• 한국환경과학회지
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• 제19권2호
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• pp.237-245
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• 2010

The relationship between air temperatures and the fraction of urban areas (FUA) and their linear regression equation were estimated using land-use data provided by the water management information system (WAMIS) and air temperatures by the Korea Meteorology Administration (KMA) in the Seoul metropolitan area (SMA) during 1975 through 2000. The future FUA in the SMA (from 2000 to 2030) was also predicted by the urban growth model (i.e., SLEUTH) in conjunction with several dataset (e.g., urban, roads, etc.) in the WAMIS. The estimated future FUA was then used as input data for the linear regression equation to estimate an annual mean minimum air temperature in the future (e.g., 2025 and 2030). The FUA in the SMA in 2000 simulated by the SLEUTH showed good agreement with the observations (a high accuracy (73%) between them). The urban growth in the SMA was predicted to increase by 16% of the total areas in 2025 and by 24% in 2030. From the linear regression equation, the annual mean minimum air temperature in the SMA increased about $0.02^{\circ}C$/yr and it was expected to increase up to $8.3^{\circ}C$ in 2025 and $8.7^{\circ}C$ in 2030.

• 한국전문물리치료학회지
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• 제9권1호
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• pp.43-51
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• 2002

Physiological Cost Index (PCI) of walking has been widely used to predict oxygen consumption in healthy subjects or patients. The purpose of this study was to evaluate the predictability of physiological cost index of walking for the amount of exercise and cardiac function. Walking exercise was conducted in 67 healthy children (age 4-12) with a self-selected comfortable walking speed on the level surface. Walking speed was calculated, and heart rate was measured before and immediately after the walking. PCI was calculated for statistical analysis. The results were as follows; 1) The walking speed tends to increase and PCI of walking tends to decrease with age. There was significant difference in walking speed and PCI of walking among three age groups (p<.05). The change of walking heart rate tends to decrease with age, however, there was no significant difference among three age groups. 2) Linear regression equation between walking speed and age was 'Y (walking speed) = 2.124X (age) + 48.286' ($R^2$=.337), (p=.00). 3) The walking heart rate tends to decrease with age. Linear regression equation between walking heart rate and age was 'Y (walking heart rate) = 143.346 - 2.63X (age)' ($R^2$=.3425), (p=.00). 4) The walking heart rate decreased as body surface area (BSA) increased. Linear regression equation between walking heart rate and BSA was 'Y (walking heart rate) = 149.830 - 27.115X (BSA)' ($R^2$=.3066), (p=.00). In conclusion, these equations and PCI could be useful to quantify the variation of energy expenditure of children with pathological gait when compared with age-matched healthy children.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.575-582
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• 2019

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

• Bulletin of the Korean Chemical Society
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• 제24권12호
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• pp.1757-1762
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• 2003

The interrelation between retention factors of several L-amino acids and their physico-chemical and structural properties can be determined in chromatographic research. In this paper we describe a predictor for retention factors with various properties of the L-amino acids. Eighteen L-amino acids are included in this study, and retention factors are measured experimentally by RP-HPLC. Binding energy ($E_b$), hydrophobicity (log P), molecular refractivity (MR), polarizability (${\alpha}$), total energy ($E_t$), water solubility (log S), connectivity index (${\chi}$) of different orders and Wiener index (w) are theoretically calculated. Relationships between these properties and retention factors are established, and their predictive and interpretive ability are evaluated. The equation of the relationship between retention factors and various descriptors of L-amino acids is suggested as linear and multiple linear form, and the correlation coefficients estimated are relatively higher than 0.90.

