Jose Antonio Ramos-Hernanz

A Neural Network Approximation of L-MCRS Dynamics for Reinforcement Learning Experiments

The autonomous learning of the control of Linked Multicomponent Robotic Systems (L-MCRS) is an open research issue. We are pursuing the application of Reinforcement Learning algorithms to achieve such control. However, accurate simulations needed for RL trials are time consuming, so that the process of training and validation becomes excesively long. In order to obtain results in affordable time, we perform the approximation of the detailed dynamic model of the L-MCRS by Artificial Neural Networks (ANN).

ANN Based Model of PV Modules

In this paper authors address the practical problem of designing an empirical model for a commercial photovoltaic (PV) module (Mitsubishi PV-TD1185MF5) placed at the Faculty of Engineering of Vitoria (Basque Country University, Spain) based on artificial neural networks (ANN). This model obtains Ipv from Vpv, and the paper explains how the empirical data have been gathered and discusses the obtained results. The model reached an average accuracy of 0,15 A and a medium correlation value of R = 0,995.

Artificial Neural Network Modeling of a Photovoltaic Module

This paper deals with the problem of designing an accurate and computationally fast model of a particular real photovoltaic module. There are a number of well known theoretical models, but they need the fine tuning of several parameters, whose values are often is difficult or impossible to estimate. The difficulty of these calibration processes has driven the research into approximation models that can be trained from data observed during the working operation of the plant, i.e. Artificial Neural Network (ANN) models. In this paper we derive an accurate ANN model of a real ATERSA A55 photovoltaic module, showing all the steps and electrical devices needed to reach that objective.

Intra and Intergroup Cooperative Learning in Industrial Informatics Area

This paper describes an educational experiment carried out by an Educational Innovation Project (EIP), developed in the field of Industrial Informatics during the biennium 2011/2013 at the Faculty of Engineering of Vitoria-Gasteiz (University of the Basque Country, UPV/EHU, Spain). In this paper the general situation regarding the European Higher Education Area (EHEA) at the start of that biennium, as well as the situation and specific problems that occurred in the field of Industrial Informatics at the Faculty are described. It was proposed to rectify the situation by the explicit formulation of ambitious objectives and the use of active learning methods, specifically by intragroup (between members of the same group) and intergroup (between members of different groups) cooperative learning. The paper includes the designing details of the proposed innovation carried out, the designed assessment, and the steps taken for the implementation in each one of the two years of the project. The results have been successful in the academic field since the specific and generic competences have been achieved and even from the point of view of the evaluation of teachers by students.

Dual Learning in the Bachelor’s Degree in Automotive Engineering at the Faculty of Engineering of Vitoria-Gasteiz: Quality Requirements

Since 2017–2018 academic year, the Bachelor’s Degree in Auto-motive Engineering has been implemented at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, UPV/EHU) in dual format, based on the complementarity of the University and company learning environments. With this type of education the aim is to achieve greater motivation of the students and facilitate the work insertion by having greater contact with companies. In this way, companies are fully integrated with the agents who are responsible for the education of university students. The teaching institutions need to accredit Bachelor’s degrees or itineraries that are taught using this training format (dual training). In order to get this purpose, the university institutions adapt their studies in dual format to the quality requirements proposed by the state quality agencies of the university systems, based on objective evaluation criteria.

Electrical Behavior Modeling of Solar Panels Using Extreme Learning Machines

Predicting the response of solar panels has a big potential impact on the economical viability of the insertion of alternative energy sources in our societies, diminishing the dependence on polluting fossil fuels. In this paper we approach the modeling of the electrical behavior of a commercial photovoltaic module Atersa A-55 using Extreme Learning Machines (ELMs). The training and validation data were extracted from the response of a real photovoltaic module installed at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). The resulting predictive model has one input (\(V_{PV}\)) and one output (\(I_{PV}\)) variables. We achieve a Root Mean Squared Error (RMSE) of 0.026 in the electrical current measured in Amperes.

Neuronal Electrical Behavior Modeling of Solar Panels

In this paper authors model the electrical behavior of a commercial solar panel composed of solar cells connected in series through an Artificial Neural Network (ANN) with one hidden layer. The real solar panel that has been used as proof of concept is of the commercial model ATERSA A55, and it is placed at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). The resulting model consists on one input (\(V_{PV}\)) and one output (\(I_{PV}\)), since the standard deviation of the temperature and irradiance magnitudes in the used dataset was residual.

Flow Control Devices for Wind Turbines

The following chapter provides an overview about available knowledge, references and investigations on the active and passive flow control devices, initially developed for aeronautic industry that are currently being investigated and introduced on wind turbines. The main goal pursued with the introduction of these devices is to delay the boundary layer separation and enhance/suppress turbulences. The aim is to achieve a lift enhancement, drag reduction or flow-induced noise reduction among other parameters. However, achieving these goals present some issues, because the improvement of one of these parameters may suppose an undesired effect in another. For this reason it is necessary to study in detail each one of these devices, their operating concept, applications and their main advantages and drawbacks. Depending on the flow control nature, devices can be classified as actives or passives. Passive techniques allow to improve the performance of the wind turbines without external energy expenditure whereas active techniques require external energy for their activation. There are a lot of devices and in this chapter there have been compiled some of the most important ones, both passives devices (Vortex Generators , Microtabs, Spoilers, Fences, Serrated trailing edge) and actives devices (Trailing edge flaps, Air Jet Vortex Generators, Synthetic Jets).

Welcome Program for First Year Students at the Faculty of Engineering of Vitoria-Gasteiz. Soft Skills

It is well known that there are high dropout rates in engineering faculties. There are plenty of reasons that can explain these bad results, among which we can mention group integration problems, lack of motivation or difficulty to adapt to the university study methodology. In this work we propose a Four-day Welcome Program for first-year students to help these new students to overcome some difficulties they will have to face during their time at the Engineering School. Our objective is to improve the results of the first year students by motivating them from the first day.