ABSTRACT
According to the COVID-19 pandemic, educational institutions around the world were forced to urgently switch to distance learning. A particularly difficult situation has arisen in higher educational institutions when teaching special technical disciplines, where practical and laboratory exercises, which were conducted in laboratories and special classes before the COVID-19 pandemic, play an important role. Therefore, the paper presents a method for conducting practical and laboratory classes during on-line training on the example of the Department of Automotive Electronics of the Kharkiv National Automobile and Highway University (Ukraine). It is shown that the use of information technologies makes it possible to largely compensate for the lack of full-time classes. For special (subject) disciplines, the perception of information is achieved by using films demonstrating methods for the implementation of subject tasks. The main task of the teacher when conducting practical exercises in the conditions of distance learning is the competent selection of the source video material and its optimal fragmentation. In this regard, a classification of educational video content is proposed, and examples of its use by teachers during online classes are considered. The results of this work are intended to help educators improve the level of engineering education during distance learning. © 2022 IEEE.
ABSTRACT
In this article, a system which combines computer vision with electrical devices for enrichment is proposed in order to prevent risks that can occur in the future and contribute to reducing the spread of COVID-19 and other contagious infections that can happen in the future as well. According to the researches related to the current pandemic situation (COVID-19), the policies that should be taken are clear such as social distancing and wearing masks, but due to many reasons depending on human behaviors, people lose their attention to keep the rules. This system will help to prevent these risks. In this work, occupations that contact many people daily, but not under the control, sterilized area, and not affordable to work from home were considered as well as the target objects in our system. Contribution in this research work is to develop an expanded monitoring system which helps to prevent and reduce risk of the pandemic continuing currently and can occur in future by combining computer vision with electrical sensors to increase its efficiency. Machine learning based image recognition techniques were used to detect and monitor specified actions. In order to increase its functionality and efficiency, thermal sensors, camera, and microphones were deployed as well. Therefore, IoT was used for collecting the data result from monitoring for future applications. © 2021 IEEE.
ABSTRACT
Engineering and science curricula are revamped to address sustainability as a cross cutting horizontal issue in order to have an impact on how students appreciate, understand and think critically about complex environmental, social and economic problems. Key to maximizing impact is the dissemination of a novel practice as well as its outcomes and outputs. The target of the ERASMUS+ project 'Electrical Energy Markets and Engineering Education - ELEMEND' is the capacity building of academic and teaching staff, students, electro engineering staff, employers as well as the general public. ELEMEND also aspires to create a favorable environment for energy related business and to modify the electricity user's behavior in the Western Balkan Countries in line with technological developments in smart grids. In order to ensure the sustainability of the ELEMEND results, a Dissemination and Exploitation Plan was elaborated by each of partner university taking into account all relevant stakeholders and key actors. In particular, we examine how the dissemination plan of Mediterranean University (MU), a partner university of the ELEMEND project, sets the targets to be achieved through the dissemination activities which make the project's objective and results visible and usable to all potential stakeholders. We focus on the dissemination activities of the Mediterranean University, we analyze applied methods and forms, we provide examples of how Mediterranean University pursued dissemination targets under Covid-19 conditions. © 2021 IEEE.
ABSTRACT
Current article try to find the answer to the question - how to make public transport system safer during the pandemic and after? Introduction shows the effect of pandemic to transport sector and to public transport system in particular. Second chapter deals with the recommendations for adapting the public transport system to the COVID-19 challenge, ensuring its sustainability. The short framework should help to return the public transport system to sustainable position. Third chapter deals with technologies for disinfection developed by Riga Technical University and partners, to reduce the effect of pandemic. These technologies are automated and robotic equipment for air and surface disinfection;disinfection gate and equipment for surface, as well as, air disinfection with UV radiation and ozone. Authors hope, that all offered devices could be used in different premises to make public transport system safer from the epidemiological point of view by making the working environment cleaner from viruses and helping all the employees to feel safer. © 2021 Kaunas University of Technology. All rights reserved.
ABSTRACT
Due to COVID-19 and its rapid spread all over the world, the daily life of people all around the world has an effect on many levels, and people are trying to get along with the pandemic situation and support sustainability of development in every field. Public transportation is a backbone of sustainable urban infrastructure development and has to be safe and reliable in this situation. In this research work, an IoT-based system that combines machine learning with electrical sensors for enrichments is proposed in order to prevent risks that can occur in the future. The proposed monitoring system targeted bus drivers during their work time due to their high risk of spread if they were infected. The system detects if a driver does restricted actions such as not wearing a mask, using it improperly, touching their faces, or shows some of the symptoms of COVID-19, the device equipped in front of them shows an alert and sends an action log to the centralized server. It prevents actions that can cause infection from the virus and collects data that can be used for many purposes, analysis, and other possible services as well. Machine learning based image recognition techniques were used to detect and monitor specified actions. In order to increase its functionality and efficiency, thermal sensors, cameras, and microphones were deployed as well. Therefore, IoT was used for collecting the data resulting from monitoring for future applications. © 2021 Kaunas University of Technology. All rights reserved.
ABSTRACT
The paper analyzes the problems that have arisen in universities in connection with the COVID-19 pandemic using the example of the international educational master's program of double degrees "Electric Vehicles and Energy-Saving Technologies" (EV&EST). Riga Technical University and Kharkiv National Automobile and Highway University are participating in the development of the EV&EST program. A brief description of the EV&EST program and the priority tasks that were set for its implementation are given. Problems that hinder the timely completion of some tasks are identified. A brief description of the distance course, which was developed within the framework of the EV&EST program in the Moodle educational environment, is given. A complex for laboratory and practical exercises based on a solar charging station for electric vehicles, which was developed under conditions of adaptive quarantine, is described. Laboratory and practical exercises have been developed at this complex.
ABSTRACT
In this work we study applicability of Kalman filters as decision support for early warning and emergency response system for infectious diseases as CoVID-19. Here we use only the actual observations of new cases/deaths from epidemiological survey. We investigated the behavior of various time varying measurement driven models. We implement time varying Kalman filters. Preliminary results from Greece and Latvia showed that Kalman Filters can be used for short term forecasting of CoVID-19 cases. The mean percent absolute error may vary by model;some models give satisfactory results where the mean percent absolute error in new cases is of the order of 2%-5%. The mean absolute error in new deaths is of the order of 1-2 deaths. We propose the use of Kalman Filters for short term forecasting, i.e. next day, which can be a useful tool for improved crisis management at the points of entry to a country or hospitals.
ABSTRACT
The development, introduction and evaluation of ICT based techniques have been at the forefront of educational reform of all levels for the last two decades. Microlearning, gamified learning are two novel ICT techniques most appropriate for e-learning material and programs which attracted renewed attention because of the CoVID-19 social distancing measures. Alongside those, the more established technique of practice-oriented learning is being revisited to adapt to the new challenges. This article presents the main results and conclusions of deliverable 3.1. of the ERASMUS+ Capacity Building for Higher Education project "Electrical Energy Markets and Engineering Education (ELEMEND)" which is devoted to training partner staff in micro learning, practice oriented teaching and gamified learning. After the goals of ELEMEND are presented in the light of modern engineering education, the concept of micro learning as a training paradigm is discussed. Next, gamification technology is described in view of its role in the educational process and specific implementation examples are given. Finally, the practice-oriented approach both in teaching and in learning with the methods used to carry on this approach is presented.