Semitransparent monocrystalline solar cells manufactured by laser cutting and anisotropic etching

This paper presents two possible technologies used to manufacture semitransparent monocrystalline building integrated solar cells: laser cutting and anisotropic etching. Nd:YAG 1,064 nm laser cut sidewalls are rough and contain molten residues, resulting in shunts across the through-holes confirmed by I–V characteristic measurement and reverse bias measurement results. Tetramethylammonium hydroxide etched edges are uniform with smooth sidewalls. Carrier lifetime and Kelvin probe measurements reveal the flaws occurring in the two technologies.     

To be human is to be creative

We will assert than in the era of Ubiquitous Technology to be human is to be creative. Small children are experimental and creative actors. The socialisation process in modern societies, both at home and at educational institutions, does not enhance and develop their creativity. On the contrary, their creativity is discouraged in many ways. We conceptualise creativity developmentally: It is possible to use activities, teaching methods, motivation and procedures to enhance and develop creativity, even in older people. This paper gives some guides that can be used both at home and at work to explore, enhance and develop ones own creativity and the creativity of others. Each suggestion is presented from a practical viewpoint and then related to some of the tools and concepts that scientists and artists use in their creative endeavours.     

Ordinal and nominal classification of wind speed from synoptic pressurepatterns

Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are not direct wind measures available. Different approaches have been applied to this reconstruction, such as measure-correlate-predict algorithms, approaches based on physical models such as reanalysis methods, or more recently, indirect measures such as pressure, and its relation to wind speed. This paper adopts the latter method, and deals with wind speed estimation in wind farms from pressure measures, but including different novelties in the problem treatment. Existing synoptic pressure-based indirect approaches for wind speed estimation are based on considering the wind speed as a continuous target variable, estimating then the corresponding wind series of continuous values. However, the exact wind speed is not always needed by wind farm managers, and a general idea of the level of speed is, in the majority of cases, enough to set functional operations for the farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classification task, given that the problem is simplified. Thus, this paper tackles the problem of wind speed prediction from synoptic pressure patterns by considering wind speed as a discrete variable and, consequently, wind speed prediction as a classification problem, with four wind level categories: low, moderate, high or very high. Moreover, taking into account that these four different classes are associated to four values in an ordinal scale, the problem can be considered as an ordinal regression problem. The performance of several ordinal and nominal classifiers and the improvement achieved by considering the ordering information are evaluated. The results obtained in this paper present the support vector machine as the best tested classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to significantly improve ranks with respect to nominal classification, although differences in accuracy aresmall.     

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.     

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.     

Toward the scalability of neural networks through feature selection

In the past few years, the bottleneck for machine learning developers is not longer the limited data available but the algorithms inability to use all the data in the available time. For this reason, researches are now interested not only in the accuracy but also in the scalability of the machine learning algorithms. To deal with large-scale databases, feature selection can be helpful to reduce their dimensionality, turning an impracticable algorithm into a practical one. In this research, the influence of several feature selection methods on the scalability of four of the most well-known training algorithms for feedforward artificial neural networks (ANNs) will be analyzed over both classification and regression tasks. The results demonstrate that feature selection is an effective tool to improve scalability.     

Collective intelligence as mechanism of medical diagnosis: The iPixel approach

Collective intelligence (CI) is an active field of research, which capitalizes the knowledge of human collectives in order to create, to innovate and to invent. There are two important mechanisms to implement CI: recommender and reputation systems. Recommender systems are used to provide filtered information from a large amount of elements. The recommendations are intended to provide interesting elements to users. Recommendation systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This work presents iPixel Recommender Engine, which is focused on the medical field. iPixel Recommendation Engine supports the process of differential diagnosis by recommending mammographic evaluations. Each mammogram is collectively tagged by the users’ community with a semantic sense; this feature allows iPixel acquires collective knowledge. iPixel can associate more than one feature with each mammogram. This work also presents a qualitative evaluation, where the basic features that a recommendation system should have in the medical field were obtained. Finally, a comparison was carried out with other similar recommender systems in order to know the Pixel advantages.     

Component-based technique for determining the effects of acupuncture for fighting migraine using SPECT images

In this work, SPECT brain images are analyzed automatically in order to determine the effects of acupuncture applied for fighting migraine. For this purpose, two different groups of patients are randomly collected and received verum and sham acupuncture, respectively. Changes in the brain perfusion patterns can be measured quantitatively by dealing with the images in a classification context. A classification scheme consisting of a component-based feature extraction technique in combination with Support Vector Machines allows us to accurately determine the regions of interest (ROIs) where acupuncture produced more intense effects, and whether these effects are correlated with a decrease or an increase of the brain activity. Effects produced by verum and sham acupuncture are studied, and the best method for intensity normalization is discussed. The result is a complete, objective system which can be used for general purposes in the visual assessment of perfusion images.     

‘Long autonomy or long delay?’ The importance of domain in opinion mining

Nowadays, people do not only navigate the web, but they also contribute contents to the Internet. Among other things, they write their thoughts and opinions in review sites, forums, social networks, blogs and other websites. These opinions constitute a valuable resource for businesses, governments and consumers. In the last years, some researchers have proposed opinion extraction systems, mostly domain-independent ones, to automatically extract structured representations of opinions contained in those texts. In this work, we tackle this task in a domain-oriented approach, defining a set of domain-specific resources which capture valuable knowledge about how people express opinions on a given domain. These resources are automatically induced from a set of annotated documents. Some experiments were carried out on three different domains (user-generated reviews of headphones, hotels and cars), comparing our approach to other state-of-the-art, domain-independent techniques. The results confirm the importance of the domain in order to build accurate opinion extraction systems. Some experiments on the influence of the dataset size and an example of aggregation and visualization of the extracted opinions are also shown.     

Binary ant colony optimization applied to variable screening in the Mahalanobis–Taguchi System

This work presents the application of the Mahalanobis–Taguchi System (MTS) to a dimensional problem in the automotive industry. The combinatorial optimization problem of variable selection is solved by the application of a recent version of binary ant colony optimization algorithm. Moreover, a comparison with respect to binary particle swarm optimization algorithm is also presented and a discussion regarding the numerical results is given.