This report relates to the Horizon 2020 project entitled ‘Making City’. The report was conducted by the Hanze University of Applied Sciences to the benefit of theMunicipality of Groningen and other consortium partners in the Making City project and addresses the legal impediments that may arise when creating and achieving a Positive Energy District (PED). In doing so, it specifically addresses the situation in the city of Groningen and the legal framework of the Netherlands.This report highlights legal developments of (upcoming) EU and mostly Dutch legislation related to a PED, such as the Collective Heat and Supply Act (Warmtewet) and the Environmental Act. Moreover, smart contracts used in the Block chain technology is discussed and a chapter on Intellectual Property legislation is included which becomes relevant when using new innovations and technologies. Furthermore, it identifies certain legal barriers that emerged in the establishment of the Groningen PED.
This report relates to the Horizon 2020 project entitled ‘Making City’. The report was conducted by the Hanze University of Applied Sciences to the benefit of theMunicipality of Groningen and other consortium partners in the Making City project and addresses the legal impediments that may arise when creating and achieving a Positive Energy District (PED). In doing so, it specifically addresses the situation in the city of Groningen and the legal framework of the Netherlands.This report highlights legal developments of (upcoming) EU and mostly Dutch legislation related to a PED, such as the Collective Heat and Supply Act (Warmtewet) and the Environmental Act. Moreover, smart contracts used in the Block chain technology is discussed and a chapter on Intellectual Property legislation is included which becomes relevant when using new innovations and technologies. Furthermore, it identifies certain legal barriers that emerged in the establishment of the Groningen PED.
Using technology to improve the adolescents' journey to school by bike in province of Drenthe and Groningen.All unsafe area that children spotted on the map, are because the lack of traffic safety ( lack of visibility, high speed, etc). In general children do not have a positive perception of cycling to school, and their favourite mode of traveling to school is car. What technology based intervention can make adolescents’ cycling to and from school safer and more attractive for them? Also does it help to encourage those who live far from the school (>10 km) to cycle to and from school more often?
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.