Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm

Prediction models based on artificial intelligence techniques have been widely used in Time Series Forecasting in several areas. They are often fuzzy models or neural networks. This paper describes the development of neural and fuzzy models for forecasting time series of practical examples, and shows the comparisons of results between models, including the results of statistical modeling. The use of data clustering algorithms like Fuzzy C-Means is considered in fuzzy models.     

Synthesis and characterization of nanocomposites of thermoplastic polyurethane with both graphene and graphene nanoribbon fillers

Thermoplastic polyurethane (TPU) nanocomposites containing graphene and graphene nanoribbons were obtained by polymerizing 1,4-butanediol with two diisocyanates (namely, 1,6-hexane diisocyanate or isophorone diisocyanate), in which the nanofillers were previously dispersed. Raman spectroscopy and Transmission Electron Microscopy demonstrated the formation of few-layer graphene and graphene nanoribbons dispersed in the monomers. At variance to the methods commonly reported in literature, that used in this work consists of the direct exfoliation of graphite without any chemical manipulation. Apart from the obvious cost and ease advantages, the so-obtained graphene does not contain any carboxy or alkoxy groups formed during the exfoliation process, which, at variance, are typically present in the most commonly reported methods. This finding paves the way toward the large-scale production of graphene and its nanoribbons, which are considered even more interesting than graphene itself for many potential applications. The obtained nanocomposites show a peculiar thermal and rheological behavior due to the presence of the nanofillers and to their reinforcing or plasticizing effect exerted on the TPU matrices.     

Ontology-based approach to Basic Grafcet formalization

In recent years, various proposals have been put forth to formalize the Grafcet graphical language. The objective of this paper is to propose an ontology-based approach to formalize this language. The authors have implemented a semi-coarse grain ontology written in Ontology Web Language and have tested it by including it in an existing educational tool for the teaching of Grafcet language for use in programmable logic controllers.     

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.     

Modelling commuting time in the US: Bootstrapping techniques to avoid overfitting

The research on commuting has emerged in recent decades, but the issue of whether the empirical techniques used are appropriate has not been analysed. Thus, results from prior research could be based on non-accurate models, leading to misleading conclusions. We apply an algorithmic approach based on bootstrapping, variable selection, and mean absolute prediction errors, which is designed to avoid overfitting. Using the American Time Use Survey, we find that models with a reduced set of explanatory variables have similar accuracy to standard econometric models. Our results shed light on the importance of determining whether models can be overfitted.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

Iberian universities: a characterisation from ESI rankings

The access to bibliographic and citation databases allows to evaluate scientific performance, and provides useful means of general characterisation. In this paper we investigate the clustering of Iberian universities, resulting from the similarity in the number and specific nature of the scientific disciplines given by the Essential Science Indicators database. A further refining of the analysis, as provided by PCA, clearly reveals the relationship between the universities and the scientific disciplines in the main groups. Similarity between universities is not dictated only by the number of areas in the ranking, but also stems from the nature of the ranked scientific areas and the specific combination in each university.     

Perfluoropolyether polymers by UV curing: design,synthesis and characterization

Perfluoropolyether (PFPE) structures can be functionalized with acrylic groups using appropriate hydrogenated acrylic monomers: the macromers obtained are highly reactive under UV irradiation, and fluorinated polymers can be obtained. In the first part of this work is described the synthesis of new PFPE (meth)acrylic oligomers by extending OH‐terminated fluorinated chains with urethane groups and reactive acrylic functions. The photopolymerization reaction of each product is then reported followed by the characterisation of the main thermal, mechanical and surface properties of UV‐cured coatings. The polymers have good thermal resistance and fair mechanical and chemical resistance. More interestingly they show very low refractive index and low surface tension. For these latter properties the products can be advantageously used in highly demanding applications such as photonic devices and nano‐patterning. Copyright © 2011 Society of Chemical Industry