首页|Researchers from National Technical University of Athens Publish Findings in Mac hine Learning (The MLDAR Model: Machine Learning-Based Denoising of Structural R esponse Signals Generated by Ambient Vibration)
Researchers from National Technical University of Athens Publish Findings in Mac hine Learning (The MLDAR Model: Machine Learning-Based Denoising of Structural R esponse Signals Generated by Ambient Vibration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting originating from Athens, Gre ece, by NewsRx correspondents, research stated, "Engineers have consistently pri oritized the maintenance of structural serviceability and safety. Recent strides in design codes, computational tools, and Structural Health Monitoring (SHM) ha ve sought to address these concerns." Funders for this research include Imsfare Project "advanced Information Modellin g For Safer Structures Against Manmade Hazards". Our news journalists obtained a quote from the research from National Technical University of Athens: "On the other hand, the burgeoning application of machine learning (ML) techniques across diverse domains has been noteworthy. This resear ch proposes the combination of ML techniques with SHM to bridge the gap between high-cost and affordable measurement devices. A significant challenge associated with lowcost instruments lies in the heightened noise introduced into recorded data, particularly obscuring structural responses in ambient vibration (AV) mea surements. Consequently, the obscured signal within the noise poses challenges f or engineers in identifying the eigenfrequencies of structures. This article con centrates on eliminating additive noise, particularly electronic noise stemming from sensor circuitry and components, in AV measurements. The proposed MLDAR (Ma chine Learning-based Denoising of Ambient Response) model employs a neural netwo rk architecture, featuring a denoising autoencoder with convolutional and upsamp ling layers. The MLDAR model undergoes training using AV response signals from v arious Single- Degree-of-Freedom (SDOF) oscillators. These SDOFs span the 1-10 Hz frequency band, encompassing low, medium, and high eigenfrequencies, with their accuracy forming an integral part of the model's evaluation. The results are pr omising, as AV measurements in an image format after being submitted to the trai ned model become free of additive noise."
National Technical University of AthensAthensGreeceEuropeCyborgsEmerging TechnologiesMachine Learning
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