• 한국방재학회 논문집
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• 제11권3호
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• pp.127-132
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• 2011

본 연구에서는 강원도 인제군 산간지역에 대해 토사량 예측모형을 이용하여 산정한 토사량과 실제 준설량을 비교하였다. MSDPM과 LADMP를 한국지형에 맞게 보정하고 수정하여 사용하였다. 두 식 모두 토사 유발 강우강도와 토사 유발 강우량 개념을 도입하였으며 보정계수를 사용하여 식을 보정하였다. 계산 결과와 준설량을 비교한 결과, MSDPM의 계산결과가 LADMP보다 더 잘 일치하는 것으로 나타났다. MSDPM의 계산결과는 준설량과 약 27.6% 절대치 차이가 났으며 LADMP의 계산결과는 준설량과 약 50.6%의 절대치 차이를 나타냈다. 본 연구에서 보정된 두 개의 토사량 예측모형은 우리나라 산간지역의 토사량 예측을 위해서 사용 가능할 것으로 판단된다.

• 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.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1998년도 춘계학술대회논문집
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• pp.58-61
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• 1998

Anisotropy is closely related to the formability of sheet metal and should be considered carefully for more realistic analysis of actual sheet metal forming operations. In order to better describe anisotropic plastic properties of aluminum alloy sheets, a planar anisotropic yield function which accounts for the anisotropy of uniaxial yield stresses and strain rate ratios simultaneously was proposed recently[1]. This yield function was used in the finite element simulations of cup drawing tests for an aluminum alloy 2008-T4. Isotropic hardening with a fixed initial back stress based on experimental tensile and compressive test results was assumed in the simulation. The computation results were in very good agreement with the experimental results. It was shown that the initial back stress as well as the yield surface shape have a large influence on the prediction of the cup height profile.

• Journal of Biosystems Engineering
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• 제30권6호통권113호
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• pp.360-365
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• 2005

Precision agriculture attempts to improve cropping efficiency by variable application of crop treatments such as fertilizers and pesticides, within field on a point-by-point basis. Therefore, a more complete understanding of the relationships between yield and soil properties is of critical importance in precision agriculture. In this study, the functional relationships between measured soil properties and rice yield were investigated. A supervised back-propagation neural network model was employed to relate soil chemical properties and rice yields on a point-by point basis, within individual site-years. As a results, a positive correlation was found between practical yields and predicted yields in 1999, 2000, 2001, and 2002 are 0.916, 0.879, 0.800 and 0.789, respectively. The results showed that significant overfitting for yields with only the soil chemical properties occurred so that more of environmental factors, such as climatological data, variety, cultivation method etc., would be required to predict the yield more accurately.

• Journal of Biosystems Engineering
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• 제23권3호
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• pp.253-258
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• 1998

The part I was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Infrared(NIR) Reflectance analyzer. The purpose of this study(part II) is to measure fundamental data required for the prediction of rice quality, and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undried paddy powder by using Near Infrared(NIR) Reflectance analyzer. The results of this study were summarized as follows : The predicted values of protein contents obtained from the undried paddy powder were well correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to the lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1998년도 하계 학술대회 논문집
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• 한국정밀공학회지
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• 제16권11호
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• pp.98-105
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• 1999

As the IC(Integrated Circuit) has been densified and complicated, it is required to thorough process control to improve yield. Experts, for this purpose, focused on the process analysis automation, which is came from the strict data management in semiconductor manufacturing. In this paper, we presents the process analysis system that can analyze causes, for a output after processes. Also, the plasma etching process that highly affects yield among semiconductor process is modeled to predict a output before the process. To approach this problem, we use adaptively trained neural networks that exhibit superior accuracy over statistical techniques. And in comparison with methods in other paper, a method that history of trend for input data is considered is shown to offer advantage in both learning and prediction capability. This research regards CD(Critical Dimension) that is considerable in high integrated circuit as output variable of the prediction model.

• 산업공학
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• 제22권3호
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

• 한국초지조사료학회지
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• 제41권1호
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• pp.47-55
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• 2021

본 연구는 기후요인과 토양요인이 알팔파 건물수량에 어느 정도 영향을 미치는지를 기여도로 평가할 목적으로, 기상변수와 토양물리성변수를 고려하여 일반선형모형으로 수량예측모델을 구축하였다. 알팔파 수량예측모델 구축과정은 알팔파, 기상 및 토양자료수집, 가공, 통계분석 및 모델구축 순이었다. 수량예측모델은 알팔파와 양적자료인 기상변수를 선택하기 위한 다중회귀분석과 질적자료인 토양물리성변수도 고려하기 위해서 일반선형모형을 사용하였다. 그 결과 DMY에 영향을 미치는 기상변수는 적산온도와 생육일수이었으며, 토양물리성변수는 점토함량이 선택되었다. DMY에 영향을 미치는 변수별 기여도는 점토함량(63%), 적산온도(21%) 및 생육일수(11%)순 이었으며 요인별 기여도는 기후요인(적산온도, 21%와 생육일수, 11%)이 32%, 토양요인(점토함량)이 63%로 나타나 토양요인이 기후요인보다 알팔파 건물수량에 더 기여하는 것으로 평가하였다. 본 연구에서 이용한 알팔파 자료는 토성, 시비수준 및 품종이 제한되어 있어 앞으로 이들 요인을 고려한 다양한 조건의 재배실험을 통하여 보다 많은 자료축적이 요구된다.

• Computers and Concrete
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• 제29권 6호
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.