors. This paper presents...
Conference Paper
A novel Weighted Unscented Kalman Filtering method is introduced for neural activity estimation from electroencephalographic signals. The introduction of a weighting stage improves the solution by extracting relevant information directly from the measured data. Besides, a discrete nonlinear state space model representing the brain neural activity i...
Conference Paper
Estimation of neural activity using Electroencephalography (EEG) signals allows identifying with high temporal resolution those brain structures related to pathological states. This work aims to improve spatial resolution of estimated neural activity employing time-varying dynamic constraints within the iterative inverse problem framework. Particul...
Conference Paper
A methodology for tuning multivariable controllers in state space by using genetic algorithms is proposed. The multivariable controller is designed in discrete time by using an extended state space feedback with integral action. The feedback gain is computed by using a linear quadratic regulator. Optimal constraints are tuned by genetic algorithms...
Article
Full-text available
This paper proposes a new adaptive control strategy for a wind energy conversion system based on a permanent magnet synchronous generator and a pulse-width modulated current source converter. Most of the studies on wind farms are based on double fed induction technology. Nevertheless, the proposed conversion system is a good alternative due to its...
Article
Full-text available
Las turbinas eólicas son una de las tecnologías más avanzadas dentro del conjunto de las energías limpias para obtener energía eléctrica. Debido al comportamiento estocástico del viento, se debe realizar el control de estas turbinas para maximizar la potencia de salida. Con el fin de solu - cionar el problema de control óptimo se debe conocer la di...
Conference Paper
A Weighted Dynamic Inverse Problem Solution is proposed for electroencephalographic current density reconstruction. The method considers physiologically based models that takes both spatial and temporal dynamics into account and a weighting stage to obtain the covariance of the measurement equation from observations. The calculated weighting matrix...
Conference Paper
Electroencephalographic (EEG) recordings contain dynamic information inherent to its complex behavior, therefore, the accurate estimation of neural activity is highly dependent on the inclusion of such information in the inverse problem solution. The present work presents a way to obtain constraints for the Bayesian inverse problem solution, throug...
Article
Full-text available
En este artículo de investigación se presenta la estimación de la actividad neuronal y la dinámica del cerebro a partir de señales electroencefalográficas (EEG) usando un filtro de Kalman dual. El comportamiento dinámico del cerebro se representa a partir de modelos lineales con base en restricciones fisiológicas. Como resultado, se obtiene un mejo...
Article
Full-text available
This paper presents a methodology for designing a minimum variance control- (MVC) and Kalman filter- (KF) based adaptive system. MVC is a technique of great interest, and it is widely used because it can reduce either energy or material consumption, or else, it can increase production performance. The Kalman filter is a recursive method that provid...
Article
This paper presents a methodology for parameter identification of a doubly fed induction generator (DFIG) in the presence of spurious data. DFIG is widely used in the electrical energy production using wind; one problem that the control system for these machines faces is the variability in the parameter values, and optimal performance for this cont...
Article
Full-text available
This paper presents a methodology for designing a minimum variance control- (MVC) and Kalman filter- (KF) based adaptive system. MVC is a technique of great interest, and it is widely used because it can reduce either energy or material consumption, or else, it can increase production performance. The Kalman filter is a recursive method that provid...
Article
Full-text available
This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that takes both spatial and temporal dynamics into account and a weighting stage to modify the assumptions of the model from observations. The calculated w...
Conference Paper
Full-text available
This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that take both spatial and temporal dynamics into account and a weighting stage to modify the assumptions of the model from observations. The calculated we...
Article
Full-text available
This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary element method. A comparative analysis for the dyna...
Conference Paper
The estimation of current distributions from electroencephalographic recordings poses an inverse problem, which can approximately be solved by including dynamical models as spatio-temporal constraints onto the solution. In this paper, we consider the electrocardiography source localization task, where a specific structure for the dynamical model of...
Conference Paper
A new electroencephalographic current density reconstruction method is introduced using a physiologically based nonlinear modeling that describes better the dynamic behavior of the neural activity. In addition, time-variant parameters are considered into the model to capture the dynamics for normal and pathological states measured from signals. The...
Conference Paper
Full-text available
Epilepsy is a brain pathology that affects approximately 40 million people in the world. The most utilized clinical test for epilepsy diagnose is the electroencephalogram (EEG). For this reason, nowadays are being developed multiple tools devised for automatic seizure detection on EEG signals. In this work, several approaches of TFR estimation for...
Article
Full-text available
This work presents a control system used when the detailed parameter information of a plant in not available. Therefore, is useful to design a controller with an approximated model of the system, this is reachable through the use of parameter estimation based on the input and output signals. The estimation procedure is commonly developed with deter...
Article
Full-text available
Resumen En este artículo se presenta un método de estimación de la actividad neuronal sobre el cerebro, que tiene en cuenta, en la solución del problema inverso, un modelo dinámico de propagación para la actividad neuronal sobre un modelo realista con elementos finitos de frontera, que incluye un modelo fisiológico que describe la interacción reali...
