Application of Near-Infrared Spectroscopy for Evaluation of Drying Stress on Lumber Surface: A Comparison of Artificial Neural Networks and Partial Least Squares Regression

Watanabe, Ken; Kobayashi, Isao; Matsushita, Yasuhiro; Saito, Shuetsu; Kuroda, Naohiro; Noshiro, Shuichi

doi:10.1080/07373937.2013.846911

Cited by 23 publications

(16 citation statements)

References 21 publications

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“…Artificial neural networks are increasingly being used for modeling in the field of wood science. For instance, in the field of wood drying, Avramidis (2006) [13] predicted the drying rate of wood based on neural network construction model; Zhang Dongyan (2008) [14] constructed a neural network model for predicting wood MC during conventional drying;İlhan Ceylan (2008) [15] used neural network models to study wood drying characteristics; Watanabe (2013Watanabe ( , 2014 [16,17] employed artificial neural network model to predict the final moisture content of Sugi (Cryptomeria japonica) during drying and evaluate the drying stress on the wood surface. Ozsahin (2017) [18] utilized artificial neural networks to successfully predict the equilibrium moisture content and specific gravity of heat-treated wood.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

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The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

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“…Tiryaki and Aydin (2014) predicted the compression strength parallel to grain of heat-treated woods indicated that ANN modeling provided high predictive precision, and the R 2 for the testing set was obtained as 0,997%. Watanabe et al (2014) investigated the drying stress at the surface of lumber during drying using near-infrared spectroscopy combined with ANN models.…”

Section: Introductionmentioning

confidence: 99%

Effects of saturated vapor pre-steaming on drying strain in Asian White Birch: Experimentation and modelling

Avramidis

Zhao

et al. 2019

Maderas, Cienc. tecnol.

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The effect of low pressure saturated vapor pre-steaming on restrained shrinkage strain, mechanosorptive creep and the distribution of moisture content was investigated during conventional drying of wood discs. Mechano-sorptive creep was furthermore modelled by artificial neural network theory with five inputs, i.e., pre-steaming and drying temperatures, wood moisture content, relative humidity and distance from the pith. Results revealed that, pre-steaming partly reduced the variation of moisture content distribution along radial direction, increased restrained shrinkage strain in heartwood and decreased in sapwood and slightly decreased the mechano-sorptive creep. The neural network model provided reasonable prediction results, namely, the coefficient of determination for training, validation and test sets greater than 0,95.

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“…However, artificial neural network (ANN) is powerful tool to deal with the nonlinear and multiple processing systems (Ding et al, 2017). As artificial neural networks possess excellent ability of high learning and identifying, it can carry out complicated non-linear relationships between the input and output of a system with an appropriate choice of free parameters or weight easily (Watanabe et al, 2014). In the last decade, the ANN has already been successfully applied to chemistry (Cristea et al, 2003;Sun et al, 2011), food processing (Ding et al, 2016), microbiology (Ferrari et al, 2017), medicine (Amato et al, 2013), psychology (Levine, 2007) as well as various other fields.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network–Genetic Algorithm to Optimize Yin Rice Inoculation Fermentation Conditions for Improving Physico-chemical Characteristics

Ding

Zhou

et al. 2018

FSTR

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In this research, a nonlinear model describing the relationship between the inoculation fermentation parameters and the quality of yin rice were investigated based on artificial neural network and genetic algorithm (ANN-GA) model. The ANN-GA model had excellent potential for predicting the viscosity property of yin rice, and fermentation parameters were optimized by using genetic algorithm. Through ANN-GA model, the optimized inoculation fermentation parameters were: 0.05 % lactic acid bacteria, 0.05 % Saccharomyces cerevisiae, 0.2 % Rhizopus oryzae, then fermenting for 48 h at 25 ℃. The results were further validated by experiments. Moreover, it revealed that inoculation fermentation not only effectively improved physico-chemical characteristics of yin rice, but also shorten period of fermentation about 14 days compared to the natural fermentation. These results indicated that the accuracy and reliable of fermentation parameters optimized by ANN-GA model.

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Application of Near-Infrared Spectroscopy for Evaluation of Drying Stress on Lumber Surface: A Comparison of Artificial Neural Networks and Partial Least Squares Regression

Cited by 23 publications

References 21 publications

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Effects of saturated vapor pre-steaming on drying strain in Asian White Birch: Experimentation and modelling

Artificial Neural Network–Genetic Algorithm to Optimize Yin Rice Inoculation Fermentation Conditions for Improving Physico-chemical Characteristics

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