• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.189-193
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• 2006

대체용수원의 개발이 시급하게 대두되어지고 있는 가운데 제한된 수자원을 보다 효과적으로 사용하기 위한 하나의 방법으로 지하댐(Groundwater Dam) 건설을 이용한 지하수 자원의 개발이 하나의 방법으로 제안되었다. 하지만 해안지역에 설치된 지하댐을 운영할 경우 지하수위 변동에 따른 염수의 침입을 고려하여 운영하여야 한다. 특히 갈수시는 지하수위 하강이 강하게 나타나는 시기로 지하수위는 지하댐 최적운영을 위한 중요한 지표가 된다. 특히 강우량 자료를 활용한 가뭄지수와 지하수위의 관계를 설명 할 수 있다면 예상 강우자료를 활용한 장래의 지하수위를 예측 할 수 있으며 이것은 지하댐 운영에 매우 효과적으로 활용 할 수 있을 것이다. 본 연구에서는 기존의 강우와 예상 강우 자료를 활용하여 지하수위 예측기법을 개발하였다. 과거 강수량의 일이동 평균값을 바탕으로 한 다항 회귀모델을 수립하여, 계절적 특성을 고려한 구간을 분리하여 적용하였다. 예측된 지하수위의 정확성을 알아보기 위해 관측된 지하수위와 예측된 지하수위를 비교 분석하였다. 분석 결과 단순회귀기법을 지하수위를 예측한 경우 $0.62{\sim}0.63$의 상관계수를 보인반면 다항회귀기법을 적용한 결과 $0.62{\sim}0.84$로 상관계수가 증가하였다. 대체적으로 관측된 지하수위와 예측된 지하수위는 비슷한 경향을 보였다. 따라서 지하댐 운영에 있어 최적의 취수량을 개발하기위해 일강우자료를 활용한 지하수위 예측기법의 활용성은 매우 높은 것으로 판단된다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.4
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• pp.314-320
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• 2011

In this work, optimization of two-stage shock absorption system for lunar lander has been carried out. Because of complexity of impact phenomena of shock absorption system, a 1-D constitutive model is proposed to describe the behavior of shock absorption system. Quadratic polynomial regression meta-model is constructed by using a commercial software ABAQUS with the proposed 1-D constitutive model, and sequential approximate optimization of two-stage shock absorption system has been carried out along with the constructed meta-model. Through the optimization, it is verified that landing impact force on lunar lander can be considerably reduced by changing the cell size and foil thickness of honeycomb structure in two-stage shock absorption system.

• Annual Conference of KIPS
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• 2022.11a
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• pp.91-93
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• 2022

빨래 건조대는 국내에서 빨래 건조를 위해 주로 사용되지만, 건조 알림 기능이 없어 빨래 건조기에 비하여 사용상의 불편함이 따른다. 본 연구에서는 다항회귀(polynomial regression) 기계학습 모델을 사용하여 빨래 건조시간 예측이 가능한 스마트 빨래 건조 알림 시스템을 제안하였다. 제안된 다항회귀 알고리즘은 빨래 건조대에 부착된 수분센서로부터 측정된 수분량 데이터로부터 옷감 종류에 따른 빨래 건조 시간을 예측하는데 선형회귀보다 높은 정확도를 보였다(면 97.5>95.3%, 합성섬유 94.8>92.8%).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.75-81
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• 1999

This paper presents in the rrethod power demand estimated of consuming facility algorithm using orthogonal polynomial regression rmdel. Estimation rmdel presented can use mathematical rrethod consists. of extrapolation and correlation rrethod, Computation tirre and capacity of presented rmdel was rmre economic than multiple regression rrodel because low-order equation can use in the high-order equation without sorre correction, and vice-versa. Therefore this rmthed can be very usefulness rmthed in the power demand estimation Fourth-order rrodel was very good armng this rrodel that was coJTJp)Sed the estimation rmdel of second, third and fourth-order. Power demand estimated result of consuming facility using correlation rrethod was good in the percentage error of about 2[%1 Also It was to verify efficiency and awroPJiation the estimated rmdel that estimation percentage error was about 1[%] in the oower demand estimated result of 1997.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.26-32
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• 2005

In this paper, the effect of various regression models is investigated on structural optimization using the central composite method. Three bar truss and the upper platform of a satellite are optimized using various regression models that are polynomial, exponential and log functions. Response surface method is non-gradient, semi-global, discrete and fast converging in optimization problem. Sampling points are extracted by the design of experiments using the central composite method. Response surface is generated using the various regression functions. Structural analysis for calculating constraints is executed to find static and dynamic responses. From this study, it is verified that the response surface method has advantage in optimum value and computation time in comparison to other optimization methods.

• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.15-23
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• 2013

Weight reduction of a automobile has been steadily tried in automotive industry to improve fuel efficiency, driving performance and the production profits. Since the weight of BIW takes up a large portion of the total weight of the automobile, reducing the weight of BIW greatly contributes to reducing the total weight of the vehicle. To reduce weight, vehicle manufacturers have tried to apply lightweight materials, such as aluminum and high-strength steel, to the components of BIW instead of conventional steel. In this research, material arrangement of an automotive BIW was optimized by formulating a design problem to minimize weight of the BIW while satisfying design requirements about bending and torsional stiffness and perform a metamodel-based design optimization strategy. As a result of the design optimization, weight of the BIW is reduced by 45.7% while satisfying all design requirements.