Researchers at Penn State University have developed an algorithm that makes it easier for humans to recognize and analyze patterns that appear in both natural and manufactured systems.
The team focused on understanding patterns in nonlinear, dynamic systems, because these are especially difficult as they fluctuate over multiple dimensions and are nearly impossible to understand via human observation.
The researchers created the algorithm by analyzing spatial data in complex microscopic images produced by ultra-precision machining (UPM).
The spatial data showed a variety of surfaces over the UPM images, ranging from flat to rough to severely rugged.
The algorithm allowed the surface roughness to be approximated, resulting in cost savings and resource conservation.
Said Penn State researcher Hui Yang, "You can use this algorithm on complex-structured data that is measurable or observable and is represented in two-dimensional, three-dimensional, or high-dimensional images."
From Penn State News
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