Zoekresultaten

Software voor slimme systemen

Dit essay geeft een systeemvisie op het ontwikkelen van embedded software voor slimme systemen: (mobiele) robots en sensornetwerken.

Mitigating Children’s Pain and Anxiety during Blood Draw Using Social Robots

Young pediatric patients who undergo venipuncture or capillary blood sampling often experience high levels of pain and anxiety. This often results in distressed young patients and their parents, increased treatment times, and a higher workload for healthcare professionals. Social robots are a new and promising tool to mitigate children’s pain and anxiety. This study aims to purposefully design and test a social robot for mitigating stress and anxiety during blood draw of children. We first programmed a social robot based on the requirements expressed by experienced healthcare professionals during focus group sessions. Next, we designed a randomized controlled experiment in which the social robot was applied as a distraction method to measure its capacity to mitigate pain and anxiety in children during blood draw in a children’s hospital setting. Children who interacted with the robot showed significantly lower levels of anxiety before actual blood collection, compared to children who received regular medical treatment. Children in the middle classes of primary school (aged 6–9) seemed especially sensitive to the robot’s ability to mitigate pain and anxiety before blood draw. Children’s parents overall expressed strong positive attitudes toward the use and effectiveness of the social robot for mitigating pain and anxiety. The results of this study demonstrate that social robots can be considered a new and effective tool for lowering children’s anxiety prior to the distressing medical procedure of blood collection.

The digital skills divide: evidence from the European tourism industry

Purpose: This paper aims to present the findings from a European study on the digital skills gaps in tourism and hospitality companies. Design/methodology/approach: Mixed methods research was adopted. The sample includes 1,668 respondents (1,404 survey respondents and 264 interviewees) in 5 tourism sectors (accommodation establishments, tour operators and travel agents, food and beverage, visitor attractions and destination management organisations) in 8 European countries (UK, Italy, Ireland, Spain, Hungary, Germany, the Netherlands and Bulgaria). Findings: The most important future digital skills include online marketing and communication skills, social media skills, MS Office skills, operating systems use skills and skills to monitor online reviews. The largest gaps between the current and the future skill levels were identified for artificial intelligence and robotics skills and augmented reality and virtual reality skills, but these skills, together with computer programming skills, were considered also as the least important digital skills. Three clusters were identified on the basis of their reported gaps between the current level and the future needs of digital skills. The country of registration, sector and size shape respondents’ answers regarding the current and future skills levels and the skills gap between them. Originality/value: The paper discusses the digital skills gap of tourism and hospitality employees and identifies the most important digital skills they would need in the future.

Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.

QUO VADIS: Exploring the future of shipping in the Baltic Sea.

A Digital Twin of the Trondheim Fjord for Environmental Monitoring : A Pilot Case

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.