• 대한화장품학회지
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• 제25권2호
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• pp.129-138
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• 1999

피부 전용 제재 개발을 위해 요구되는 동물 대체 시험법 중 가장 적극적으로 연구가 행하여지곤 실제 실용화가 예상되는 것은 안점막 자극 시험으로 지금까지 여러 가지 방법이 개발되었지만 그 중 계란 유정란의 응모요막(CAM)을 이용한 방법이 현재 가장 활발히 진행되고 있다. 이 방법이 일부 국가에서 이미 안점막 자극 시험 동물 대체 시험법으로 공인되었으며 현재까지도 validation 연구를 활발히 진행하고 있다. 본 연구에서도 국내에 적합한 안점막 자극 시험 동물 대체 시험법의 공인 시험법 개발 및 validation study를 목표로 계란 유정란의 응모요막을 이용한 방법 중 HET-CAM 방법을 시행하였으며 안점막 동물 대체 시험법으로 확립하고자 하였다. 틴ET-CAM 방법의 보완을 위해 배양된 세포를 통해 자극도를 측정할 수 있는 방법인 Cytotoxicity test를 도입하여 시행하였으며 두 방법의 data들을 분석하여 validation study를 수행하였다. 국내 유수의 6개 장업사가 본 연구에 참가하여 20가지의 화장품 전용제재를 대상으로 1차, 2차 validation study 를 진행하였다. HET-CAM test, Draize eye irritation test, Cytotoxicity test 측정 결과 HET-CAM 의 “Q” 수치는 대부분 강자극 수치인 2 이상이었고 10% sodium hydroxide가 가장 높은 수치를 보였으며 Tween 20(sorbitanpolyoxyethylene monolaurate) 100%가 가장 낮은 수치를 보였다. In vi패의 경우 10% sodium hydroxide가 가장 높은 수치를 보였으며 30군 propylene glycol 이 가장 낮은 자극수치를 보였다. HET-CAM test 와 Draize eye irritation test, Cytotoxicity test 간의 상관성 분석은 linear correlation coefficient 와 rank correlation coefficient를 구하여 비교하였으며 6개 장업사(A-F)의 실험실에서의 HET-CAM test 결과를 취합하여 각각 두 실험실간의 상관관계(linear correlation)를 분석하였다. Linear correlation coefficient 분석 결과를 보면 전반적으로 상관관계가 0.589 - 0.954의 범위였으며, 특히 A사와 B사 사이의 경우 0.954이었으며, E사와 D사 사이의 경우 0.942로 높은 상관관계를 보였다. 그 외에도 A사와 D사 사이의 경우(0.589)와 B사와 D사 사이의 경우(0.638)를 제외하고는 대체로 높은 상관관계를 나타내었다.

• Journal of Astronomy and Space Sciences
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• 제30권3호
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• pp.163-168
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• 2013

Parameters associated with solar minimum have been studied to relate them to solar activity at solar maximum so that one could possibly predict behaviors of an upcoming solar cycle. The number of active days has been known as a reliable indicator of solar activity around solar minimum. Active days are days with sunspots reported on the solar disk. In this work, we have explored the relationship between the sunspot numbers at solar maximum and the characteristics of the monthly number of active days. Specifically, we have statistically examined how the maximum monthly sunspot number of a given solar cycle is correlated with the slope of the linear relationship between monthly sunspot numbers and the monthly number of active days for the corresponding solar cycle. We have calculated the linear correlation coefficient r and the Spearman rank-order correlation coefficient $r_s$ for data sets prepared under various conditions. Even though marginal correlations are found, they turn out to be insufficiently significant (r ~ 0.3). Nonetheless, we have confirmed that the slope of the linear relationship between monthly sunspot numbers and the monthly number of active days is less steep when solar cycles belonging to the "Modern Maximum" are considered compared with rests of solar cycles. We conclude, therefore, that the slope of the linear relationship between monthly sunspot numbers and the monthly number of active days is indeed dependent on the solar activity at its maxima, but that this simple relationship should be insufficient as a valid method to predict the following solar activity amplitude.

