Abstract
Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.

Abstract

Abstract
This paper presents an innovative strategy to assess the photovoltaic (PV)-based distributed generation (DG) hosting capacity considering the operation under normal and emergency conditions of electrical distribution systems (EDSs). In the emergency condition, the proposed strategy aims to improve the recoverability of EDSs against a set of high-impact fault scenarios. This recoverability process is achieved by the optimal coordination of topology reconfiguration, islanding operation of dispatchable DG units, and pre-positioning and displacement of mobile DG (MDG) units. This problem is formulated as a two-stage stochastic formulation, where the first one defines the DG hosting capacity and the amount of MDG units to be positioned in staging locations. Meanwhile, the second stage simulates high-impact fault events and, by applying resilience alternatives, the EDS recoverability can be improved. Inherently, the two-stage stochastic formulation is represented by a mixed-integer linear programming (MILP) model. The objective function of this MILP model maximizes the installed PVbased DG capacity while the amount of energy load shedding after fault events is minimized. To validate and show the scalability of the proposed strategy, two EDS are studied under different high-impact fault events and considering the application of multiple resilience alternatives. Results show that by estimating the capacity of PV-based DG simultaneously with the restoration process, the number of pre-positioned and dispatched MDG units can be reduced. On the other hand, when this PV capacity is determined disregarding fault scenarios, this solution could lead to unviable conditions and, thus, generation curtailment of up to 80% could be required.

Abstract

Abstract

Abstract
Partnerships for Science Education (PAFSE) is a case study of project-based learning applied to real-world problems connected with public health and sustainable development. The EU-funded project organizes science education activities at low secondary level engaging students and school stakeholders (universities, research centres, start-ups, enterprises, governmental organisations, NGOs) in health promotion and disease prevention actions that benefit the health and well-being of the community. Projects leaded by schools are addressed within educational scenarios co-created with partners interested in STEM education and developed under a relevant public health issue through their continuous engagement in open schooling approach.