Duino-Based Learning (DBL) in control engineering courses

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis document presents a project to develop freely redistributable materials to conduct educational lab projects with MATLAB, Simulink, Arduino and low-cost plants. This work materials introduce the fundamentals of Control Engineering through exercises and videos. Along with all this, the most important steps and issues appeared in the project are explained, so anyone interested on doing a project can have a starting point instead of starting a project from scratch, which most of times this results hard to implementPeer ReviewedPostprint (author's final draft

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. A modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations

Marcadores hormonales del síndrome de sobreentrenamiento

Los programas de entrenamiento deportivo pretenden mejorar el rendimiento mediante la exposición a cargas de trabajo de intensidad creciente. Sin embargo, ocasionalmente se produce una desadaptación, el denominado Síndrome de Sobreentrenamiento (SSE), que perjudica gravemente el rendimiento y la salud del deportista. Para el diagnóstico del SSE se ha reecurrido a diversos marcadores entre los que cabe destacar los hormonales. La monitorización de las hormonas implicadas en la regulación de las respuestas de estrés permite evaluar el grado de adaptación al entrenamiento. Se concluye que el cociente testosterona/cortisol es, actualmente, el marcador con más posibilidades de ser utilizado como instrumento disgnóstico. Sin embargo, se requiere más investigación para clarificar los criterios y las condiciones de su aplicación a diversas especialidades deportivas. Además, se considera el uso complementario de indicadores psicológicos y la necesidad de estudiar las interrelaciones entre ambos tipos de marcadores para comprender más cabalmente la naturaleza e implicaciones globales del síndromeTraining programmes aim to improve sports performance through workloads of increasing intensity. However, sometimes an imbalance appears, the so called Overtraining Syndrome (OTS), which produces enhanced fatigability, health problems and seriously impairs performance. The ability to differentiate between the desadaptative state of OTS and the appropiate adaptation to training, which also frequently involves fatigue states, is a main problem. For this the assessment of the degree of adaptation to training. Testosterone/cortisol ratio is currently the marker with more possibilities to be used as a diagnostic instrument. However, more research is necessary in order to clarify the criteria and conditions to apply it. In addition, psychological indicators could complement the detection and prevention of this syndrome. The analyses of the interrelationships between physiological and psychological markers is claimed to better understand the nature and global implications of this syndrom

Self-interactions can stabilize excited boson stars

We study the time evolution of spherical, excited (i.e. nodeful) boson star (BS) models. We consider a model including quartic self-interactions, controlled by a coupling Λ. Performing non-linear simulations of the Einstein- (complex)–Klein–Gordon system, using as initial data equilibrium BSs solutions of that system, we assess the impact of Λ in the stability properties of the BSs. In the absence of self-interactions (Λ = 0), we observe the known behaviour that the excited stars in the (candidate) stable branch decay to a nonexcited star without a node; however, we show that for large enough values of the self-interactions coupling, these excited stars do not decay (up to timescales of about t ∼ 104). The stabilization of the excited states for large enough selfinteractions is further supported by evidence that the nodeful states dynamically form through the gravitational cooling mechanism, starting from dilute initial data. Our results support the healing power (against dynamical instabilities) of self-interactions, recently unveiled in the context of the non-axisymmetric instabilities of spinning BSs.publishe