EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network

This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder–Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool.     

Genetic algorithms to solve the power system restoration planning problem

This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.     

A strategy for allowing meaningful and comparable scores in approximate matching

Approximate data matching aims at assessing whether two distinct instances of data represent the same real-world object. The comparison between data values is usually done by applying a similarity function which returns a similarity score. If this score surpasses a given threshold, both data instances are considered as representing the same real-world object. These score values depend on the algorithm that implements the function and have no meaning to the user. In addition, score values generated by different functions are not comparable. This will potentially lead to problems when the scores returned by different similarity functions need to be combined for computing the similarity between records. In this article, we propose that thresholds should be defined in terms of the precision that is expected from the matching process rather than in terms of the raw scores returned by the similarity function. Precision is a widely known similarity metric and has a clear interpretation from the user's point of view. Our approach defines mappings from score values to precision values, which we call adjusted scores. In order to obtain such mappings, our approach requires training over a small dataset. Experiments show that training can be reused for different datasets on the same domain. Our results also demonstrate that existing methods for combining scores for computing the similarity between records may be enhanced if adjusted scores are used.     

Real-time collaboration of virtual laboratories through the Internet

Web-based learning environments are becoming increasingly popular in higher education. One of the most important web-learning resources is the virtual laboratory (VL), which gives students an easy way for training and learning through the Internet. Moreover, on-line collaborative communication represents a practical method to transmit the knowledge and experience from the teacher to students overcoming physical distance and isolation. Considering these facts, the authors of this document have developed a new dynamic collaborative e-learning system which combines the main advantages of virtual laboratories and collaborative learning practices. In this system, the virtual laboratories are based on Java applets which have embedded simulations developed in Easy Java Simulations (EJS), an open-source tool for teachers who do not need complex programming skills. The collaborative e-learning is based on a real-time synchronized communication among these Java applets. Therefore, this original approach provides a new tool which integrates virtual laboratories inside a synchronous collaborative e-learning framework. This paper describes the main features of this system and its successful application in a distance education environment among different universities from Spain.     

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently.The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.     

A model of the behavior of the limiting current oxygen sensors

A physical model describing the I(V) characteristics of planar limiting current oxygen sensors based in an ion-conducting electrolyte is presented. The model focuses on the minimization of the electrochemical potential and the diffusion across a porous layer covering the measuring electrode. The proposed model describes three key phenomena in the response of the sensors: the influence of the polarization resistance on the slope of I(V), the linearity between the limiting current and the diffusivity of the porous layer and a low non-zero current at zero polarization.     

Traffic analysis and synthetic models of second life

Virtual Worlds (VW), such as Massive Multiplayer Online Social Games, have been gaining increasing attention in the last few years, mainly due to the new way users interact with them. However, little effort has been devoted to understand their traffic profile and the implications to the traffic management area. With the current growing rate of VWs’ usage, their traffic demand could eventually impose a significant burden on the operation of a typical Internet Service Provider (ISP) network. In this paper, we seek to understand the traffic behavior of an increasingly popular VW application, namely Second Life (SL), from both the connection and network level perspectives. We also show results of a traffic analysis of a SL client, when an avatar performs different actions in the VW, at different places and under different network conditions. Our results show that SL makes intensive use of network resources (mostly bandwidth), since the capacity needed for having a full SL experience (listening to live music) may reach 500 kbps. We then extend the traffic analysis work on SL by providing analytical models for its traffic profile. We aim at helping the network management and planning area to estimate the impact of an intense use of such VW on access links or core networks. Such synthetic models are also useful to the networking research community for the use in network simulations.     

MPC for battery/fuel cell hybrid vehicles including fuel cell dynamics and battery performance improvement

In this paper, the performance and durability of hybrid PEM fuel cell vehicles are investigated. To that end, a hybrid predictive controller is proposed to improve battery performance and to avoid fuel cell and battery degradation. Such controller deals with this complex control problem by handling binary and continuous variables, piecewise affine models and constraints. Moreover, the control strategy is to track motor power demand and keep batteries close to a desired battery state of charge which is appropriately chosen to minimize hydrogen consumption. It is important to highlight the consideration of constraints which are directly related to the goals of this paper, such as minimum fuel cell power threshold and time limitation between fuel cell startups and shutdowns. Furthermore, different models have been elaborated and particularized for a vehicle prototype. These models include few innovations such as a reference governor which smooths fuel cell power demand during sharp power profiles, forcing batteries to supply such peaks and resulting a longer fuel cell lifetime. Battery thermal dynamics are also taken into account in these models in order to analyze the effect of battery temperature on its degradation. Finally, this paper studies the feasibility of the real implementation, presenting an explicit formulation as a solution to reduce execution time. This explicit controller exhibits the same performance as the hybrid predictive controller does with a reduced computational effort. All the results have been validated in several simulations.