Ryan Ruvald

Här hålls Hackathon – gymnasieelever löser framtida problem med AI

Närmare 80 gymnasieelever i Karlskrona samlades för att delta i ett stort hackathon – för att utforska AI med fokus på framtidens klassrum. Elever från Ehrenvärdska gymnasiet och af Champansgymnasiet i Karlskrona samlades under onsdagen för att delta i hackathon, ett evenemang som arrangerades av Tech concept lab och Karlskrona...

Virtual Production Studio Lab inaugurated

For the past 2 years the Virtual Production Studio Lab project have been running with the ambition of taking the Model Driven Decision Arena research prototype into next level visualisation and simulation arena with a true dual use perepective to not only be a lab for enigneering research but also be a solution space...

AXESS – Assessment in XR Environments for Sustainable Solutions | 2024-2027

The industry is challenged to comply with increasingly demanding societal and customer needs. Various stakeholders must be engaged throughout the development process. Models and simulations are used to assess new solutions and optimize current ones, foresee decisions’ implications, and make them explainable within the company and with customers. However, diversity...

Launching a new chapter with Sugar

Having run 11 projects in ME310, the PDRL Global Engineering student project will explore innovation opportunities in South America and Sweden this year. A team of four BTH students will collaborate with three students from USP in São Paulo and Volvo Construction Equipment in Sweden and Curitiba, Brazil. 

Depeening the Virtual Production skills at BTH!

Generative AI Visiting and taking part at the Gothenburg Film Festival, BTH participated with an overview of application areas for generative video in movie production at Gothenburg Film Studio’s open film industry meeting, which this year focused on AI. Andrea Nordwall was the key person from BTH at the event.

AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development

Abstract This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based...