NIK VR and modelling

Author and abstract
Daniel Patel, Runar Tistel, Harald Soleim and Atle Geitung:
Translating 2D art into Virtual Reality and comparing the user experiences
Recent advancements in virtual reality on the hardware and software front have made high-quality virtual reality experiences both cheaper, and easier to obtain. This paper explores how virtual reality changes the way a user experiences art and if virtual reality is suited as a medium for expressing art. Based on two existing artworks, we have created VR versions using a game engine, and conducted a user study to get a comparison of how the experience of the traditional artworks differ from the VR versions. The artworks have been created in 3D using algorithmic modelling techniques.
Kjetil Raaen and Hanne Sørum:
Survey of interactions in popular VR experiences
As the first step in a project to examine the quality of interactions in the relatively young field ofVirtualReality (VR), this study showcases the creative variation among modes of interaction. The present paper reports on a multiple case study reviewing VR applications focusing primarily on interactions. By surveying a set of popular applications, we explore the variety as well as the developing conventions within user interactions in VR. Because this research is work-in-progress we provide some preliminary insight that we can build on and discuss in upcoming studies. Our results show a wide array of different ways of interacting with such applications. Generally, these can be categorised in one of a few groups; menus, locomotion and interaction with the virtual environment. We also argue that theory from the field of Human Computer Interaction (HCI) can be applied to VR in regards to design of user interfaces.
K. Darshana Abeyrathnaa and Chawalit Jeenanuntab:
Escape Local Minima with Improved Particle Swarm Optimization Algorithm
Particle Swarm Optimization (PSO) is a powerful meta-heuristic technique which has been maneuvered to solve numerous complex optimization problems. However, due to its characteristics, there is a possibility to trap all particles in a local minimum in the solution space and then they cannot find the way out from the trap on their own. Therefore, we modify the traditional PSO algorithm by adding an extra step so that it helps PSO to find a better solution than the local minimum that they undesirably found. We perturb all the particles by adjusting parameter values in the traditional algorithm when there is no improvement of the objective value over the training iterations, assuming that particles have stuck in a local minimum. In this research, we mainly focus on adjusting the learning factors. However, the parameter values have to be used in an effective way to perturb the particles. The behavior of the proposed modification and its parameter adjustments are studied using a function which has a large number of local minima - Schwefel’s function. Results show that 2 out of 3 PSO attempts trap in local minimum and slight changes on learning factors do not help them to get out from the traps. However, perturbances made with large learning factors can find better solutions than the local minima that they stuck in and help to find the global minimum eventually.
Sindre Stokkenes, Lars Kristensen and Torgrim Log:
Cloud-based Implementation and Validation of a Predictive Fire Risk Indication Model
The high representation of wooden houses in Norwegian cities combined with periods of dry and cold climate during the winter time often results in a high risk of severe fires. This makes it important for public authorities and fire departments to have an accurate estimate of the current fire risk in order to take proper precautions. We report on the implementation of a predictive mathematical model based on first order principles which exploits cloud-provided measurements from weather stations and weather forecasts from the Norwegian Meteorological Institute to predict the current and future fire risk at a given geographical location. We have experimentally validated the model during the winter 2018-2019 at selected geographical locations, and by considering weather data from the time of several historical fires. Our results show that our cloud and web-based implementation is both time and storage efficient, and capable of being able to accurately predict the fire risk measured in terms of the estimated time to ashover. The paper demonstrates that our methodology in the near future may become a valuable risk predicting tool for Norwegian fire brigades.