ABSTRACT
Coronavirus biologically named COVID-19 is a disease that is circulating throughout the world due to its viral nature. The interaction of people is a source of spreading of coronavirus. Millions of people have been affected by this virus, and most of them have lost their lives. At present, this viral disease has grown into a worldwide pandemic which is a troubling spot for the whole world. Few technologies are supporting to manage and solve the COVID-19 crisis. In this paper, unified modeling language (UML) will be used to describe requirements and behavior of the proposed system. Unmanned aerial vehicle (UAV) drones are flying mechanical devices without any human pilot that is efficient to reduce the spreading rate of COVID-19. In the proposed IoT-based model, a cluster-based drones’ network will be used to monitor and perform required actions to tackle the violations of standard operating procedures (SOPs). The drones will gather all data through embedded cameras and sensors and will communicate with the control room to operate the actions as required. In this model, a well-maintained and collision-free network of drones will be designed using graph theory. Drones’ network will observe the violation of SOPs in the targeted area and make decisions such as produce alarm sound to alert persons and through communications by sending people warning messages on their smartphones. Further, the persons having COVID symptoms such as high temperature and unbalance respiratory rates will be identified using wearable sensors that are deployed to the targeted area and will send information to the control room to perform required actions. Drones will be able to provide medical kits to the patients’ residences that are identified using wearable sensors to reduce interaction of people. The model will be specified using Vienna Development Method-Specification language (VDM-SL) and validated through the VDM-SL toolbox.
ABSTRACT
Aerial infrared (IR) thermography has been implemented in recent years, proving to be a powerful and versatile technique for performing maintenance at photovoltaic (PV) plants. Its application speed and reliability using unmanned aerial vehicles (UAVs) or drones make it extremely interesting at large PV plants, due to the associated savings in time and costs. Ground-level thermographic inspection is slower and more costly to apply, although it does provide higher optical resolution, due to being conducted closer to the PV modules being inspected. Both techniques used in combination can improve the diagnosis. An IR thermography inspection strategy is proposed for PV plants based on two stages. The first stage of the inspection is aerial, enabling thermal faults to be detected and located quickly and reliably. The second stage of the inspection is done on the ground and applied only to the most relevant incidents revealed in the first stage. This inspection strategy was applied to a 100 kW PV plant, with an improved diagnosis verified via this procedure, as the ground-level inspection detects one-off thermal incidents from objects creating shade and from solar reflections. For PV modules with open circuits or open substrings, the use of one technique or another is immaterial.
ABSTRACT
Sustainability-oriented transport and mobility solutions hunt for engineering and management innovations. Both are primarily adopted by startups—ambitious and dynamic forms of organization stimulated by technical and technological progress. In this paper, the original definition of a startup as the new organizational form has been proposed, and the most relevant trends in green transportation and mobility solutions have been discussed. The paper also seeks knowledge about new business models of sustainable transport and mobility-oriented innovative startups. For this purpose, descriptive statistics of about 200 transport-oriented startups have been produced, based on the results of quantitative research in 2016–2019 in Poland, and the most appealing solutions are presented. Business models of sustainability-oriented transport and mobility startups have been mapped and analyzed. Green innovations in transport concern mainly products and business models. The studied startups can be divided into responding to challenges in designing new equipment, vehicles, and infrastructure (including electromobility) and those offering solutions in managing transport processes, fleet, and passenger transport—especially micro-mobility in cities public transport. Analyzing the profitability of Sustainable Development Goals (SDG)-oriented startups, we concluded that startups related to “hardware” projects (vehicles, infrastructures, drones) need more financial support or strategic partnership than service-oriented projects. We also hypothesized that “hardware” startups contribute more to “greening” transportation and mobility sector processes than startups offering services, which requires further in-depth research.
ABSTRACT
The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space.
ABSTRACT
Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.
ABSTRACT
Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools.