• Journal of Applied Biological Chemistry
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• v.62 no.4
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• pp.433-439
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• 2019

Artemisia species are widely used as food ingredients and raw material in traditional medicine. However, to date, the secondary metabolites of Artemisia gmelinii Weber ex Stechm. have not been sufficiently investigated. The secondary metabolites of A. gmelinii, which was collected from representative regions in Chungbuk, Gangwon, and Gyeongbuk, were analyzed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTof MS) combined with an unsupervised principal component analysis (PCA) multivariate analysis. In the loading scatter plot of PCA, significant changes in metabolites were observed between the regions, ten metabolites (3: 5-O-caffeoylquinic acid, 4: 4-O-caffeoylquinic acid, 8: trans-melilotoside, 12: quercetin 3-O-hexoside, 15: 3,4-O-dicaffeoylquinic acid, 17: 3,5-O-dicaffeoylquinic acid, 18: 4,5-O-dicaffeoylquinic acid, 19: syringaldehyde, 20: caffeoylquinic acid derivative, and 23: icariside II) were evaluated as key markers among twenty-five identified metabolites. Interestingly, the contents of the identified marker significantly differed between the three groups. This is the first study to report the presence of marker metabolites and their correlating geographical cultivation in A. gmelinii.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.1
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• pp.88-93
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• 2011

This experiment was carried out to find suitable sample type for the more accurate prediction and non-destructive way in the application of near infrared reflectance spectroscopy (NIRS) technique for estimation the protein, total amino acids, and total isoflavone of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds powder. The coefficient of determination in calibration ($R^2$) and coefficient of determination in cross-validation (1-VR) for three components analyzed using NIRS revealed that milled powder sample type yielded the highest, followed by single seed, and the whole seeds as the lowest. The coefficient of determination in calibration for single seed was moderately low($R^2$ 0.70-0.84), while the calibration equation developed with NIRS data scanned with whole seeds showed the lowest accuracy and reliability compared with other sample groups. The scatter plot for NIRS data versus the reference data of whole seeds showed the widest data cloud, in contrary with the milled powder type which showed flatter data cloud. By comparison of NIRS results for total isoflavone, total amino acids, and protein of soybean seeds with three sample types, the powder sample could be estimated for the most accurate prediction. However, based from the results, the use of single bean samples, without grinding the seeds and in consideration with NIRS application for more nondestructive and faster prediction, is proven to be a promising strategy for soybean component estimation using NIRS.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.289-299
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• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.

• Journal of Korean Society on Water Environment
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• v.30 no.2
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• pp.148-158
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• 2014

In order to assess the effect of TMDLs management and improve that in the future, it is necessary to analyze long-term changes in water quality during management period. Therefore, long term trend analysis of BOD was performed on thirty monitoring stations in Geum River TMDL unit watersheds. Nonparametric trend analysis method was used for analysis as the water quality data are generally not in normal distribution. The monthly median values of BOD during 2004~2010 were analyzed by Seasonal Mann-Kendall test and LOWESS(LOcally WEighted Scatter plot Smoother). And the effect of Total Maximum Daily Loads(TMDLs) management on water quality changes at each unit watershed was analyzed with the result of trend analysis. The Seasonal Mann-Kendall test results showed that BOD concentrations had the downward trend at 10 unit watersheds, upward trend at 4 unit watersheds and no significant trend at 16 unit watersheds. And the LOWESS analysis showed that BOD concentration began to decrease after mid-2009 at almost all of unit watersheds having no trend in implementation plan watershed. It was estimated that TMDLs improved water quality in Geum River water system and the improvement of water quality was made mainly in implementation plan unit watershed and tributaries.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.61-74
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• 2020

Interval-valued data, one type of symbolic data, are observed in the form of intervals rather than single values. Each interval-valued observation has an internal variation. Principal component analysis reduces the dimension of data by maximizing the variance of data. Therefore, the principal component analysis of the interval-valued data should account for the variance between observations as well as the variation within the observed intervals. In this paper, three principal component analysis methods for interval-valued data are summarized. In addition, a new method using a truncated normal distribution has been proposed instead of a uniform distribution in the conventional quantile method, because we believe think there is more information near the center point of the interval. Each method is compared using simulations and the relevant data set from the OECD. In the case of the quantile method, we draw a scatter plot of the principal component, and then identify the position and distribution of the quantiles by the arrow line representation method.

• Journal of the Korea Concrete Institute
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• v.14 no.1
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• pp.67-75
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• 2002

For the design of concrete structures in the serviceability limit state, the uniaxial static modulus of elasticity may be a most important parameter. In particular, this may be so just for a deflection control of the structure. Even in new concrete codes, however, the elastic modulus is normally presented on the form of general empirical relationships with the compressive strength and density of concrete. Normally, there is a large uncertainty associated with the general equations obtained by regression. Thus, in a typical plot of static modulus of elasticity vs. compressive strength, a large scatter can be observed at same strength. The aim of this study is to present the method for obtain the maximum modulus of elasticity at same compressive strength. In the present paper report the effects of mix ingredients on the modulus of elasticity of high-strength concrete. The test of 284 cylinder specimens arc conducted for type I with 11 ％ replacement of fly-ash cement concretes. Different water-hinder ratio, amounts of water and coarse aggregate as variables were investigated. And also analyzed it statistically by using SAS.

• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.539-547
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• 2018

Objectives: Ozone ($O_3$) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high $O_3$ concentrations cannot be considered ideal measures. Forecasts of $O_3$ would be more beneficial to citizens' health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface $O_3$ concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan's air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between $NO_2$ and $O_3$ was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for $O_3$ and $NO_x$ were also found. Our model suggests that insolation is the most influential factor in determining the concentration of $O_3$. $CH_4$ also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted $O_3$ concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface $O_3$ for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for $O_3$ forecasting was not carried out, the model suggested in this study would be very helpful for the citizens of Korea and Japan, especially during the $O_3$ season from May to June.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.59-67
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• 2020

Growth tests on the Wando and Baengnyeongdo cultivars of Saccharina japonica were performed at the Myeongcheon and Gyedo aquafarms, Goheung in Jeollanamdo, from February to July in 2003. Five environmental conditions and 2 traits were measured monthly. The data were used to analyze the growth patterns, relationships between traits and principal component. Box plots were used to display the growth patterns. Scatter plots and regression and correlation coefficients were used to determine the strength of relationships between the traits. A principal component analysis revealed that the first principal component explained more than 91.4% and 90.5% of the total sample variance in the Myeongcheon and Gyedo aquafarms. From the viewpoint of the economic traits (blade length, blade weight), the growth of populations from the Gyedo aquafarm was stronger than that of those from the Myeongcheon aquafarm, and the growth of the Baengnyeongdo cultivar was superior to that of the Wando one.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1516-1516
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• 2001

During the recent years, wine analysis has played an increasing role due the health benefits of phenolic ingredients in red wine [1]. On the other hand there is the need to be able to distinguish between different wine varieties. Consumers want to know if a wine is an adulterated one or if it is based on the pure grape. Producers need to certificate their wines in order to ensure compliance with legal regulations. Up to now, the attempts to investigate the origin of wines were based on high-performance liquid chromatography (HPLC), gas chromatography (GC) and pyrolysis mass spectrometry (PMS) [l,2,3]. These methods need sample pretreatment, long analysis times and therefore lack of high sample throughput. In contradiction to these techniques using near infrared spectroscopy (NIRS), no sample pretreatment is necessary and the analysis time for one sample is only about 10 seconds. Hence, a near infrared spectroscopic method is presented that allows a fast classification of wine varieties in bottled red wines. For this, the spectra of 50 bottles of Cabernet Sauvignon, Lagrein and Sangiovese (Chianti) were recorded without any sample pretreatment over a wavelength range from 1000 to 2500 nm with a resolution of 12 cm$\^$-1/. 10 scans were used for an average spectrum. In order to yield best reproducibility, wines were thermostated at 23$^{\circ}C$ and a optical layer thickness of 3 mm was used. All recorded spectra were partitioned into a calibration and validation set (70% and 30%). Finally, a 3d scatter plot of the different investigated varieties allowed to distinguish between Cabernet Sauvignon, Lagrein and Sangiovese (Chianti). Considering the short analysis times this NRS-method will be an interesting tool for the quality control of wine verification and also for experienced sommeliers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.