• 대한원격탐사학회지
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• 제4권1호
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• pp.49-60
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• 1988

Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

• Journal of Positioning, Navigation, and Timing
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• 제2권2호
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• pp.131-137
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• 2013

In this study, positioning results that combined the code observation information of GPS and GLONASS navigation satellites were analyzed. Especially, the distribution of GLONASS satellites observed in Korea and the combined GPS/GLONASS positioning results were presented. The GNSS data received at two reference stations (GRAS in Europe and KOHG in Goheung, Korea) during a day were processed, and the mean value and root mean square (RMS) value of the position error were calculated. The analysis results indicated that the combined GPS/GLONASS positioning did not show significantly improved performance compared to the GPS-only positioning. This could be due to the inter-system hardware bias for GPS/GLONASS receivers, the selection of transformation parameters between reference coordinate systems, the selection of a confidence level for error analysis, or the number of visible satellites at a specific time.

• Asian-Australasian Journal of Animal Sciences
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• 제33권10호
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• pp.1544-1557
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• 2020

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (r_M1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (r_M2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The r_M1 estimates in the study were between 0.90 and 0.96 among five traits. The r_M1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average r_M2 estimates were much smaller than r_M1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, r_M2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of r_M2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the r_M2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based r_M2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

• 청정기술
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• 제24권1호
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• pp.63-69
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• 2018

순산소 연소 기술 중 $CO_2$ 회수 비용 절감 효과가 가장 우수한 케미컬루핑연소(chemical looping combustion, CLC) 기술의 핵심인 산소전달입자의 선정을 위해 환원반응 특성 및 물리화학적 특성에 대한 연구를 진행하였다. 세 종류의 산소전달입자(SDN70, N018-R2, N016-R4)를 대상으로 기포유동층 반응기에서 환원반응기체의 농도 및 환원 반응 온도 변화에 따른 산소전달입자의 연료전화율(fuel conversion)과 $CO_2$ 선택도($CO_2$ selectivity)를 측정 및 비교 분석하였다. 또한 산소전달입자의 마모손실 정도 및 입자의 표면 특성을 분석하기 위해 내마모도(Attrition Index, AI) 및 BET surface area를 측정하였다. 결과적으로 세 종류의 산소전달입자 모두 케미컬루핑연소 시스템에 활용하기 적합함을 확인하였으며, 가장 우수한 입자는 N016-R4로 판단되었다.

• 대한전자공학회논문지TC
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• 제45권2호
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• pp.13-18
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• 2008

본 논문에서는 M 개의 전송 안테나를 가진 송신국(source)이 단일 안테나를 가진 R 개의 릴레이(relay)를 이용하여 단일 안테나를 가진 수신국(destination)에 신호를 전송하는 증폭 후 전달(amplify-and-forward, AF) 릴레이 시스템에서의 outage 확률 성능을 살펴본다. 이때, R 개의 릴레이 중에서 수신국에서의 수신 신호에 대한 가장 큰 신호 대 잡음비(signal-to-noise ratio, SNR)를 보장하는 하나의 릴레이를 선택하는 최적 릴레이 선택(best relay selection) 기법이 사용되고, 송신국과 선택된 릴레이 링크에서 송신 maximum-ratio-combining(transmit MRC)이 적용되었을 때의 AF 릴레이 시스템의 outage 확률을 분석한다. 또한, 분석의 타당성을 입증하기 위해 모의실험들을 제공한다.

• 대한치과보철학회지
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• 제40권5호
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• pp.484-492
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• 2002

The selection of the maxillary anterior artificial teeth is made primarily for esthetics and they must be in harmony with the surrounding oral environment. However the selection of artificial teeth is based on the large degree of subjective judgement or the dentists, therefore, this is one of the most unscientific processes. This study was performed to determine clinically whether there is correlation among the width of the maxillary central incisor(WMCI), the intercanine distance (ICD) the facial width(FW), and the interalar nasal width(IAW) in Korean adults, and to provide the selection standards for the maxillary anterior artificial teeth. The casts were obtained from 91 undergraduate dental students(49 males and 42 females) with Angle's class I occlusion presenting well-arranged intact anterior teeth. The WMCI and ICD were measured on the casts with a vernier calipers($Miltex^{(R)}$, Germany). The photographic procedures under standardized conditions were performed to record each subject's frontal face using digital camera($Olympus^{(R)}$, C-2500L, Japan). The FW and IAW were measured with image analyzer($Image-Pro^{(R)}$ PLUS. media cybermetrics. USA). The results were obtained as follows : 1. The mean WMCI was $8.11{\pm}0.67mm$, ICD was $37.88{\pm}2.15mm$, FW was $141.29{\pm}5.84mm$. and IAW was $37.85{\pm}2.29 mm$. 2. The ratios of FW/WMCI, FW/ICD, IAW/ICD were 17.4, 3.7, 1.0 respectively. 3. All measurements(WMCI, ICD, FW, and IAW) of male group were longer than those of female group significantly in Student's t-test(p<0.01). 4. There was significant correlation between WMCI, ICD, FW, and IAW in Pearson's correlation analysis(p<0.01). 5, The relationship between IAW and ICD shows the strongest correlation among six combinations in linear regression analysis($R^2$=0.753, Y=7.046+0.815X). The FW and IAW could be very reliable guides for the selection of the maxillary anterior artificial teeth.

