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Copyright of Revista Iberoamericana de Ingeniería Mecánica is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Nicolaita de Estudios Económicos is the property of Universidad Michoacana de San Nicolas de Hidalgo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Scientia et Technica is the property of Scientia et Technica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iberoamerican Journal of Industrial Engineering is the property of Iberoamerican Journal of Industrial Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cuaderno Activa is the property of Tecnologico de Antioquia, Institucion Universitari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents comparison of Standardization and Scaling methods in cost predicting. Four methods were used for pre-processing dataset; after that, data was processed through artificial neural network (RNA). The first step was to build common variables in information projects based on opinions of some project managers. Second step was to simulate dataset based on information provided by CRConsulting. Third one was process data with machine learning according to the four methods proposed, RNA algorithms were the same in four cases. Last, the comparison results were presented through adjustment models according to the applied method. [ABSTRACT FROM AUTHOR]

Copyright of Informador Técnico is the property of Servicio Nacional de Aprendizaje SENA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Dyna is the property of Universidad Nacional de Colombia, Medellin, Facultad de Minas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Among the determining factors in export activity, many studies have high- lighted the relevance of the marketing mix. Generally, the majority of them use a variables analysis to focus on specific strategies, in particular, standardized-adaptations. This paper analyzes if there is an interactive effect of strength generated in different variables of the marketing mix that can be associated with different export profiles. The Extreme Learning Machine (ELM) algorithm has been used within the Multilayer Perceptron (MLP) of Artificial Neural Networks (ANN). In addition, the analyses combine a novel approach for sensitivity analysis developed ad hoc for this paper to determine the individual and interactive effects of predictable variables on the dependent variable in classification problems of a dichotomous nature. The results obtained allow us to confirm the existence of the postulated interactive effects, simultaneously revealing the usefulness of ANN and of the sensitivity analysis proposed for research in the area of marketing and, specifically, in firms' internationalization studies. [ABSTRACT FROM AUTHOR]

In this paper, the creation and application of two Intelligent Virtual Environments (IVE) with Artificial Neural Networks (ANN) are presented. In one EVI, the diagnosis of vision problems like astigmatism, myopia and hyperopia is studied. The other one focuses to the perception and reasoning of warning signals in a work environment. For the development of this paper, the characterization of Artificial Neural Networks is done, followed by the simulation; according to the results one network architecture is selected (Multilayer Perceptron) and then implemented in the IVE. Finally the time constraints in ANN learning and in computational cost applied to IVE are discussed. [ABSTRACT FROM AUTHOR]

This paper is an advance of the research project entitled "Development of hybrid algorithms for data mining" and presents the use of a neural network with the simulated annealing algorithm to perform the prediction of a training data set. First, it addresses the problem to be solved, which is oriented to the analysis of the techniques defined for the hybrid algorithm. Then, the applied research methodology (descriptive-exploratory scientific with experimental approach) is justified. We performed a review of the techniques selected for the hybrid neuronal networks and simulated annealing technique which is applied to a set of experimental data associated with determining in a group of patients whether their spine is normal or abnormal. Then, the tests of analysis and results are presented. [ABSTRACT FROM AUTHOR]

Copyright of Dyna is the property of Universidad Nacional de Colombia, Medellin, Facultad de Minas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a methodology for load estimation of power transformers by means of artificial neural networks. To implement the proposed methodology the data of two power transformers, located in different geographical areas and with different operational conditions, were considered. The load estimation methodology was implemented using historical data provided by the company Interconexión Eléctrica S.A. (ISA). To predict the load curves a neural network was trained using MATLAB, being able to fit a load curve with daily and weekly prediction times. The proposed method allows the estimation of load curve values in power transformers with an average percent relative error of around 10%. The method described in this paper can be applied to other equipment with similar operating characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería y Universidad is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingeniería y Universidad is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Facultad de Ingeniería Universidad de Antioquia is the property of Universidad de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Scientia et Technica is the property of Scientia et Technica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Politécnica is the property of Politechnico Colombian Jaime Isaza Cadavid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a controller performance that is develop employing an adaptive B-spline neural network algorithm for adjusting the rotor speed of the permanent magnet synchronous motor. It includes a comparative analysis with three control strategies: conventional proportional integral, sliding mode and fuzzy logic. Also, gives a systematic way to determine the optimal control gains and improve the tracking error performance. A methodology for the adaptive controller and its training procedure are explained. The efficacy of the proposed method is analyzed using time simulations where the motor is subjected to disturbances and reference changes. The proposed control technique exhibits the best performance because it can adapt to every condition, demanding low computational effort for an on-line operation and considering the system nonlinearities. [ABSTRACT FROM AUTHOR]

