Héctor Quintián

International Joint Conference

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at the 8th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2015) and the 6th International Conference on European Transnational Education (ICEUTE 2015). These conferences were held in the beautiful and historic city of Burgos (Spain), in June 2015.The aim of the 8th CISIS conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event.After a through peer-review process, the CISIS 2015 International Program Committee selected 43 papers, written by authors from 16 different countries. In the case of 6th ICEUTE conference, the International Program Committee selected 12 papers (from 7 countries). These papers are published in present conference proceedings, achieving an acceptance rate of about 39%.The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the CISIS and ICEUTE conferences would not exist without their help.

A hybrid intelligent system for PID controller using in a steel rolling process

Abstract With the aim to improve the steel rolling process performance, this research presents a novel hybrid system for selecting the best parameters for tuning in open loop a PID controller. The novel hybrid system combines rule based system and Artificial Neural Networks. With the rule based system, it is modeled the existing knowledge of the PID controller tuning in open loop and, with Artificial Neural Network, it is completed the rule based model that allow to choose the optimal parameters for the controller. This hybrid model is tested with a long dataset to obtain the best fitness. Finally, the novel research is validated on a real steeling roll process applying the hybrid model to tune a PID controller which set the input speed in each of the gearboxes of the process.

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges.

International Joint Conference CISIS'12-ICEUTE'12-SOCO'12 Special Sessions

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at CISIS 2012 and ICEUTE 2012, both conferences held in the beautiful and historic city of Ostrava (Czech Republic), in September 2012. CISIS aims to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a through peer-review process, the CISIS 2012 International Program Committee selected 30 papers which are published in these conference proceedings achieving an acceptance rate of 40%. In the case of ICEUTE 2012, the International Program Committee selected 4 papers which are published in these conference proceedings. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the CISIS and ICEUTE conferences would not exist without their help.

International Joint Conference SOCO’13-CISIS’13-ICEUTE’13

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2013, CISIS 2013 and ICEUTE 2013, all conferences held in the beautiful and historic city of Salamanca (Spain), in September 2013.Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena.After a through peer-review process, the 8th SOCO 2013 International Program Committee selected 40 papers which are published in these conference proceedings, and represents an acceptance rate of 41%. In this relevant edition a special emphasis was put on the organization of special sessions. Four special sessions were organized related to relevant topics as: Systems, Man, and Cybernetics, Data Mining for Industrial and Environmental Applications, Soft Computing Methods in Bioinformatics, and Soft Computing Methods, Modelling and Simulation in Electrical Engineer.The aim of the 6th CISIS 2013 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event.After a through peer-review process, the CISIS 2013 International Program Committee selected 23 papers which are published in these conference proceedings achieving an acceptance rate of 39%.In the case of 4th ICEUTE 2013, the International Program Committee selected 11 papers which are published in these conference proceedings.The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the SOCO, CISIS and ICEUTE conferences would not exist without their help.

Hybrid Intelligent Model for Fault Detection of a Lithium Iron Phosphate Power Cell Used in Electric Vehicles

Currently, the electrical mobility and the intermittent power generation facilities problem are two of the main purposes of batteries. Batteries, in general terms, have a complex behavior. Due to the usual electrochemical nature of batteries, several tests are made to check their performance, and it is very useful to know a priori how they are working in each case. By checking the battery temperatures for a specific voltage and current value, this work describes a hybrid intelligent model aimed at making fault detection of a LFP (Lithium Iron Phosphate - LiFePO4) power cell type, used in Electric Vehicles. A large set of operating points is obtained from a real system to create the dataset for the operation range of the power cell. Clusters of the different behavior zones have been obtained to accomplish the solution. Some simple regression methods have been applied for each cluster. Polynomial Regression, Artificial Neural Networks and Support Vector Regression were the combined techniques to develop the hybrid intelligent model proposed. The novel hybrid model allows to be achieved good results in all the operating range, detecting all the faults tested.

Intelligent Model to Obtain Current Extinction Angle for a Single Phase Half Wave Controlled Rectifier with Resistive and Inductive Load

The present work show the model of regression based on intelligent methods. It has been created to obtain current extinction angle for a half wave controlled rectifier. The system is a typically non-linear case of study that requires a hard work to solve it manually. First, all the work points are calculated for the operation range. Then with the dataset, to achieve the final solution, several methods of regression have been tested from traditional to intelligent types. The model is verified empirically with electronic circuit software simulation and analytical methods. The model allows obtaining good results in all the operating range.