• 응용통계연구
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• 제18권3호
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• pp.661-671
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• 2005

본 연구는 반복분할(recursive partitioning)에 의한 군집화 방법을 개발하고 활용 예를 보인다. 노드 분리 기준으로는 Overall R-Square를 채택하였고 실용적인 노드 분리 결정 방법을 제안하였다. 이 방법은 연속형 자료에 대하여 나무 형태의 해석하기 쉬운 단순한 규칙을 제공하면서 동시에 변수선택기능을 제공한다. 환용 예로서 Fisher의 붓꽃데이터와 Telecom 사례에 적용해 보았다. K-평균 군집화와 다른 몇 가지 사항이 관측되었다.

• 한국환경과학회지
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• 제14권12호
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• pp.1203-1209
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• 2005

Basic dyes, Rhodamine 6G(R6G), Rhodamine B(RB), and Methylene Blue(MB), dissolved in water were used to investigate single-component adsorption affinity to the pearl layer fractionated according to the size. Unfractionated pearl layers were also used as adsorbents for the R6G and RB. The Langmuir and the Redlich-Peterson(RP) models were used to fit the adsorption data, and the goodness of fit was examined by using determination coefficient($R^2$) and standard deviation(SSE). The 3-parameter RP model was found to be better in describing the dye adsorption data than the 2 parameter Langmuir model, as can be expected from the number of parameters involved in the model. The adsorption affinity to the fractionated pearl layer was higher than that to the unfractionated layer The affinity order to the fractionated Conchiolin layer was found to be R6G > MB > RB. Furthermore, the dye adsorption capacity of the various types of pearl layer was found to be in the order, the fractionated pearl > powdered pearl > unfractionated pearl, exhibiting different adsorption isotherms according to the types of layer used in the study.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.661-668
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• 2006

There have been some controversies on the use of the coefficient of determination for linear no-intercept model. One definition of the coefficient of determination, $R^2={\sum}\;{\widehat{y^2}}\;/\;{\sum}\;y^2$, is being widely accepted only for linear no-intercept models though Kvalseth (1985) demonstrated some possible pitfalls in using such $R^2$. Main objective of this note is to report that $R^2$ is not a desirable measure of fit for the no-intercept linear model. In fact it is found that mean square error(MSE) could replace $R^2$ efficiently in most cases where selection of no-intercept model is at issue.

• Korean Chemical Engineering Research
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• 제62권3호
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• pp.214-224
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• 2024

밸브는 유량과 압력 조절 등의 중요한 역할을 수행하며, 적절한 밸브 사이즈와 유형 선택이 필요하다. Engineering Procurement Construction (EPC) 산업에선 밸브 사이즈 계수(C_v)의 수식적 계산을 바탕으로 사이즈와 유형을 선정해왔으나 이러한 방식은 전문가의 많은 시간과 비용이 요구되어 비효율적이다. 본 연구는 이를 해결하기위해 머신 러닝기법을 이용한 밸브 사이즈 및 유형 예측 모델을 개발하였다. Artificial neural network (ANN), Random Forest, XGBoost, Catboost의알고리즘을 적용하여 모델들을 개발하였으며, 평가 지표로는 사이즈 예측에는 Normalized root mean squared error (NRMSE)와 R²를, 종류 예측에는 F1 score를 적용하였다. 또한, 유체 상에 따른 영향을 확인하고자 유체 전체, 액체, 기체, 스팀의 4개의 데이터 세트로 사례 연구를 진행하였다. 연구 결과, 사이즈의 경우 전체, 액체, 기체에선 Catboost(R²기준, 전체: 0.99216, 액체: 0.98602, 기체: 0.99300. NRMSE 기준, 전체: 0.04072, 액체: 0.04886, 기체: 0.03619)가, 스팀에선 Random Forest가(R²: 0.99028, NRMSE: 0.03493) 가장 뛰어난 모델임을 확인하였다. 종류의 경우 Catboost가 모든 데이터에서 가장 높은 성과를 제시하였다(F1 score 기준, 전체: 0.95766, 액체: 0.96264, 기체: 0.95770, 스팀: 1.0000). 본 연구에서 제안한 모델들을 적용할 경우, 주어진 조건에 따른 밸브 선택을 도와 의사결정 속도를 높여줄 것으로 기대된다.