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Keywords = revised U-Net DNN

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13 pages, 3605 KB  
Article
Two Revised Deep Neural Networks and Their Applications in Quantitative Analysis Based on Near-Infrared Spectroscopy
by Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Appl. Sci. 2023, 13(14), 8494; https://doi.org/10.3390/app13148494 - 23 Jul 2023
Cited by 4 | Viewed by 2452
Abstract
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were [...] Read more.
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were to use 1D convolutional layers to replace the fully connected layers. We also designed and combined average pooling and maximum pooling in our revised networks, respectively. We applied these revised network architectures to three publicly available data sets and the resulting calibration models can generate acceptable results for general quantitative analysis. It also generated rather good results for data sets that concern calibration transfer. It demonstrates that constructing network architectures by properly revising existing successful network architectures may provide additional choices in the exploration of the application of deep neural network in analytical chemistry. Full article
(This article belongs to the Special Issue Advances in Chemometrics in Analytical Chemistry)
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