New methods for discovering local behaviour in mixed databases

Clustering techniques are widely used. There are many applications where it is desired to find automatically groups or hidden information in the data set. Finding a model of the system based in the integration of several local models is placed among other applications. Local model could have many structures; however, a linear structure is the most common one, due to its simplicity. This work aims at finding improvements in several fields, but all them will be applied to this finding of a set of local models in a database. On the one hand, a way of codifying the categorical information into numerical values has been designed, in order to apply a numerical algorithm to the whole data set. On the other hand, a cost index has been developed, which will be optimized globally, to find the parameters of the local clusters that best define the output of the process. Each of the techniques has been applied to several experiments and results show the improvements over the actual techniques.Barceló Rico, F. (2009). New methods for discovering local behaviour in mixed databases. http://hdl.handle.net/10251/12739Archivo delegad

Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms

ABSTRACT Diabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies. One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate "online" plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements from compartments alternative to blood. Current commercially available CGMS are minimally invasive and offer an estimation of plasma glucose from measurements in the interstitial fluid CGMS is a key component of the technical approach to build the artificial pancreas, aiming at closing the loop in combination with an insulin pump. Yet, the accuracy of current CGMS is still poor and it may partly depend on low performance of the implemented Calibration Algorithm (CA). In addition, the sensor-to-patient sensitivity is different between patients and also for the same patient in time. It is clear, then, that the development of new efficient calibration algorithms for CGMS is an interesting and challenging problem. The indirect measurement of plasma glucose through interstitial glucose is a main confounder of CGMS accuracy. Many components take part in the glucose transport dynamics. Indeed, physiology might suggest the existence of different local behaviors in the glucose transport process. For this reason, local modeling techniques may be the best option for the structure of the desired CA. Thus, similar input samples are represented by the same local model. The integration of all of them considering the input regions where they are valid is the final model of the whole data set. Clustering is tBarceló Rico, F. (2012). Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17173Palanci

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

[EN] Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-hCGMSGold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter-and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.Manuscript received April 17, 2012; revised September 10, 2012 and January 21, 2013; accepted March 11, 2013. Date of publication March 19, 2013; date of current version May 1, 2013. This work was supported in part by the Spanish Ministry of Science and Innovation under Project DPI2010-20764-C02 and in part by the European Union under Grant FP7-PEOPLE-2009-IEF, Ref 252085. The work of F. Barcelo-Rico was supported by the Spanish Ministry of Education (FPU AP2008-02967).Barceló-Rico, F.; Diez, J.; Rossetti, P.; Vehi, J.; Bondía Company, J. (2013). Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring. IEEE Journal of Biomedical and Health Informatics. 17(3):530-538. https://doi.org/10.1109/JBHI.2013.2253325S53053817

Modelling and control of a continuous distillation tower through fuzzy techniques

This paper presents a methodology for the design of a fuzzy controller applicable to continuous processes based on local fuzzy models and velocity linearizations. It has been applied to the implementation of a fuzzy controller for a continuous distillation tower. Continuous distillation towers can be subjected to variations in feed characteristics that cause loss of product quality or excessive energy consumption. Therefore, the use of a fuzzy controller is interesting to control process performance.A dynamic model for continuous distillation was implemented and used to obtain data to develop the fuzzy controller at different operating points. The fuzzy controller was built by integration of linear controllers obtained for each linearization of the system. Simulation of the model with controller was used to validate the controller effectiveness under different scenarios, including a study of the sensibility of some parameters to the control.The results showed that the fuzzy controller was able to keep the target output in the desired range for different inputs disturbances, changing smoothly from a predefined target output to another. The developed techniques are applicable to more complex distillation systems including more operating variablesThe authors acknowledge the partial funding of this work by the projects: Regional Government Project GVPRE/2008/108, and National Projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01.Barceló Rico, F.; Gozálvez Zafrilla, JM.; Diez Ruano, JL.; Santafé Moros, MA. (2011). Modelling and control of a continuous distillation tower through fuzzy techniques. Chemical Engineering Research and Design. 89(1):107-115. https://doi.org/10.1016/j.cherd.2010.04.015S10711589

A multiple local models approach to accuracy improvement in continuous glucose monitoring