• 천문학회보
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• 제41권1호
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• pp.79.3-80
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• 2016

We have investigated the correlation analysis between global temperature anomaly and two main factors: geomagnetic activity (aa index) of Earth external factor and CO2 of Earth internal factor. For this, we used NOAA Global Surface Temperature anomaly (Ta) data from 1868 to 2015. The aa index indicates the geomagnetic activity measured at two anti-podal subauroral stations (Canberra Australia and Hartland England) and the CO2 data come from historical ice core records and NOAA/ESRL data. From the comparison between (Ta) and aa index, we found several interesting things, First, the linear correlation coefficient between two parameters increases until 1985 and then decreases rapidly. Second, the scattered plot between two parameters shows a boundary of the correlation tendency (positive and negative correlation) near 1985. A partial correlation of (Ta) and two main factors (aa index, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1985 and the CO2 effect becomes much more important after then. These results indicate that the CO2 effect become very an important factor since at least 1985. For a further analysis, we simply assume that Ta = Ta(aa)+Ta(CO2) and made a linear regression between (Ta) and aa index from 1868 to 2015. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta-Ta(aa) since 1985. Our results will be discussed in view of the prediction of global warming.

• Journal of applied mathematics & informatics
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• 제9권2호
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• pp.807-815
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• 2002

We consider a more general linear regression super-population model than the one of Chaudhuri and Stronger(1992) . We can find the same type of the best linear unbiased(BLU) predictor as that of Chaudhuri and Stenger and see that the optimal design is again a purposive one which prescribes choosing one of the samples of size n which has $\chi$ closest to $\bar{X}$.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.345-350
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• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• 환경위생공학
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• 제23권2호
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• pp.19-25
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• 2008

For measurement of tylosin, ammonia nitrogen, and glucose concentration during the culture of Streptomyces fradiae using Near Infrared Spectroscopy, the calibration using various mathematical models was performed and then, based on the linear model, the validation was carried out. In the case of sucrose concentration using the MLR method, the Standard Error of Prediction and Multiple correlation coefficient were 1.97, and 0.991, respectively. In the case of ammonia nitrogen concentration using the PLSR method, the Standard Error of Prediction and Multiple correlation coefficient were 0.13, and 0.990, respectively. In the case of tylosin concentration using the PLSR method, the standard Error of Prediction and Multiple correlation coefficient were 0.54, and 0.984, respectively.

• 한국정밀공학회지
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• 제25권7호
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• pp.72-78
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• 2008

The line-of-sight(LOS) stabilization system is a precision electro-mechanical gimbals assembly for suppressing vibration due to its environment and tracking the target in a desired direction. This paper describes the design of gimbals system to reject the disturbance and to improve stabilization. The controller consists of a DSP with transducer and actuator interfaces. Unknown parameters of the gimbals are estimated by the signal compression method. The cross-correlation coefficient between the impulse response from the assumed model and the one from model of the gimbals is used to obtain the better estimation. The quasi-impulse response through linear element included in the gimbals could be obtained by the signal compression method. The unknown parameter of the linear element could be estimated as comparing the bode plots for impulse response from gimbals with them from model's response.

• Advances in materials Research
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• 제11권1호
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• pp.75-90
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• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.303-310
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• 2007

In this paper, the correlation between the vibration characteristics of the track components and the parameters affecting the vibration is analysed. To do it, the accelerations of each track component such as rails, sleepers and ballast are measured in Kyong-Bu high-speed railway lines. The RMS values of the measured data are calculated and the corrugation, the track irregularity and the pad stiffness are considered as the parameters in the viewpoint of track. By using the linear regression, the correlation coefficient is calculated to analyse the relationship. The parameter whose correlation coefficient is more than 0.7 is considered as the major one. Also, the 1/3 Octave analysis is calculated to analyse the dominant frequency band of the vibrations of the track components.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1131-1134
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• 2003

Unknown parameters of a nonlinear system were estimated using a signal compression method. The estimated parameters were natural frequency and tile damping coefficient. This study applied a algorithm using tile comparison of the cross-correlation coefficient between the impulse response from a model and it from the signal compression method. The impulse through linear element included in a nonlinear system could be obtained by the signal compression method. The unknown parameters of the linear element could be estimated by comparing the Bode plots of system's impulse response with them of model's response. In this study, a LSCM(LabVIEW-Signal-Compression-Method) was developed to identify a nonlinear system. The LSCM consisted of National Instrument's (NI) Data Acquisition (DAQ) Board (Model PCI-1200), a monitoring program using LabVIEW software package, DAQ Signal Accessory Board, and 2nd-order electric circuits. The designed electric circuits consisted of resistors, inductors and capacitors. To evaluate the performance of the LSCM, the response from model with known parameters is compared with the response from the real system using the monitoring program. The results from simulation of experiment showed that the developed LSCM provided a reliable estimation performance.