To better control the growing process of horticulture plants greenhouse growers need an automated way to efficiently and effectively find where diseases are spreading.The HiPerGreen project has done research in using an autonomous quadcopter for this scouting. In order for the quadcopter to be able to scout autonomously accurate location data is needed. Several different methods of obtaining location data have been investigated in prior research. In this research a relative sensor based on optical flow is looked into as a method of stabilizing an absolute measurement based on trilateration. For the optical flow sensor a novel block matching algorithm was developed. Simulated testing showed that Kalman Filter based sensor fusion of both measurements worked to reduce the standard deviation of the absolute measurement from 30 cm to less than 1 cm, while drift due to dead-reckoning was reduced to a maximum of 11 cm from over 36 cm.
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The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind
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Much of the discussion about Wikipedia, both in the news and in more scholarly circles, still largely reflects the concerns found in populist perspectives. What’s missing is an informed, radical critique from the inside. The Critical Point of View (CPOV) research initiative, whose material is brought together in this reader, poses different questions than those we have thus far encountered.
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Reason’s typology of safety culture (i.e. Just, Informative, Learning, Flexible and Reporting cultures) is widely used in the industry and academia. Through literature review we developed a framework including 36 markers that reflect the operationalization of Reason’s sub-cultures and general organizational prerequisites. We used the framework to assess to what extent safety culture development guidelines of seven industry sectors (i.e. aviation, railway, oil and gas, nuclear, healthcare, defense and maritime) incorporate academic references, and are similar to each other. Gap analysis and statistics showed that the guidelines include 53–69 % of the safety culture markers, with significant differences across subcultures and industry sectors. The results suggested that there is a gap between the industry guidelines and literature, as well as variant approaches to safety culture across the industry. The framework suggested in the study might be used as reference for completing existing safety culture development plans and constructing safety culture assessment instruments.
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Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, but there is much disquiet over problematic and dangerous implementations of AI, or indeed even AI itself deciding to do dangerous and problematic actions. These developments have led to concerns about whether and how AI systems currently adhere to and will adhere to ethical standards, stimulating a global and multistakeholder conversation on AI ethics and the production of AI governance initiatives. Such developments form the basis for this chapter, where we give an insight into what is happening in Australia, China, the European Union, India and the United States. We commence with some background to the AI ethics and regulation debates, before proceedings to give an overview of what is happening in different countries and regions, namely Australia, China, the European Union (including national level activities in Germany), India and the United States. We provide an analysis of these country profiles, with particular emphasis on the relationship between ethics and law in each location. Overall we find that AI governance and ethics initiatives are most developed in China and the European Union, but the United States has been catching up in the last eighteen months.
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This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public.
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Inaugural lecture as Lector Precision Livestock Farming at HAS University of Applied Sciences on October 14, 2016. PLF, Precision Livestock Farming, uses technologies to continuously monitor animal behaviour, animal health, production and environmental impact.
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De African Digital Rights Network (ADRN) heeft een nieuw rapport gepubliceerd waarin de toevoer en verspreiding van digitale surveillance technologie in Afrika in kaart is gebracht. Onderzoeker Anand Sheombar van het lectoraat Procesinnovatie & Informatiesystemen is betrokken bij het ADRN-collectief en heeft samen met de Engelse journalist Sebastian Klovig Skelton, door middel van desk research de aanvoerlijnen vanuit Westerse en Noordelijke landen geanalyseerd. De bevindingen zijn te lezen in dit Supply-side report hoofdstuk van het rapport. APA-bronvermelding: Klovig Skelton, S., & Sheombar, A. (2023). Mapping the supply of surveillance technologies to Africa Supply-side report. In T. Roberts (Ed.), Mapping the Supply of Surveillance Technologies to Africa: Case Studies from Nigeria, Ghana, Morocco, Malawi, and Zambia (pp. 136-167). Brighton, UK: Institute of Development Studies.
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