Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and...

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Bibliographic Details
Main Authors: Syed Danish Ali, Sameen Raut, Joseph Dahlen, Laurence Schimleck, Richard Bergman, Zhou Zhang, Vahid Nasir
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Sensors
Subjects:
convolutional neural network (CNN)
data augmentation
deep learning
ensemble learning
generative adversarial network (GAN)
gradient-boosting machines (GBMs)
Online Access:https://www.mdpi.com/1424-8220/24/6/1992

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https://www.mdpi.com/1424-8220/24/6/1992

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