• Journal of the Korean Wood Science and Technology
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• v.47 no.1
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• pp.101-109
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• 2019

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• Journal of the Korean Wood Science and Technology
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• v.47 no.3
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• pp.265-276
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• 2019

In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.

• Journal of the Korean Wood Science and Technology
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• v.43 no.4
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• pp.446-455
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• 2015

Based on comparative studies on standards and grading procedures of machine graded lumber in Korea and other countries, this study proposed a procedure of determining the grade classification and design strengths of domestic machine graded lumber. Differences between machine stress rated lumber and E-rated laminations were detailed in order to clarify the need for the procedure improvement. To this improvement the use of average MOE requirement for grading was introduced instead of the fixed minimum MOE requirement which is currently used in the Korean standards. It was found that the fixed minimum MOE requirement method was easier for an inspector to grade but, less efficient as a strength predictor than the average MOE requirement method. The advantage of average MOE requirement method is statistically MOR-MOE regression-based MOR prediction and highly efficient in quality control though it requires a computer-aided operation system in an initial setup. A major weakness of the current Korean grading system was found that different strength characteristics depending on wood species were not reflected on the grade classification and the tabulated allowable design stress. The proposed procedures were developed taking advantages of respective merits of both methods and based on MOR-MOE regression analysis. Through this procedure, the grades of machine stress rated lumber should be revised to become interchangeable with E-rated lamination, which would be beneficial to the cost competitiveness of domestic machine graded lumber and glued laminated timber industry.