• 한국산림과학회지
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• 제98권2호
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• pp.178-182
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• 2009

A new mixed-effects model was developed that predicts individual-tree total height for Pinus densiflora trees in Gangwon province as a function of individual-tree diameter (cm). The mixed-effects model contains two random-effects parameters. Maximum likelihood estimation was used to fit the model to 560 height-diameter observations of individual trees measured throughout Gwangwon province in 2007 as part of the National Forest Inventory Program in Korea. The new model is an improvement over fixed-effects models because it can be calibrated to a local area, such as an inventory plot or individual stand. The new model also appears to be an improvement over the Forest Resources Evaluation and Prediction Program for the ten calibration trees used in this study. An example is provided that describes how to estimate the random-effects parameters using ten calibration trees.

• 한국산림과학회지
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• 제99권2호
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• pp.204-212
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• 2010

This study was performed in 22 unthinned Larix olgensis plantations in northeast China. Data were collected on 95 sample trees of different canopy positions and the diameter at breast height ($d_{1.3}$) ranged from 5.7 cm to 40.2 cm. The individual tree models for the prediction of vertical distribution of live crown, branch and needle biomass were built. Our study showed that the crown, branch and needle biomass distributions were most in the location of 60% crown length. These results were also parallel to previous crown studies. The cumulative relative biomass of live crown, branch and needle were fitted by the sigmoid shape curve and the fitting results were quite well. Meanwhile, we developed the crown ratio and width models. Tree height was the most important predictor for crown ratio model. A negative competition factor, ccf and bas which reflected the effect of suppression on a tree, reduced the crown ratio estimates. The height-diameter ratio was a significant predictor. The higher the height-diameter ratio, the higher crown ratio is. Diameter at breast height is the strongest predictor in crown width model. The models can be used for the planning of harvesting operations, for the selection of feasible harvesting methods, and for the estimation of nutrient removals of different harvesting practices.

• Design & Manufacturing
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• 제15권4호
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Journal of Forest and Environmental Science
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• 제28권2호
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• pp.68-74
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• 2012

A total of 45 Pinus sylvestnis var. mongolica trees from 9 plots in northeast China were destructively sampled to develop aboveground prediction equations for inventory application. Sampling plots covered a range of stand ages (12-47-years-old) and densities (450-3,840/ha). The distribution of aboveground biomass of whole-trees and tree component (stems, branches and leaves) of individual trees were studied and 4 equations were developed as functions of diameter at breast height (DBH), total height (HT). All the equations have good estimation effect with high prediction precision over 90%. Forest biomass was estimated based on the individual biomass prediction equations. It was found forest biomass of all organs increased with the increasing of stand age and density. And the period of 45-50 years was the suitable harvest time for Pinus sylvesris plantation.

• Design & Manufacturing
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• 제16권3호
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• 한국산림과학회지
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• 제90권2호
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• pp.176-183
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• 2001

본(本) 연구(硏究)에서는 목재(木材)의 다목적(多目的) 생산량(生産量)(multiple-product yield) 예측(豫測)에 대한 해결책(解決策)으로서 테다소나무(Pinus taeda L.) 조림지(造林地)를 대상으로 하여 Weibull 직경분포(直徑分布) 수확예측(收穫豫測) 시스템을 개발(開發)하였다. 직경분포(直徑分布) 수확예측(收穫豫測) 모형(模型)을 개발(開發)하기 위하여, 4개의 백분위수(百分位數) 식(式)들을 근거(根據)로 한 모수(母數) 회복(回復)(parameter recovery) 절차법(節次法)을 적용(適用)하였다. 또한 직경급(直徑級)에 대한 수확량(收穫量) 계산(計算)을 위하여 단목(單木) 수고(樹高) 예측식(豫測式)을 개발(開發)하였으며, 그리고 단목(單木) 재적(材積) 예측식(豫測式)을 이용(利用)함으로써 직경급(直徑級)에 대해 기대되는 재적량(材積量)을 계산(計算)할 수가 있다. 본(本) 연구(硏究)에서 사용(使用)된 직경급(直徑級)에 대한 Weibull 누적함수(累積函數)의 상한선(上限線) 차이(差異) 방법(方法)이 기존(旣存)의 상한선(上限線)과 하한선(下限線)의 절차법(節次法)보다도 괄약오차(括約誤差)를 줄 일수 있는 보다 나은 절차법(節次法)이였다. 본(本) 연구(硏究)에서 제시(提示)된 Weibull 직경분포(直徑分布) 수적예측(收積豫測) 시스템에 대한 타당성(妥當性) 검정(檢定)의 한 방법(方法)으로서 Kolmogorov-Smirnov test 결과(結果), 각(各) plot당 예측(豫測)된 직경분포(直徑分布)와 관측(觀測)된 직경(直徑) 분포급(分布級) 사이에서 통계적(統計的) 유의성(有意性)이 없는 것으로 나타났다. 이와 같은 직경분포(直徑分布) 수확예측(收穫豫測) 시스템은 다목적(多目的) 목재(木材) 생산량(生産量) 예측(豫測)과 임분(林分) 구조(構造) 모형(模型) 및 임분(林分)의 경영(經營)에 유용(有用)한 정보(情報)를 제공(提供)할 것이다.

