• Journal of the Korean Wood Science and Technology
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• 제47권1호
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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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• 제47권4호
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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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• 제47권3호
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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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• 제43권4호
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• pp.446-455
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• 2015

국내외 기계등급제재목(구조재 및 층재)의 등급기준 및 설계강도 산출방법을 비교 분석하고 국내 제재산업 실정을 고려한 평균 탄성계수(modulus of elasticity, 이하 MOE) 기준방법 적용을 제안하였다. 먼저 올바른 기계등급제재목 기준 정착을 위해 기계등급구조재와 기계등급층재의 공통점과 차이점을 설명하였다. 최소 고정 MOE 기준 등급을 사용하는 국내 기준은 등급구분에는 편리하나 휨강도(modulus of rupture, 이하 MOR) 예측과 자원이용도 측면에서는 효율성이 낮은 것으로 파악되었다. 해외에서 사용되는 평균 MOE 기준 방법은 초기 컴퓨터 기반 작동을 요구하나 MOR-MOE 직선회귀에 근거한 합리적인 MOR 예측과 품질관리 측면에서 효율성이 높은 것으로 분석되었다. 무엇보다도 현 국내 기계등급구조재 등급체계는 수종별 강도 특성을 반영하지 못하고 있다는 것이 가장 큰 문제점으로 분석되었으며 이러한 결과를 기반으로 MOR-MOE 직선회귀분석에 근거한 기계등급제재목 등급기준 및 기준설계값 산출방법 적용을 제안하였다. 이를 통하여 궁극적으로 부가가치가 높은 국산 기계등급구조재 생산 활성화를 이루고, 기계 등급구조재의 층재 전용 가능에 따른 구조용 집성재 가격경쟁력 제고 효과를 얻을 수 있다고 사료되었다.

• 한국안전학회지
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• 제5권2호
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• pp.66-75
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• 1990

This study is conducted to establish the structural standard of tube and coupler scaffold which is suitable for our present stuation through the comparison analysis for domestic and foreign standards as well as measurement of field survey. The results of this study are as follows ： 1) The load is classified by three categories, light-duty(equal and lower than 150kg/m$^2$), medium-duty(150-250 kg/m$^2$), heavy-duty(250-350kg/m$^2$), and the equivalent horizontal length of side posts is each, 1.5-1.8m, 1.2-1.5m, equal and lower than 1.2m, and the equivalent horizontal length between front and rear posts is each 1.2-1.5m, 0.9-1.2m, equal and lower thatn 0.9m, in accordance with the load classification. 2) The height between upper and lower runner is equal and lower than 1.5m, and the brace across the width of scaffold should be installed within 15m in horizontal direction at 45 degree angle. 3) The entire scaffold should be securely tied to the wall of permanent structure with uslng anchor and bolt at intervals not to exceed 6m in case of non-connection and 4.5m in case of connection in both horizontal and vertical direction. 4) The post should be installed on the sound foundation tied to lumber footing with using base plate, and standard platform plank should be produced in the factory and widely used in the construction field.

• 한국환경복원기술학회지
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• 제10권4호
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• pp.41-53
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• 2007

The Baekdudaegan Mountain Range is a backbone of the Korean Peninsula which carries special spiritual and sentimental signatures for Koreans as well as significant ecological values for diverse organisms. However, in spite of importance of this region, the forests of Baekdudaegan have been damaged in a variety of human activities by being used as highland vegetable grower, lumber region, grass land, and bare land, and are still undergoing destruction. The existing researches had determined the details of the damage through on-site and recent observations. Such methods cannot provide quantitative and integrated analysis therefore could not be utilized as objective data for the ecological conservation of Baekdudaegan forests. The goal of this study is to quantitatively analyze the forest damage in the Baekdudaegan preservation region through land cover categorization and change detection techniques by using satellite images, which are 1980s, and 1990s Landsat TM, and 2000s Landsat ETM+. The analysis was executed by detecting land cover changed areas from forest to others and analyzing changed areas' spatial patterns. Through the change detection analysis based on land cover classification, we found out that the deforested areas were approximately three times larger after the 1990s than from the 1980s to the 1990s. These areas were related to various topographical and spatial elements, altitude, slope, the distance form road, and water system, etc. This study has the significance as quantitative and integrated analysis about the Baekdudaegan preservation region since 1980s. These results could actually be utilized as basic data for forest conservation policies and the management of the Baekdudaegan preservation region.

• 한국건설관리학회논문집
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• 제24권6호
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• pp.66-73
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• 2023

건설 프로젝트는 기획부터 완공까지 공사비 예측, 확인, 그리고 정산 단계로 이루어진다. 건설원자재 평균 가격은 변동성을 지닌다. 하지만 건설 프로젝트의 자재비 산정은 계획단계 시점의 시세를 반영하여 결정되기 때문에, 시공단계에서 자재가 투입될 시점의 시세 변동에 따라 예상한 가격과 차이가 날 수 있다. 건설 산업은 건설원자재 가격 변동으로 인한 수요예측 실패, 프로젝트 비용변경으로 인한 사용자 비용 증가, 예측 체계성 부족으로 인한 손실이 발생한다. 이에 따라 건설원자재 가격 예측의 정확도 개선이 필요하다. 본 연구는 Data Refactor 기법의 개선을 통해 건설원자재 가격 예측 및 적용성 검증을 목적으로 한다. 건설원자재의 가격 예측의 정확도를 높이기 위하여 기존의 데이터 리팩토 간의 저·고빈도의 분류 및 ARIMAX 활용법을 빈도 위주 및 ARIMA 기법 활용으로 개선하여 건설원자재 목재, 시멘트 등 6개 품목의 단기(미래 3개월), 중기(미래 6개월), 장기(미래 12개월) 가격을 예측하였다. 분석한 결과 개선된 Data Refactor 기법을 기반으로 한 예측값이 오차는 줄었고 변동성은 확장되었다. 따라서, 본 연구에서 제안된 Data Refactor 기법을 통해 건설원자재 가격을 더 정확하게 예측하여 예산을 효과적으로 관리할 수 있을 것으로 기대된다.