Optimal operation of energetic installations must considerably influence on its efficiency. By this, the objective of the present paper consists in the definition of the required models structure for the operation of downdraft gasifiers and its identification based on systemic analysis, it identification and the determination of the better adjustment type of model. For the models identification an experimental installation was constructed using, between other biomasses, firewood. Starting from an 3N experimental plan, with three replicas, needed models were identified helped by artificial neural networks and regression techniques. The best results were obtained using neural network, being therefore the most advisable in the preliminary stage of the process of the adaptive construction of operation models of the studied process. [ABSTRACT FROM AUTHOR]

Time series prediction is an important research problem due to its implications in engineering, economics, finance and social sciences. An important topic about this problematic is the development of new models and its comparison with previous approaches in terms of forecast accuracy. Recently, support vector machines (SVM) have been used for time series prediction, but the reported experiences are limited and there are some problems related to its specification. The aim of this paper is to propose a novel technique for estimating some constants of the SVM usually fixed empirically by the modeler. The proposed technique is used to estimate several SVM with the aim of forecast five benchmark time series; the obtained results are compared with the statistics reported in other papers. The proposed method allow us to obtain competitive SVM for the time series forecasted in comparison with the results obtained using other most traditional models. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería y Competitividad is the property of Universidad del Valle and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cuadernos de Administración (01203592) is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Pediatría de Atención Primaria is the property of LUA Ediciones 3.0 S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Globalization is a multidimensional phenomenon that includes the commercial, financial, technological and macroeconomic factors, which have different effects on household income inequality within countries. This paper studies the interrelation between this variables to identify the main factors of each dimension and the direction of its effect through the Sensitivity Analysis of Artificial Neural Networks. This data mining tool finds out non-linear relationships with a non-parametric approach. The main factors that reduce inequality includes credit and spending on research and development, control of corruption and population growth, a credible position in the financial account and trade globalization. [ABSTRACT FROM AUTHOR]

Environmental noise is a big problem related to the environmental pollution in cities, which affects the quality of people life. In this paper, a methodology that uses an approach based on Volunteered Geographic Information (VGI) for the monitoring, analysis and prediction of environmental noise is proposed. It can be very useful to propose alternatives and initiatives that improve the life in a city. So, this work is composed of the following stages: data acquisition, analysis and, data processing, as well as the information visualization, considering the temporality of the same and taking into account macro and micro levels of analysis for the study surface. In addition, some details of the design and development of a geographic information system are presented, consisting of a web-mapping system, an application for mobile devices called "NoiseMonitor", geospatial analysis and machine learning methods (support vector machines and artificial neural networks) for the prediction of environmental noise; by using contextual information; that is, some data related to the city. This kind of work seeks to take advantage of the willingness of citizens to participate collaboratively to sense their environment and be considered as human sensors, which unlike traditional approaches, the cost associated with the development and implementation of this project is much lower. Likewise, a case study based on the Mexico City is presented and discussed, particularly the fourth quadrant of the Historic Center of the City, which is very representative for the variety of environmental noise that is generated in that area. The application domain of this approach is oriented towards big data from a collaborative perspective, Internet of Things and smart cities. [ABSTRACT FROM AUTHOR]

In this paper describes the design and simulation of a two-stage hybrid controller for the inverted reaction wheel pendulum (RWP) is presented. In the first stage, the general pendulum arm modeling is performed through a nonlinear model to determine the stored energy in the plant, and using a strategy known as swing up energy regulation, the data required for the training of an artificial neural network are obtained. In the second stage, via a soft switching system, neuronal control is exchanged by a controller based on the extended linearization of the state variables, whereby the permanence of the pendulum in the region of operation is guaranteed. The control strategy proposed shows excellent performance against external disturbance phenomena and ensures the overall operation of the physical system. [ABSTRACT FROM AUTHOR]