• Design & Manufacturing
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• 제17권4호
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• 한국산림과학회지
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• 제100권1호
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• pp.62-69
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• 2011

본 연구는 5차 국가산림자원조사(National Forest Inventory, NFI) 표본점 자료를 이용하여 충청도 지역에 분포하는 주요 수종에 대한 수고-흉고직경 생장모델을 개발하고자 하였다. 충청도 지역 고정표본지 내에서 수고와 흉고직경이 측정된 주요 수종의 총 임목 본수는 2,681본이었으며, 무작위로 생장모델의 개발을 위해 90% 자료와 모델 타당성 검정을 위해 10% 자료로 나누어서 분석하였고, 본 연구에서 제시된 최종모형의 추정된 계수는 100% 자료를 이용하였다. 8개 주요 수종에 대한 생장모델들의 적합성 검정은 결정계수($R^2$), 추정치의 오차인 평균제곱근오차(RMSE), 평균편의(MD), 절대평균편의(AMD)와 직경급별로 평균편의(MD)를 비교 분석하였다. 본 연구 결과에 의하면, 6개 주요 생장식의 결정계수는 모두 94% 이상의 높은 설명력을 나타냈으며, 특히 C-R 생장모델과 Weibull 생장모델은 다른 모델에 비해 좋은 결과를 나타냈다. 직경급 30 cm 이하에서는 소나무, 리기다소나무, 굴참나무, 신갈나무가 상대적으로 가장 작은 평균편의를 나타낸 반면, 직경급 30 cm 이상에서는 신갈나무, 상수리나무, 졸참나무가 큰 평균편의를 나타냈다. 또한 본 연구의 결과로 제시된 6개 주요 생장식에서 추정한 수고를 임목자원평가 프로그램에 적용하여 간재적을 분석한 결과, 직경급 30 cm까지는 큰 차이를 보이지 않지만, 30 cm 이상인 대경목의 경우 추정된 간재적은 큰 차이를 보이므로, 생장모델 선정에 주의를 기울여야 한다.

• Journal of Forest and Environmental Science
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• 제34권2호
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• pp.119-125
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• 2018

The tropical forests in developing countries are faced with the problem of illegal exploitation of trees. However, dearth of empirical means of expressing the dimensions, structure, quality and quantity of a removed tree has imped conviction of offenders. This study aimed at developing a model that can effectively estimate individual tree basal area (BA) from stump diameter (Ds) for Tectona grandis stands in Omo Forest Reserve, Nigeria, for timber valuation in case of illegal felling. Thirty-six $25m{\times}25m$ temporary sample plots (TSPs) were laid randomly in six age strata; 26, 23, 22, 16, 14, and 12 years specifically. BA, Ds and diameter at breast height were measured in all living T. grandis trees within the 36 TSPs. Least square method was used to convert the counted stumps into harvested stem cross-sectional areas. Six basal area models were fitted and evaluated. The BA-Ds relationship was best described by power model which gave least values of Root mean square error (0.0048), prediction error sum of squares (0.0325) and Akaike information criterion (-15391) with a high adjusted coefficient of determination (0.921). This study revealed that basal area estimation was realistic even when the only information available was stump diameter. The power model was validated using independent data obtained from additional plots and was found to be appropriate for estimating the basal area of Tectona grandis stands in Omo Forest Reserve, Nigeria.

• 한국산림과학회지
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• 제98권3호
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• pp.351-356
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• 2009

본 연구는 전통적인 empirical 생장모델에 환경 인자를 독립변수로 사용하여 이를 환경 인자에 대한 생장 모델의 변화를 분석하여 정도가 높은 생장 모형 구축 가능성 여부를 판단하였다. 이를 위하여 기본적으로 추정된 편백의 수고 및 직경 생장모델에 각 지역의 환경 인자인 평균 기온, 평균 강수량, 고도, 토양의 유기질 함유량을 독립 변수로 추가 하였다. 수고 생장 모델은 Gompertz 다형방정식에 점근 변수인 ${\alpha}$에 온도 고도 인자의 2독립 변수로 도입하여 모델의 정도를 향상 시킬 수 있었다. 직경 생장 모델 또한 Gompertz 다형방정식에 연평균 강수량과 해발고를 도입한 모델이 정도가 높은 것으로 나타났다. 환경 인자를 도입하기 전과 후의 모델의 정도 향상을 비교 했을 때 아주 뚜렷하게 모델의 향상은 나타나지 않았으나 일정 비율의 정도를 향상 시킬 수 있었다. 이는 생장 모델을 구축하기 위한 데이터 조사의 어려움 및 투자할 수 있는 예산을 감안하여 모델의 정도를 향상시키기 위하여 비교적 쉽게 구할 수 있는 환경 인자들의 이용 가능성이 많은 것으로 판단된다.