Implementación de un módulo avanzado de imputación de datos faltantes para KLASS

El presente trabajo describe la implementación del método de imputación de datos faltantes MIMMI en el sistema Java-KLASS. Este software comprende un compendio de herramientas para la realización de Minería de Datos, que al momento no contaba con un método adecuada de imputación de datos faltantes

Resilience and dependencies in the european transmission power grid. A data science and networks approach

The project is based on analyzing data from European Transmission Power Grid under a data science approach, by taking into account additional open data about the energetic policies of the involved countries. Available data comes back to 20 years and a combination of complex networks methods and data science will be used for a global understanding of the phenomeno

A novel data condition and performance hybrid imputation method for energy effcient operations of marine systems

Datasets with missing values can adversely affect the accuracy of any subsequent decision making, for instance in condition- and performance-monitoring for energy efficient operations of ship systems. Missing data imputation is therefore, a necessary step as it ensures that the data can reach their full knowledge extracting potential. This paper aims at developing a novel hybrid imputation method, which can be employed to condition data acquired from marine machinery systems, thus increasing the quality of the original dataset and improving the decision making for ship efficient operations. The paper includes of all necessary imputation preparatory steps and further post-imputation processes. The developed method employs a hybrid k-NN and MICE imputation algorithm which combines data mining with first-principle knowledge. The proposed hybrid approach is compared with the individual performance of k-NN and MICE algorithms and is implemented in a dataset acquired from the main engine system of an oceangoing vessel. It is shown that the hybrid approach performs best, exhibiting an average error of 2.2% compared to the k-NN and MICE algorithms with errors 5.6% and 3.3%, respectively, highlighting that the small error of the proposed novel method improves the quality of data used in condition- and performance-monitoring

Mixed intelligent-multivariate missing imputation

In real applications, important rates of missing data are often found and have to be pre-processed before the analysis. The literature for missing imputation is abundant. However, the most precise imputation methods require long time, and sometimes speci c software; this implies a signi cant delay to get nal results. The Mixed Intelligent-Multivariate Missing Im- putation (MIMMI) method is proposed as a hybrid missing imputation methodology based on clustering. MIMMI is a non parametric method that combines the prior expert knowledge with multivariate analysis without requiring assumptions on the probabilistic models of the variables (normality, exponentiality, etc). The proposed imputation values implicitly take into account the joint distribution of all variables and can be determined in a relatively short time. MIMMI uses the conditional mean according to the self-underlying structure of the dataset. It provides a good trade-o between accuracy and both simplicity and required time to data preparation. The mechanics of the method is illustrated with some case-studies, both synthetic and real applications related with human behavior. In both cases, acceptable quality results were obtained in short time.Peer Reviewe

Advanced computational engineering

This is an author's accepted manuscript of an article published in “International Journal of Computer Mathematics"; Volume 91, Issue 1, 2014; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/00207160.2014.880256It is with pleasure that we offer the readers of the International Journal of Computer Mathematics this special issue consisting of some of the most significant contributions to computational and mathematical methods with advanced applications in engineering presented at the International Conference on Mathematical Modelling in Engineering & Human Behaviour 2012, held at the Instituto Universitario de Matemática, Multidisciplinar, Polytechnic City of Innovation in Valencia, Spain, September 4–7, 2012, cf. http://jornadas.imm.upv.es/2012/Ehrhardt, M.; Jódar Sánchez, LA.; Villanueva Micó, RJ. (2014). Advanced computational engineering. International Journal of Computer Mathematics. 91(1):1-3. doi:10.1080/00207160.2014.880256S13911Aznar, F., Pujol, M. J., Sempere, M., & Rizo, R. (2013). A macroscopic model for high intensity radiofrequency signal detection in swarm robotics systems. International Journal of Computer Mathematics, 91(1), 32-41. doi:10.1080/00207160.2013.771180Bernal, A., Abarca, A., Barrachina, T., & Miró, R. (2013). Methodology to resolve the transport equation with the discrete ordinates code TORT into the IPEN/MB-01 reactor. International Journal of Computer Mathematics, 91(1), 113-123. doi:10.1080/00207160.2013.799668Castro, M. A., Rodríguez, F., Cabrera, J., & Martín, J. A. (2013). Difference schemes for time-dependent heat conduction models with delay. International Journal of Computer Mathematics, 91(1), 53-61. doi:10.1080/00207160.2013.779371Cornolti, L., Lucchini, T., Montenegro, G., & D’Errico, G. (2013). A comprehensive Lagrangian flame–kernel model to predict ignition in SI engines. International Journal of Computer Mathematics, 91(1), 157-174. doi:10.1080/00207160.2013.829213García-Oliver, J. M., Novella, R., Pastor, J. M., & Winklinger, J. F. (2013). Evaluation of combustion models based on tabulated chemistry and presumed probability density function approach for diesel spray simulation. International Journal of Computer Mathematics, 91(1), 14-23. doi:10.1080/00207160.2013.770844Gibert, K. (2013). Mixed intelligent-multivariate missing imputation. International Journal of Computer Mathematics, 91(1), 85-96. doi:10.1080/00207160.2013.783209González-Pintor, S., Ginestar, D., & Verdú, G. (2013). Preconditioning the solution of the time-dependent neutron diffusion equation by recycling Krylov subspaces. International Journal of Computer Mathematics, 91(1), 42-52. doi:10.1080/00207160.2013.771181Guardiola, C., Pla, B., Blanco-Rodríguez, D., & Reig, A. (2013). Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles. International Journal of Computer Mathematics, 91(1), 147-156. doi:10.1080/00207160.2013.829567Montoliu, C., Ferrando, N., Cerdá, J., & Colom, R. J. (2013). Application of the level set method for the visual representation of continuous cellular automata oriented to anisotropic wet etching. International Journal of Computer Mathematics, 91(1), 124-134. doi:10.1080/00207160.2013.801464Montorfano, A., Piscaglia, F., & Onorati, A. (2013). Wall-adapting subgrid-scale models to apply to large eddy simulation of internal combustion engines. International Journal of Computer Mathematics, 91(1), 62-70. doi:10.1080/00207160.2013.783207Ramos-Martínez, E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2013). Ensemble of naïve Bayesian approaches for the study of biofilm development in drinking water distribution systems. International Journal of Computer Mathematics, 91(1), 135-146. doi:10.1080/00207160.2013.808335Salvador, F. J., Martínez-López, J., Romero, J.-V., & Roselló, M.-D. (2013). Study of the influence of the needle eccentricity on the internal flow in diesel injector nozzles by computational fluid dynamics calculations. International Journal of Computer Mathematics, 91(1), 24-31. doi:10.1080/00207160.2013.770483Sastre, J., Ibáñez, J., Ruiz, P., & Defez, E. (2013). Accurate and efficient matrix exponential computation. International Journal of Computer Mathematics, 91(1), 97-112. doi:10.1080/00207160.2013.791392Serrano, J. R., Arnau, F. J., Piqueras, P., & García-Afonso, O. (2013). Application of the two-step Lax and Wendroff FCT and the CE-SE method to flow transport in wall-flow monoliths. International Journal of Computer Mathematics, 91(1), 71-84. doi:10.1080/00207160.2013.783206Zhyrova, A., & Štys, D. (2013). Construction of the phenomenological model of Belousov–Zhabotinsky reaction state trajectory. International Journal of Computer Mathematics, 91(1), 4-13. doi:10.1080/00207160.2013.76633