Howard Bowman and Colin Johnson of the Computing Laboratory at the University of Kent at Canterbury (UKC) have been awarded a grant of £150,000 from the Engineering and Physical Sciences Research Council to construct computational models of human attention. The research will be undertaken in collaboration with the Medical Research Councils Cognition and Brain Sciences Unit in Cambridge, one of the UKs leading centres for research into human attention.
We live in environments in which many events occur simultaneously and compete for our attention. For example, when standing on a street corner we are subject to a plethora of stimuli: cars passing, conversations amongst pedestrians and street vendors plying their trade. When placed in such environments, humans are very good at prioritising these competing stimuli, directing attention towards the highest priority events and ignoring the rest.
When we perceive a significant event such as a car careering off the road, the current task is interrupted and attention is redirected to responding to the new event by, for example, jumping out of the way of the oncoming danger. In contrast, computer systems do less well: robots struggle to perform effectively in environments in which demands on their processing change unpredictably.
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"Automation in Aerospace Industry @ Fraunhofer IFAM"
The Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM l Stade is presenting its forward-looking R&D portfolio for the first time at...
With an X-ray experiment at the European Synchrotron ESRF in Grenoble (France), Empa researchers were able to demonstrate how well their real-time acoustic monitoring of laser weld seams works. With almost 90 percent reliability, they detected the formation of unwanted pores that impair the quality of weld seams. Thanks to a special evaluation method based on artificial intelligence (AI), the detection process is completed in just 70 milliseconds.
Laser welding is a process suitable for joining metals and thermoplastics. It has become particularly well established in highly automated production, for...
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