• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.148-155
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• 2009

The objective of this study is to know market structures of softwood logs being imported to South Korea from log producing countries. Import demand of softwood logs imported to South Korea from America, New Zealand and Chile is fixed as a function of log prices, the lagged dependent variable and output. On the basis of the adaptive expectations model, linear regression models that the explanatory variables included and the lagged dependent variable were estimated by Seemingly Unrelated Regression Equations (SURE). The short-run and long-run own price elasticity of America's softwood log import demand is -1.738 and -4.250 respectively. Then long-run elasticity is much higher than short-run elasticity. Short-run and long-run crosselasticity of New Zealand's softwood log import demand with respect to American's softwood log import price are inelastic at 0.505 and 0.883 respectively. Short-run and long-run cross-elasticity of Chile's softwood log import demands with respect to American's softwood log import prices were highly elastic at 2.442 and 4.462 respectively. Long-run elasticity was almost twice as high as short-run elasticity.

• Journal of Korea Foresty Energy
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• v.25 no.2
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• pp.49-54
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• 2006

This study was carried out to provide basic data for using domestic structural softwood lumber efficiently and ensuring structural safety of timber structures. The ratios (k-factor) between static and dynamic MOE measured by ultrasonic device for $2{\times}6$ domestic softwood structural lumber are 1.0602 for Korean red pine, 1.0013 for Korean white pine and 1.2320 for Japanese larch. In machine grade using nondestructive method, 76% of Korean red pine was classified into higher than E9 grade, 85% of Korean white pine was sorted into higher than E7 grade and 68% of Japanese larch was classified into higher than E11 grade. Correlation between MOE and MOR by static bending with k-factor from nondestructive method provide a possibility to predict bending strength and allowable stress of domestic softwood structural lumber.

• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.45-52
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• 2008

The objectives of this study are to explain a supply plan of domestic softwood log by long-term prospect of MDF production to stably promote industry of MDF. For it, we developed the long supply function as Ordinary Least Squares Method. Between 2005 and 2050, it was estimated that quantity of domestic production of MDF increased from 1,653 thousand $m^3$ to 2,041 thousand $m^3$. In 2050, quantities of domestic softwood log used by raw materials to product MDF of 2,041 thousand $m^3$ were estimated to be used about 1,355 thousand $m^3$. Exampling Pinus rigida used presently by raw materials to product MDF, cutting area of it is estimated to be 10,828 ha per year. And larch is cutted about 9,160 ha per year. This study estimated annual softwood log cutting amount and total afforestation area at 2050 year by 3 scenarios which are 35%, 45% and 55% about use of domestic softwood log for MDF production. If we do a criterion of cutting area, we advantage to plant larch. But the species of trees are use and growth property. We think that the afforestation policy must be performed on the base of those to supply raw materials of MDF. Although government plans hardwood afforestation policy after cutting Pinus rigida, it needs to support and manage certainly afforestation area of softwoods to need to supply raw materials of MDF to stably promote industry of MDF.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.203-208
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• 2006

This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

• Journal of the Korean Wood Science and Technology
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• v.30 no.3
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• pp.109-115
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• 2002

Mycelial growth of L. edodes by pretreatments of softwood was studied on a sawdust medium. The sawdust used was from the following softwood species : Larix leptolepis, Pinus densiflora and Pinus koraiensis. The pretreatment consisted of cold-water (48 h), hot-water (3 h) and steam extractions (3 h) at a ratio of 500 g : 3,000 mL (sawdust : distilled water). The sawdust medium was a mixture of 76% sawdust, 20% rice bran, 3% glucose, 0.4% potassium nitrate and 0.6% calcium carbonate. Following sawdust pretreatments proved most suitable : L. leptolepis (steam extraction), P. densiflora (hot-water extraction) and P. koraiensis (hot-water extraction). Mycelial growth on P. koraiensis sawdust increased in proportion to an increase in hot-water extraction time. Mycelial growth was optimum on the sawdust extracted for 12 hours, hot-water extraction beyond this period proved unsuitable. With the exception of P. densiflora at 100 ㎍/mL, antifungal activity occurred in every sample. Maximum inhibition of mycelial growth was obtained from following concentration of hot-water extractives : P. densiflora (10⁴ ㎍/mL) and P. koraiensis (10⁴ ㎍/mL). This study has provided useful preliminary information for the cultivation of L. edodes.

• Journal of the Korean Wood Science and Technology
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• v.45 no.2
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• pp.202-212
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• 2017

This study was to establish the interrelation between chemical compositions and near infrared (NIR) spectra for the classification on distinguishability of domestic gymnosperms. Traditional wet chemistry methods and infrared spectral analyses were performed. In chemical compositions of five softwood species including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cypress (Chamaecyparis obtusa), and cedar (Cryptomeria japonica), their extractives and lignin contents provided the major information for distinction between the wood species. However, depending on the production region and purchasing time of woods, chemical compositions were different even though in same species. Especially, red pine harvested from Naju showed the highest extractive content about 16.3%, whereas that from Donghae showed about 5.0%. These results were expected due to different environmental conditions such as sunshine amount, nutrients and moisture contents, and these phenomena were also observed in other species. As a result of the principal component analysis (PCA) using NIR between five species (total 19 samples), the samples were divided into three groups in the score plot based on principal component (PC) 1 and principal component (PC) 2; group 1) red pine and Korean pine, group 2) larch, and group 3) cypress and cedar. Based on the chemical composition results, it was concluded that extractive content was highly relevant to wood classification by NIR analysis.

• Journal of Korean Society of Forest Science
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• v.96 no.2
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• pp.222-226
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• 2007

This study developed the Korean roundwood import prediction model using vector autoregressive (VAR) method. The roundwood was divided into softwood and hardwood by species. The VAR model of roundwood import was specified with two lagged endogenous variables, that is, roundwood import volume and roundwood import price. The results showed that the significance levels of F-statistics in the softwood and hardwood roundwood import equations rejected the hypothesis that all coefficients are zero. So, we concluded that roundwood import volume can be explained by lagged import volume and lagged import price in Korea. The coefficient signs of all variables were as expected. Also, the model has good explanatory power, and there is no autocorrelation.

• 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 Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.106-112
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• 2011

The effects of alkaline treatment on the WRV, crystalline structure and sheet structure of softwood and hardwood bleached kraft pulp were investigated. Sodium hydroxide and sodium carbonate were used as chemicals for alkaline treatment and two levels of alkali dosage (5%, 10%) were applied respectively. Alkali treated and untreated pulp were refined to three levels (550, 450 and 350 mL CSF). WRV of the alkali treated pulps depended on the alkaline type and concentration. It was found that the crystalline structures of softwood and hardwood pulp were not changed by refining. Sodium carbonate and lower concentration of sodium hydroxide treatment did not caused any modification of cellulose crystalline structure, while higher concentration of sodium hydroxide treatment caused the partial modification of cellulose crystalline structure. Alkaline treatment of hardwood bleached kraft pulp led to the shrinkage of fiber diameter and bulky structure of sheet. Alkaline treatment of softwood bleached kraft pulp did not cause the significant change in fiber shrinkage and bulk of sheet.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.