Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy-Reference-Cited by-同舟云学术

Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy

Published:2023-08-30 Issue:4 Volume:55 Page:57-69
ISSN:0253-3200
Container-title:Journal of Korea Technical Association of The Pulp and Paper Industry
language:en
Short-container-title:J. of Korea TAPPI

Author:

Hwang Sung-Wook¹,Park Geunyong²,Kim Jinho²,Jeong Myung-Joon³

Affiliation:

1. Human Resources Development Center for Big Data-based Glocal Forest Science 4.0 Professionals, Kyungpook National University

2. Department of Wood Science and Technology, Kyungpook National University

3. Department of Wood Science & Technology, College of Agricultural Life Science, Jeonbuk National University

Publisher

Korea Technical Association of the Pulp and Paper Industry

Subject

Media Technology,General Materials Science,General Chemistry

Link

https://www.ktappi.kr/articles/pdf/doi/10.7584/JKTAPPI.2023.8.55.4.57.pdf

Reference30 articles.

1. Characterization of Korean handmade papers collected in a Hanji reference book

2. Infrared Spectroscopy of Carbon Materials: A Quantum Chemical Study of Model Compounds

3. Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy

4. Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

5. Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches

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