Credit: RMIT University
Researchers have published a study revealing their successful approach to designing much quieter propellers.
The Australian research team used machine learning to design their propellers, then 3D printed several of the most promising prototypes for experimental acoustic testing at the Commonwealth Scientific and Industrial Research Organisation s specialised echo-free chamber.
Results now published in
Aerospace Research Central show the prototypes made around 15dB less noise than commercially available propellers, validating the team s design methodology.
RMIT University aerospace engineer and lead researcher Dr Abdulghani Mohamed said the impressive results were enabled by two key innovations - the numerical algorithms developed to design the propellers and their consideration of how noise is perceived in the human ear - as part of the testing.
Credit: University of Copenhagen
One of the most classic algorithmic problems deals with calculating the shortest path between two points. A more complicated variant of the problem is when the route traverses a changing network whether this be a road network or the internet. For 40 years, an algorithm has been sought to provide an optimal solution to this problem. Now, computer scientist Christian Wulff-Nilsen of the University of Copenhagen and two research colleagues have come up with a recipe.
When heading somewhere new, most of us leave it to computer algorithms to help us find the best route, whether by using a car s GPS, or public transport and map apps on their phone. Still, there are times when a proposed route doesn t quite align with reality. This is because road networks, public transportation networks and other networks aren t static. The best route can suddenly be the slowest, e.g. because a queue has formed due to roadworks or an accident.
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IMAGE: Computational models of air quality have long been used to shed light on pollution control efforts in the United States and Europe, but the tools have not found widespread adoption. view more
Credit: James East
Computational models of air quality have long been used to shed light on pollution control efforts in the United States and Europe, but the tools have not found widespread adoption in Latin America. New work from North Carolina State University and Universidad de La Salle demonstrates how these models can be adapted to offer practical insights into air quality challenges in the Americas outside the U.S.
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In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward.
Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action steer right, steer left, or continue straight to avoid hitting a pedestrian that its cameras see in the road.
But what if there s a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called adversarial inputs, it might take unnecessary and potentially dangerous action.
In a new paper published in
Light: Science & Applications, the group led by Professor Andrea Fratalocchi from Primalight Laboratory of the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, introduced a new patented, scalable flat-optics technology manufactured with inexpensive semiconductors.
The KAUST-designed technology leverages on a previously unrecognized aspect of optical nanoresonators, which are demonstrated to possess a physical layer that is completely equivalent to a feed-forward deep neural network. What we have achieved, explains Fratalocchi, is a technological process to cover flat surfaces, which in optical jargon are called flat optics, with physical neural units that are able to process light as a neural network does with an electrical signal.