This is a copy of an article published in the Diabetes Technology & Therapeutics © 2012 copyright Mary Ann Liebert, Inc.; Diabetes Technology & Therapeutics is available online at: http://online.liebertpub.com/toc/dia/14/1[EN] Background: Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance. Methods: CGM data from GlucoDay (R) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations (G) over cap, at different time instants [I-k, Ik-1, (G) over cap (k-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm. Results: Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD) = 10.8 +/- 1.5% versus 14.7 +/- 5.4%, respectively (P = 0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8 +/- 2.6%, P<0.05). Conclusions: The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.The authors acknowledge the partial funding of this work by the Spanish Ministry of Science and Innovation projects DPI2007-66728-C02-01 and DPI2010-20764-C02-01 and by the European Union through FEDER funds and grant FP7-PEOPLE-2009-IEF, Reference 252085. F.B.R. is the recipient of a fellowship (FPU AP2008-02967) from the Spanish Ministry of Education.Barceló Rico, F.; Bondía Company, J.; Diez Ruano, JL.; Rossetti ., P. (2012). A multiple local models approach to accuracy improvement in continuous glucose monitoring. Diabetes Technology & Therapeutics. 14(1):74-82. https://doi.org/10.1089/dia.2011.0138S748214

Modelat, control i optimització d'una torre per a destil·lacions binàries mitjançant tècniques d'intel·ligència artificial

Consulta en la Biblioteca ETSI Industriales (6257)[ES] L'objectiu de qualsevol projecte final de carrera, i d'este també, és clar, és la d'aplicar els coneixements adquirits en els anys de carrera, en este cas d'Enginyeria en Automµatica i Electrµonica Industrial. Però els coneixements són molts i els projectes en general abarquen un número limitat de disciplines. En este cas, la motivació principal per a l'elecció d'este projecte és que és multidisciplinar i ambdues disciplines són d'interés personal. Per una banda durant tota la carrera s'han estudiat molts conceptes teµorics i, una aplicació pràctica sempre aclareix molts dubtes i fa que els conceptes estudiat s'assolisquen. Éste és l'objectiu principal, ja que quan són massa els conceptes teòrics estudiats és perd la referència pràctica que és la important al final.Barceló Rico, F. (2007). Modelat, control i optimització d'una torre per a destil·lacions binàries mitjançant tècniques d'intel·ligència artificial. http://hdl.handle.net/10251/36282.Archivo delegad

Geometrical codification for clustering mixed categorical and numerical databases

[EN] This paper presents an alternative to cluster mixed databases. The main idea is to propose a general method to cluster mixed data sets, which is not very complex and still can reach similar levels of performance of some good algorithms. The proposed approach is based on codifying the categorical attributes and use a numerical clustering algorithm on the resulting database. The codification proposed is based on polar or spherical coordinates, it is easy to understand and to apply, the increment in the length of the input matrix is not excessively large, and the codification error can be determined for each case. The proposed codification combined with the well known k-means algorithm showed a very good performance in different benchmarks and has been compared with both, other codifications and other mixed clustering algorithms, showing a better or comparable performance in all cases.The authors acknowledge the partial funding of this work by the National projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01.Barceló Rico, F.; Diez, J. (2012). Geometrical codification for clustering mixed categorical and numerical databases. Journal of Intelligent Information Systems. 39(1):167-185. https://doi.org/10.1007/s10844-011-0187-yS16718539

A comparative study of codification techniques for clustering heart disease database

[EN] This paper compares various proposals for codifying categorical attributes in a heart disease database so that numerical clustering algorithms can be applied to them. An approach for the codification of categorical attributes based on polar coordinates is proposed. This is compared with other codifications and methods for clustering mixed databases found in the literature. Our proposal has many advantages: it is relatively easy to understand and apply; the increment in the length of the input matrix is not excessively large; and the committed error is under control. The proposed codification has been combined in this case with the well-known k-means algorithm and has shown a very good performance in a heart disease database benchmark.Partially supported by national project DPI2007-66728-C02-01, regional project GVPRE-2008-108, and the authors’ institution.Barceló Rico, F.; Diez Ruano, JL.; Bondía Company, J. (2011). A comparative study of codification techniques for clustering heart disease database. Biomedical Signal Processing and Control. 6(1):64-69. https://doi.org/10.1016/j.bspc.2010.07.004S64696