tiphys an open networked platform for higher education on industry 4 0

Abstract Objective of Tiphys project is building an Open Networked Platform for the learning of Industry 4.0 themes. The project will create a Virtual Reality (VR) platform, where users will be able to design and create a VR based environment for training and simulating industrial processes but they will be able to study and select among a set of models in order to standardize the learning and physical processes as a virtual representation of the real industrial world and the required interactions so that to acquire learning and training capabilities. The models will be structured in a modular approach to promote the integration in the existing mechanisms as well as for future necessary adaptations. The students will be able to co-create their learning track and the learning contents by collaborative working in a dynamic environment. The paper presents the development and validation of the learning model, built on CONALI learning ontology. The concepts of the ontology will be detailed and the platform functions will be demonstrated on selected use cases

The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient

[EN] In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three "problem transformation" methods are tested and compared. For the feature extraction stage, the startup current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multi-label framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.This work was supported in part by the Spanish MINECO and FEDER program in the framework of the "Proyectos I + D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia" under Grant DPI2014-52842-P and in part by the Horizon 2020 Framework program DISIRE under the Grant Agreement 636834.Georgoulas, G.; Climente Alarcón, V.; Antonino-Daviu, J.; Tsoumas, IP.; Stylios, CD.; Arkkio, A.; Nikolakopoulos, G. (2016). The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient. IEEE Transactions on Industrial Informatics. 13(2):625-634. https://doi.org/10.1109/TII.2016.2637169S62563413

Transformation of Robotics Education in the Era of Covid-19: Challenges and Opportunities

The COVID-19 pandemic has significantly impacted many aspects of our social and professional life. To this end, Higher Education institutions reacted rather vastly to this unpreceded situation although many issues have been reported in the international literature since the emergence of the first global lockdown. As we are now transitioning back to the ‘normality’, universities and businesses consider the so-called ‘blended’ or ‘hybrid’ model as a means of facilitating the transition phase. In view of this decision, several studies can be identified wherein blended learning scenarios are proposed and described. The present work constitutes such an effort. Precisely, while adjusting the lens to the didactic of Robotics courses, we propose a blended learning model via which the laboratory activities are performed without the physical presence of the students in the physical context. The aforementioned objective is attained under the aid of the Virtual Reality technology coupled with the Digital Twin model. We hope that the ideas presented in this manuscript will motivate and inspire more researchers, instructional designers, and educators to consider the adoption of such alternative instructional techniques to mitigate the shortcomings that the remote education setting brings and further to improve the overall learning experience

A Virtual Reality Laboratory for Blended Learning Education: Design, Implementation and Evaluation

Launched during the pandemic, the EU-funded JANUS project aimed to ensure the continuity of student workshops at universities using a virtual reality (VR) robotics laboratory. With the return to normality, the project has been redesigned to capitalise on the positive outcomes of the experience. The VR lab provides safe and unrestricted access to the labs and experiments with the machines, reducing the consequences of student mistakes and improving the user experience by allowing the experiment to be repeated from different angles, some of which are impossible to access in the real lab. In addition, integration with an interactive learning platform called “ViLLE” allows for continuous assessment of the learning experience. Self-evaluation of the material taught and learned can be integrated with the execution of the exercises that pave the way for Kaizen. Two VR workshops for the blended learning of robotics were developed during the JANUS project. Their evaluation reported favourable responses from the students whose learning performance was indirectly measured

Design, Development, and Evaluation of a Virtual Reality Serious Game for School Fire Preparedness Training

Immersive virtual reality (VR) is a technology that can be effective for procedural skills training through game-based simulations such as serious games. The current study describes the instructional design, development, and evaluation of the FSCHOOL fire preparedness serious game in a cave automatic virtual environment (CAVE-VR) for elementary school teachers. The main game mechanics include a storytelling scenario, enhanced realism, freedom of movement, levels, and points corresponding to the learning mechanics of instruction, action, simulation, discovery, repetition, and imitation. The game was developed in Unity 3D with the help of the Fire Dynamics Simulator and a script to emulate and visualize fire propagation. The game featured three levels to respond to school fire safety regulations and was evaluated by elementary school teachers (N = 33) in Greece. A comparative quantitative study was conducted with experimental and control groups. The results indicate that the VR serious game is appropriate for training, providing challenge, enjoyment, and mastery

Why Fuzzy Cognitive Maps Are Efficient

In many practical situations, the relation between the experts\u27 degrees of confidence in different related statements is well described by Fuzzy Cognitive Maps (FCM). This empirical success is somewhat puzzling, since from the mathematical viewpoint, each FCM relation corresponds to a simplified one-neuron neural network, and it is well known that to adequately describe relations, we need multiple neurons. In this paper, we show that the empirical success of FCM can be explained if we take into account that human\u27s subjective opinions follow Miller\u27s seven plus minus two law

Dow Theory\u27s Peak-and-Trough Analysis Justified

In the analysis of dynamic financial quantities such as stock prices, equity prices, etc., reasonable results are often obtained if we only consider local maxima ( peaks ) and local minima ( troughs ) and ignore all the other values. The empirical success of this strategy remains a mystery. In this paper, we provide a possible explanation for this success

Development of weather application for enhancing sea safety and rescue operations

mong many issues which affect the marine-related daily activities, the weather conditions are considered one of the most important. In this study, all the tools and the methods were used to develop a modern weather application which is able to support the quality of vessel services in the sea safety and rescue operations, is presented. The weather application can operate using as basic data source continuous measurements from a mobile meteorological station on a pilot vessel. The meteorological station records automatically all the measurements of its sensors while appropriate modules in the internal structure of the system analyze the initial information and provide final outputs. In the framework of the LINCOLN Project where new concepts of added-value specialized vessels and relative automated services, are developed and promoted, this application is intended to provide accurate and on-site information about weather conditions

Symbolic Aggregate ApproXimation (SAX) under Interval Uncertainty

In many practical situations, we monitor a system by continuously measuring the corresponding quantities, to make sure that an abnormal deviation is detected as early as possible. Often, we do not have ready algorithms to detect abnormality, so we need to use machine learning techniques. For these techniques to be efficient, we first need to compress the data. One of the most successful methods of data compression is the technique of Symbolic Aggregate approXimation (SAX). While this technique is motivated by measurement uncertainty, it does not explicitly take this uncertainty into account. In this paper, we show that we can further improve upon this techniques if we explicitly take measurement uncertainty into account