• 한국가스학회지
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• 제11권3호
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• pp.13-18
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• 2007

다변량 통계 분석법(Multivariate statistical analysis method)의 대표적 방법인 다중 선형 회귀법(Multiple linear regression. MLR)을 이용하여 2성분계 혼합물의 인화점을 회귀 분석하고 예측하였다. 가연성 물질의 인화점에 대한 예측은 실제 화학 공정 설계에서 화재 및 폭발 위험성을 판단하는 중요한 부분 중의 하나이다. 본 연구에서는 순수 성분의 물성 자료만을 이용하여 2성분계 혼합물의 인화점 실험 자료에 대해 다중 선형 회귀법(MLR)을 수행하였고, 이를 이용하여 새로운 혼합물에 대한 인화점을 예측하였다. 2성분계 혼합물의 인화점에 대한 MLR의 회귀 성능과 새로운 혼합물에 대한 예측 성능을 알아보기 위해, 기존의 인화점 추정 방법인 Raoult의 법칙과 Van Laar식에 의한 추정값과 비교해 보았다.

• 한국철도학회논문집
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• 제19권4호
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• pp.547-554
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• 2016

본 연구는 궤도 틀림을 관리하기 위한 궤도 품질 지수(TQI)의 진전율 추정에 관한 것이다. 이와 관련한 기존 연구 대부분은 시간에 따른 TQI 값의 선형 회귀분석을 통해 구해진 기울기를 기준으로 상수 진전율을 제시하는 데 그치고 있다. 본 연구는 과거 데이터 혹은 전문가의 식견으로부터 도출되는 파라미터의 사전 분포를 효과적으로 반영할 수 있으며, 파라미터값의 확률 분포를 유도해 낼 수 있는 베이지안 방법론에 기초한 진전율 추정 모델을 제안하고, 기존의 전통적인 회귀분석 모형과의 비교 연구를 통해, 베이지안 방법론의 활용 가능성을 검토해 보았다.

• 응용통계연구
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• 제24권1호
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• pp.207-216
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• 2011

본 논문에서는 일반화추정방정식(GEE)모형에 대한 부스트랩 방법의 적용에 대하여 살펴본다. 다양한 부스트랩 방법들 중 GEE모형에 적용이 가능한 잔차, 쌍 및 점수함수 부스트랩 방법을 가상 및 실제 자료들에 적용한 결과 회귀계수들에 대한 추정치와 표준오차가 점근값들과 차이를 보이는 것으로 나타났다. 따라서 표본수가 크지 않은 경우 부스트랩 방법을 통하여 GEE모형에서의 회귀계수에 대한 추정치화 표준편차를 구하는 것이 효과적임을 알 수 있다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.129-133
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• 2023

Classification or prediction problem is how to solve it using a specific feature to obtain the predicted class. A wheat seeds specifications 4 3 classes of seeds will be used in a prediction process. A multi linear regression will be built, and a prediction error ratio will be calculated. To enhance the prediction ratio an ANN model will be built and trained. The obtained results will be examined to show how to make a prediction tool capable to compute a predicted class number very close to the target class number.

• 대한공간정보학회지
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• 제19권4호
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• pp.109-118
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• 2011

본 연구는 지면 데이터�V을 이용하여 다중선형 회귀분석에 의한 평면방정식을 도출하여 전역필터링 한 것을 기준으로 전체 데이터�V을 이용하여 도출된 평면방정식으로 전역필터링 한 것과 가상격자별로 평면방정식을 도출하여 지역필터링을 수행한 결과를 분석하여 정확도를 평가하였다. 그 결과 지면 데이터�V을 이용한 전역필터링의 평균정확도를 기준으로 전체 데이터�V을 이용한 전역필터링의 정확도는 약 2~3%정도 떨어지고, 가상격자를 이용한 지역필터링의 정확도는 약 2~4% 떨어지는 것으로 나타났다. 특히 가상격자가 3~4cm일 때 기준자료와 약 2%의 정확도의 차이가 나타낸 것으로 보아 가상격자 사이즈를 라이다 스캔간격의 3~4배 크기로 지정하여 필터링 하는 것이 바람직 할 것으로 판단된다. 따라서 필터링의 적용방법에 따라 평균정확도가 차이가 발생하였으며, 향후 보다 다양한 실제지형을 선정하여 필터링의 정확도에 대한 연구가 필요할 것으로 생각된다.

• Geomechanics and Engineering
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• 제29권5호
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.