Beta Scale Invariant Map

Abstract In this study we present a novel version of the Scale Invariant Map (SIM) called Beta-SIM, developed to facilitate the clustering and visualization of the internal structure of complex datasets effectively and efficiently. It is based on the application of a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution, when applied to the Scale Invariant Map. The Beta-SIM behavior is thoroughly analyzed and successfully demonstrated over 2 artificial and 16 real datasets, comparing its results, in terms of three performance quality measures with other well-known topology preserving models such as Self Organizing Maps (SOM), Scale Invariant Map (SIM), Maximum Likelihood Hebbian Learning-SIM (MLHL-SIM), Visualization Induced SOM (ViSOM), and Growing Neural Gas (GNG). Promising results were found for Beta-SIM, particularly when dealing with highly complex datasets.

Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

The eleven papers included in this special issue represent a selection of extended contributions presented at the 8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013 held in Salamanca, Spain, September 11th–13th, 2013, and organized by the BISITE and the GICAP research groups. The International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field. The papers are organized as follows. In the first contribution, Barreto and Barros, introduce a simple and efficient extension of the Extreme Learning Machine (ELM) network. The proposed variant of the ELM, henceforth named Robust ELM (RELM), is designed using M-estimators to compute the output weights instead of the standard ordinary least squares (OLS) method. They evaluate the performance of the RELM using batch and recursive learning rules, and also introduce a model selection strategy based on Particle Swarm Optimization (PSO) to find an optimal architecture for datasets contaminated with nonGaussian noise and outliers. By means of comprehensive computer simulations using synthetic and real-world data sets, they show that the proposed robust ELM classifiers consistently outperform the original version. This paper, by Garcia et al., presents a meta-learning recommendation system able to predict the expected performance of noise filters in noisy data identification tasks. For such, a metabase is created; containing meta-features extracted from several corrupted data sets along with the performance of some noise filters when applied to these data sets. Next, regression models are induced from this meta-base to predict the expected performance of the investigated filters in the identification of noisy data. The experimental results show that meta-learning can provide a good recommendation of the most promising filters to be applied to new classification data sets. This contribution, by Sáez et al., proposes a new measure to establish the expected behavior of a classifier with noisy data trying to minimize the problems of considering performance and robustness individually: the Equalized Loss of Accuracy (ELA). The advantages of ELA against other robustness metrics are studied and all of them are also compared. Both, the analysis of the distinct measures and the empirical results, show that ELA is able to overcome the aforementioned problems that the rest of the robustness metrics may produce, having a better behavior when comparing different classifiers over the same data set. In this paper Martínez-Ballesteros et al., present a study of wellknown quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending of the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real world applications related to ozone and earthquakes are reported. In this contribution, Gala et al., apply Support Vector Regression (SVR), Gradient Boosted Regression (GBR), Random Forest Regression (RFR) as well as a hybrid method to combine them to downscale and improve 3-h accumulated radiation forecasts provided by Numerical Weather Prediction (NWP) systems for seven locations in Spain. They use either direct 3-h aggregated radiation forecasts or they build first global accumulated daily predictions and disaggregate them into 3-h values, with both approaches outperforming the base NWP forecasts. They also show how to disaggregate the 3-h forecasts into hourly values using interpolation based in clear sky (CS) theoretical and experimental radiation models, with the disaggregated forecasts again being better than the base NWP ones and where empirical CS interpolation yields the best results. Besides providing ample background on a problem that others many opportunities to the Machine Learning (ML) community, their study shows that MLmethods or, more generally, hybrid artificial intelligence systems are quite effective and, hence, relevant for solar radiation prediction. Next paper, by Palacios et al., suggest that a new extension to vague datasets of the classification algorithm Fuzzy Unordered Rule Induction Algorithm (FURIA) has advantages over other approaches in both the computational effort during the learning stage and the linguistic quality of the induced classification rules. The new approach is benchmarked with different test problems and compared to other artificial intelligence tools for dyslexia diagnosis in the literature.

10th International Conference on Soft Computing Models in Industrial and Environmental Applications

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at the 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015), held in the beautiful and historic city of Burgos (Spain), in June 2015.Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate and analyze very complex issues and phenomena. This Conference is mainly focused on its industrial and environmental applications.After a through peer-review process, the SOCO 2015 International Program Committee selected 41 papers, written by authors from 15 different countries. These papers are published in present conference proceedings, achieving an acceptance rate of 40%.The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the International Program Committees for their hard work during the review process. This is a crucial issue for creation of a high standard conference and the SOCO conference would not exist without their help.