This paper shows the development and use of a hybrid algorithm of machine learning for the missing values predict task, with this task that is carried out while the data preprocessing phase. Firstly, we go through the problem to solve, which is pointed to the study and analysis of different techniques for the missing values predict in order to suggest a hybrid technique as a product of this research for the task and link it to the OLAP (On-Line Analytical Processing) technology. Then, justifying the research methodology (scientific descriptive - probing) applied in this project. The techniques review was carried out filling out the missing values; based on the techniques proof and the study cases, k-Nearest Neighbors algorithms and artificial neural networks were selected and a hybrid technique (KMediaSom) was suggested, applied to a synthetic data set and a real one, coming from a OLAP; for the algorithms implementation was used Matlab. Right away, the analysis and results are set out in order to specify its applicability about efficacy and time complexity. Results are suitable as for the synthetic data set as for the real one; according to the test signs achieved. Finally, the conclusions, where it i's proved that the technique or hybrid algorithms generate better results than the techniques used by separately. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of EMPIRIA: Revista de Metodología de Ciencias Sociales is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Tecno Lógicas is the property of Instituto Tecnologico Metropolitano and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Companies engaged in the manufacture, marketing and maintenance of cylinders for liquefied petroleum gas in Colombia, stamped steel plates and welded to the product a unique serial code to be identified within the cylinder of the country park. Currently, the identification process is manual and checked approximately 7000 cylinders per day in a single factory. The main objective of this paper is to present a vision system that uses artificial neural networks to recognize the code. This system consists physically of a portable device that controls light environment and scene for the acquisition of images. Another component of the system is to adjust the image. The adjustment is based on median filtering, binarization, label, and segmentation, this processing allows more meaningful information and image discrimination. Finally, the intelligent component identification is performed with Hopfield neural networks and an algorithm that checks the development of image recognition. The effectiveness of the system was reported with experimental results obtained on the basis of error with a significant number of samples. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ciencia en su PC is the property of Instituto de Informacion Cientifica y Tecnologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper the most important elements that characterize the secondary circuits of cold water in centralized air conditioning systems for Ice water in Cuban hotels are exposed. By means of an experimental station the energy potentialities of the use of the variable frequency drive in a pumping system were demonstrated. Starting from a building of 59 rooms of a hotel, some thermal and hydraulics characteristic for different operation conditions are presented. It allows for validating the energy behaviour presents in to variable flow drive systems. The most outstanding characteristics in the climatology of the tourist region analyzed are exposed. From these data and the occupational percent of the hotel, the thermal load of the building was calculated for a representative year. With this results an artificial neural network was trained to predict the behaviour with 2.6 % standard deviation. [ABSTRACT FROM AUTHOR]

Copyright of Adicciones is the property of Sociedad Cientifica Espanola de Estudios sobre el Alcohol and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes the design, implementation and testing of the software tool designated as UV-SRNA-PDA (Universidad del Valle's artificial neural network simulator for a personal digital assistant), which is oriented to artificial neural networks simulation and complex industrial processes identification. This application works on a PDA (personal digital assistant) Palm T5 using a customized data acquisition system. Two kinds of artificial neural networks were implemented: perceptron and multi-layer perceptron (MLP) using as learning algorithms the following: backpropagation, descendent gradient, variable learning rate and momentum. To test the proposed system, first and second order systems were selected (only the latter is reported here), finding their neural models and validating their results with MATLAB and UV-SRNA 2.0 (PC version of UV- SRNA-PDA). These tests achieved an average training error of 5.62 x 10-3 ± 3.55 x 10-4 and an average validating error of 4.56 x 10-3 ± 5.95 x 10-4. In both cases, the results were better or comparable with those from other software simulation tools. However, the typical training time on the UV-SRNA-PDA was 900 s compared to 3 s in MATLAB and 8 s in UV-SRNA 2.0. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia, Docencia y Technologia is the property of Universidad Nacional de Entre Rios and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Espacios is the property of Talleres de Impresos Oma and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Informador Técnico is the property of Servicio Nacional de Aprendizaje SENA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Archivos Venezolanos de Farmacología y Terapéutica is the property of Archivos Venezolanos de Farmacologia y Terapeutica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Cubana de Ciencias Forestales is the property of Universidad de Pinar del Rio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Prospectiva (1692-8261) is the property of Universidad Autonoma del Caribe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)