Manuel Jesús Vasallo

A Methodology for Sizing Backup Fuel-Cell/Battery Hybrid Power Systems

Hybridization of fuel cells and batteries combines the advantages of both power sources. This paper proposes the use of fuel-cell/battery hybrid power systems as backup power systems and develops a methodology for sizing both fuel cell and battery bank, according to a minimum lifecycle cost criterion, from any defined hourly load profile and any defined backup time. For this purpose, an existing power-system-sizing computer tool has been used, but its initial capabilities have been extended. The developed methodology allows decisions to be taken before any investment is made. As a practical application, the methodology is used for the sizing of a backup power system for a telecommunication system.

Slide Window Bounded-Error Time-Varying Systems Identification

This technical note presents a new identification method for discrete linear systems with time-varying parameters based on bounded-error approach. It is assumed a bounded additive error on measurements and a bound on parameter variation. Each time instant, the identification method uses a slide window along historical data and an explicit expression to provide an outer bound of the set of model parameters that is consistent with measurements, model structure and bounds on error and variations considered. Furthermore, the center estimation of the obtained outer solution set is a suitable nominal estimation of the parameters. This center is the optimal solution of a least squares problem that penalizes the prediction error of the model and the parameter variations. The explicit expression that provides the outer bound of the parameters and the optimal property of the center estimation are the main contributions of the technical note. In order to clarify the proposed method, an application example and a comparison with a previous recursive bounded-error method are included.

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification

AbstractThe present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical AbstractDiagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis

AIM This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). MATERIAL AND METHODS The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. RESULTS The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, while the specificity of the system is significantly lower than that of human graders (0.5098), it is high enough to screen more than half of the patients unaffected by the disease. CONCLUSION Results are promising in integrating this system in DR screening programmes. At an early stage, the system could act as a pre-screening system, by screening healthy patients (with no obvious signs of DR) and identifying only those presenting signs of the disease.

A General Framework for Predictors Based on Bounding Techniques and Local Approximation

This paper introduces a general framework for prediction based on nonparametric local estimation and bounding techniques. A set of historic input-output measurements of the system is stored in a database. When a prediction for a given point is required, data from the neighborhood of this point is retrieved and a prediction is formed. These prediction methods return an interval that bounds the considered system output. The width of the obtained interval prediction reflects the amount of information about the system available at the point to be predicted. In addiction, the midpoint of the interval prediction can be used as central estimate. The contribution of the paper is threefold. First, a general framework that covers previous methods proposed in the literature is presented. Second, the general properties of the framework are analyzed. Third, new predictors based on this framework are proposed. Finally, a benchmark example and a comparative study are provided for illustration purposes.

Interval predictor based on a Reversed Huber's error function

In dynamical systems context, a predictor is a method that provides an estimation of the future system output using past information of the system. An interval predictor provides an outer estimation of the future output. The center of this interval can be used as central or nominal prediction. A method to formulate interval predictors is to assume an unknown but bounded error in the system measurements. The aim of this work is to study the benefits of using a Reversed Hubers function as error function in this kind of predictors. A Reversed Hubers function is a convex function, piecewise linear near zero but quadratic for large values. The paper provides a nonparametric formulation of the interval predictor and shows by a real world example that the proposed predictor can improve the performance of the central prediction.

Robust predictor for nonlinear systems based on bounding-error methods

A new robust predictor for nonlinear systems is proposed. The predictor uses a set of system input-output measurements and a local linearization method based on bounded-error to return an interval that bounds the system output. The midpoint of the prediction interval is the optimal solution of an optimization problem which minimizes a quadratic prediction-error functional cost with a regularization term. The width of the prediction interval can be used as a reliability index of this central prediction. Bounded-error methods use an unique error bound applied to all measurements. The main idea of this work is to use a reliability index that provides a different error bound for each measurement. This allows us to apply the proposed method to measurements with outliers or different error bounds. The main contribution of the paper is the explicit expression that provides the prediction interval and assures a low computational effort.