A novel combined electrochemical-magnetic method for water treatment

Electrocoagulation (EC) is a wastewater treatment process in which aqueous pollutants can be removed by adsorption, entrapment, precipitation or coalescence during a coagulation step produced by electrochemically generated metallic species. When using Fe as the sacrificial electrode, Fe(2+) and Fe(3+) ions are formed. As Fe(3+) species are paramagnetic, this property can in principle be used to facilitate their removal through the application of a magnetic field. In the present work we present a proof-of-concept for a combined electrochemical-magnetic method for pollutant removal. For this approach, the amounts of Fe(2+) and Fe(3+) produced in an EC cell at various voltages were measured by spectroscopic methods to confirm that Fe(3+) species predominate (up to 84%). The effectiveness of the presence of a magnetic field in the precipitation of coagulants from a suspension was confirmed by monitoring the turbidity change versus time with and without exposure to a magnetic field, up to a 30% improvement.     

Second Life as a support element for learning electronic related subjects: A real case

Looking for more active and motivating methodological alternatives from the students’ perspective, which promote analysis and investigation abilities that make the student a more participative agent and some learning processes are facilitated, a practical study was conducted in the University of La Sabana (Chía, Colombia), in Computing Engineering (INF) and Agroindustrial Production Engineering (IPA) academic programs under the guiding question: Is Second Life an attractive tool for students, does it increment motivation, promote participation and facilitate learning processes in electronic related subjects in Engineering degrees?Second Life (SL) platform was then used as a traditional on-site class complement in electronic related subjects. A follow up was conducted with several groups during a year and a half, some students followed the subject with the traditional on-site methodology and others did it following a mixed methodology that combined traditional on-site lessons with virtual sessions trough SL, making a comparison and applying a quantitative as well as a qualitative methodology for the information analysis and recollection.After the specified study period it was found that using SL as part of the methodology, increments motivation toward electronic related subjects, and promotes participation and investigation, due to the fact that this tool makes learning possible in a funnier, more interactive and deeper way than if it had been taught in a master class. Having real-time access from a virtual classroom to all types of audiovisual information, helps more senses get involved in the learning process, making it more effective; nevertheless a weakness was detected related to the attention and concentration: students tend to get distracted, because their classroom is the computer and from there they have access not only to the many worlds of SL but to all the social networks and the Internet in general.     

A collaborative platform for cognitive decision making in the Knowledge Society

Two key aspects of the Knowledge Society are the interconnection between the actors involved in the decision making processes and the importance of the human factor, particularly the citizen’s continuous learning and education. This paper presents a new module devoted to knowledge extraction and diffusion that has been incorporated into a previously developed decision making tool concerning the Internet and related with the multicriteria selection of a discrete number of alternatives (PRIOR-Web). Quantitative and qualitative procedures using data and text mining methods have been employed in the extraction of knowledge. Graphical visualisation tools have been incorporated in the diffusion stage of the methodological approach suggested when dealing with decision making in the Knowledge Society. The resulting collaborative platform is being used as the methodological support for the cognitive democracy known as e-cognocracy.     

A supervisory loop approach to fulfill workspace constraints in redundant robots

An approach based on geometric invariance and sliding mode ideas is proposed for redundancy resolution in robotic systems to fulfill configuration and workspace constraints caused by robot mechanical limits, collision avoidance, industrial security, etc. Some interesting features of the proposal are that: (1) it can be interpreted as a limit case of the classical potential field-based approach for collision avoidance which requires using variable structure control concepts, (2) it allows reaching the limit surface of the constraints smoothly, depending on a free design parameter, and (3) it can be easily added as a supervisory block to pre-existing redundancy resolution schemes. The algorithm is evaluated in simulation on a 6R planar robot and on the freely accessible 6R robot model PUMA-560, for which the main features of the method are illustrated.     

Hypercatadioptric line images for 3D orientation and image rectification

In central catadioptric systems 3D lines are projected into conics. In this paper we present a new approach to extract conics in the raw catadioptric image, which correspond to projected straight lines in the scene. Using the internal calibration and two image points we are able to compute analytically these conics which we name hypercatadioptric line images. We obtain the error propagation from the image points to the 3D line projection in function of the calibration parameters. We also perform an exhaustive analysis on the elements that can affect the conic extraction accuracy. Besides that, we exploit the presence of parallel lines in man-made environments to compute the dominant vanishing points (VPs) in the omnidirectional image. In order to obtain the intersection of two of these conics we analyze the self-polar triangle common to this pair. With the information contained in the vanishing points we are able to obtain the 3D orientation of the catadioptric system. This method can be used either in a vertical stabilization system required by autonomous navigation or to rectify images required in applications where the vertical orientation of the catadioptric system is assumed. We use synthetic and real images to test the proposed method. We evaluate the 3D orientation accuracy with a ground truth given by a goniometer and with an inertial measurement unit (IMU). We also test our approach performing vertical and full rectifications in sequences of real images.     

Distance maps from unthresholded magnitudes

A straightforward algorithm that computes distance maps from unthresholded magnitudes is presented, suitable for still images and video sequences. While results on binary images are similar to classic Euclidean Distance Transforms, the proposed approach does not require a binarization step. Thus, no thresholds are needed and no information is lost in intermediate classification stages. Experiments include the evaluation of spatial and temporal coherence of distance map values, showing better results in both measurements than those obtained with Sobel or Deriche gradients and classic chessboard distance transforms.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

Intelligent system for time series classification using support vector machines applied to supply-chain

To be able of anticipate demand is a key factor for commercial success in the supply-chain sector. The benefits can be grouped around two main concepts: firstly the optimization of operations through the development of optimal strategies for procurement and secondly the stock reduction that reduces storage costs, handling, etc. There is currently a variety of methods for making predictions, these methods vary from pure statistical methods such as exponential smoothing Holt-Winters or ARIMA models, to those based on artificial intelligence techniques like neural networks or fuzzy systems. However, despite being able to build accurate models, in managing the supply chain based on forecasts there is a problem known as “Forrester effect” irrespective of the model chosen. To monitor the impact of this effect, given the volume of information handled in large corporations, is a very expensive task (often manual) for such corporations because it requires investigating issues such as the adequacy of the model, allocation of known models to the sales time series, discovery of new patterns of behavior, etc. This article proposes an intelligent system based on support vector machines to solve problems concerning the allocation and discovery of new models. With this focus in mind, the system objective is to build groups of time series that share the same forecasting model. For the identification of new models, the system will assign “virtual models” for those groups that do not have a predefined pattern. Using the proposed method, it has been possible to group a sample of more than 14,000 time series (real data taken from a store) in around 70 categories, of which only 12 of them already grouped over 98% of the total.     

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.     

A case based reasoning model for multilingual language generation in dialogues

The process of Natural Language Generation for a Conversational Agent translates some semantic language to its surface form expressed in natural language. In this paper, we are going to show a Case Based Reasoning technique which is easily extensible and adaptable to multiple domains and languages, that generates coherent phrases and produces a natural outcome in the context of a Conversational Agent that maintains a dialogue with the user.