Conference Paper
Renewable energy sources have focused a special attention in wind energy conversion systems, where the goal is maximal power extraction. This paper presents an evaluation of the linear controllers eigen structure assignment, linear quadratic regulator, and the robust loop shaping design procedure actuating on a small wind energy conversion system w...
Conference Paper
Full-text available
This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either t...
Article
Full-text available
Time-frequency representations (TFR) are one of the most popular characterization methods for non-stationary biosignals. Despite of their potential advantages, these representations suffer of large quantity of redundant and irrelevant data which makes them difficult to use for classification purposes. In this work, a methodology for reduction of ir...
Article
Full-text available
Resumen Este trabajo se centra en el diseño de un controlador lineal mediante la estrategia de optimización de múltiple objetivo sobre una máquina de inducción modelada en el sistema de coordena-das de flujo del rotor. Los índices de desempeño seleccionados son la sensibilidad de las variables de estado de forma individual, la ganancia del controla...
Conference Paper
Full-text available
In order to get the maximum power from the wind, the variable-speed turbine should run at different speed when wind speed changes.The dynamic characteristics of the control system must be properly defined and designed to achieve satisfactory generated power quality and system security, availability and reliability. Therefore great attention has to...
Conference Paper
Full-text available
Se estudia el problema de reconstrucción de las fuentes bioeléctricas del cerebro, a partir de sus potenciales generados en el cuero cabelludo. El problema es mal condicionado debido a que existen diferentes fuentes que producen una misma medición de potencial superficial, además pequeñas variaciones en los datos de entrada pueden producir variacio...
Article
Full-text available
The reconstruction of bioelectrical brain sources form their measured scalp potentials is considered here. For avoiding assumptions on the number of sources, a distributed source model is implemented. The direct problem is studied by using a spherical four-layered head model. For dealing with the inverse problem, Tikhonov regularization is proposed...
Article
Full-text available
This articles shows the application of a classic non-linear control technique called "linearization by feedback of status variables." An application on the induction engine is made. Variable to be controlled is speed of the engine shaft. The system employs a vectorial control scheme for AC engines developed during the last decades. This is a method...
Article
Este artículo presenta el diseño de un controlador adaptativo, el sistema de control emplea lógica difusa adaptativa, modos deslizantes y es entrenado con la técnica de mínimos cuadrados recursivos. El problema de la variación de parámetros es resuelto con el controlador adaptativo; se utiliza un regulador interno PI con el cual se produce que el c...
Conference Paper
This paper presents a robust control system over the induction motor based on the H<sub>infin</sub> and loop shaping procedure. It has a set of simples steps to design the linear control law, the model used is the rotor-flux oriented coordinated system and is linearized by the Taylor's series. Finally the output feedback controller test shows the p...
Article
Full-text available
The modelling of the induction machine like a drive have a great importance in the controller design, a model that make simple this work is the rotor-flux field oriented coordinated system described in this paper, also a simple technique to control the motor in open loop with voltage-frequency is analyzed to present a start-up of the device.
Article
Se presenta un esquema para la aplicación del control por realimentación de variables de estado estimadas a través de un observador sobre un sistema de parámetros desconocidos. Los parámetros del sistema son estimados a través de un algoritmo adaptativo de mínimos cuadrados. Para la validación de resultados se aplica el control a un sistema de terc...
Article
Full-text available
Se presenta un esquema para la aplicación del control por planos deslizantes usando identificación paramétrica adaptativa de un sistema multivariable. En el diseño del controlador se considera que el sistema es desacoplado y que los acoples se pueden considerar como perturbaciones. Las pruebas sobre el desempeño del controlador se realizan sobre un...
Article
Full-text available
Se presentan esquemas de caracterización dinámica aplicados sobre señales electromiográficas utilizando una estructura de bancos de filtros con variación de parámetros adaptativos. Los bancos de filtros son calculados a partir de esquemas de actualización. Los criterios de adaptabilidad utilizados para la caracterización dinámica son momentos estad...
Article
Full-text available
Se presenta un esquema para la aplicación del control por planos deslizantes usando lógica difusa de un modelo académico para el helicóptero. Las pruebas sobre el desempeño del controlador se realizan sobre el sistema con perturbaciones temporales. La ventaja del control implementado es que no necesita del conocimiento completo del modelo matemátic...
Article
Full-text available
En este artículo se propone una metodología para el diseño de un controlador óptimo cuadrático sobre un sistema de una entrada y dos salidas, sometido a cambios en los parámetros de operación. Debido a que para el diseño del controlador por realimentación de variables de estado es necesario tener todas las variables de estado, se utiliza un observa...
Chapter
We present a methodology for feature extraction by means of adaptive filter banks in case of automatic identification of arrhythmias using ECG recording. Proposed filter banks, which are supposed to track in more accurate way any change of parameters of time-varying sequence, is developed for biorthogonal wavelet bases using Teager algorithm. Besid...
Chapter
We present a methodology for dynamic feature extraction by means of adaptive filter banks in case of automatic identification of brain zone using micro electrode recording. Proposed biorthogonal filter banks changes according energy. Besides, adaptive lifting schemes, which allow filter order change, are used for filter bank implementation. Lifting...