• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.667-681
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• 2013

In this study, monthly and seasonal dissolved oxygen trends of 19 water quality measurement stations in Sapgyo stream watershed were analyzed using monthly dissolved oxygen (DO) data measured for 16 years (1995~2010). Mann-Kendall trend test and Sen's slope estimator were carried out for trend analysis. Furthermore, Sapgyo stream watershed was divided into four different sections (Sapgyo stream, Muhan stream, Gykgyo stream, and Sapgyo lake) and chi-square test of homogeneity for DO trend was carried out for four different sections. The study results indicated that most of water quality measurement stations showed increasing or non-significant trend of DO on a monthly and seasonal basis. The chi-square test of homogeneity for each water quality measurement station showed the statistical homogeneity in seasonal DO trend; however, the test results showed the statistical non-homogeneity in monthly DO trend for the stations located in the reservoir. Overall, the dissolved oxygen trend in each water quality measurement station showed different patterns depending on the location of each station and season.

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2004.05a
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• pp.29-29
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• 2004

• ALGAE
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• v.19 no.1
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• pp.39-47
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• 2004

Production dynamics of eelgrass, Zostera marina was examined in Jindong Bay on the south of the Korea peninsula. Eelgrass leaf productivities and environmental factors such as underwater photon flux density, water temperature, and nutrient availabilities in the water column and sediments were monitored from March 2002 to December 2003. While water temperature exhibited a distinct seasonal trend, underwater irradiance and nutrient availabilities exhibited high degree of fluctuation, and did not show a seasonal trend throughout the experimental periods. Eelgrass leaf elongation and production rates showed significant seasonal variations. Leaf productivity was highest in May (30.0 mg dry wt sht$^{-1}$ d$^{-1}$ or 3.7g dry wt m$^{-2}$d$^{-1}$) and lowest in November (3.2 mg dry wt sht$^{-1}$ d$^{-1}$ or 0.12 g dry wt m $^{-2}$ d$^{-1}$). Eelgrass leaf productivities did not show a strong correlation with underwater irradiance or environmental nutrient availabilities. The production rates, however, were positively correlated with water temperature during spring periods, and were correlated negatively at high water temperature exceeded 20℃ during summer months. While relative growth rates were highest in spring and lowest in high water temperature periods, plastochrone interval was longest during summer and shortest during spring. These results imply that seasonal growth dynamics of eelgrass, Z. marina was mainly controlled by water temperature.

• Atmosphere
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• v.19 no.2
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• pp.155-168
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• 2009

Characteristics of seasonal mean diurnal temperature range (DTR) and their causes over South Korea are investigated using the 60 stations data of Korea Meteorological Administration from 1976 to 2005. In general, the seasonal mean DTR is greatest during spring (in inland area) and least during summer (urban and coastal area). The spatial and seasonal variations of DTR are closely linked with the land surface conditions (especially vegetation activity and soil moisture) and atmospheric conditions (cloud amount, precipitation, local circulation). The seasonal mean DTR shows a decreasing trend at the major urban areas and at the north-eastern part of South Korea. Whereas, it shows an increasing trend at the central area of the southern part. Decreasing and increasing trends of DTR are more significant during summer and fall, and during spring and winter. The decrease (increase) of DTR is mainly caused by the stronger increase of daily minimum (maximum) temperature than daily maximum (minimum) temperature. The negative effects of precipitation and cloud amount on the DTR are greater during spring and at the inland area than during winter and at the coastal area. And the effect of daytime precipitation on the DTR is greater than that of nighttime precipitation.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.101-115
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• 2005

We propose in this article a procedure for testing unit and fractional orders of integration, with the roots simultaneously occurring in the trend, the seasonal and the cyclical component of the time series. The tests have standard null and local limit distributions. However, finite sample critical values are computed, and several Monte Carlo experiments conducted across the paper show that the rejection frequencies against unit (and fractional) orders of integration are relatively high in all cases. The tests are applied to the UK consumption and income series, the results showing the importance of the roots corresponding to the trend and the seasonal components and, though the unit roots are found to be fairly suitable models, we show that fractional processes (including one for the cyclical component) may also be plausible alternatives in some cases.

• Journal of Fashion Business
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• v.11 no.1
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• pp.1-14
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• 2007

This study was aimed at giving help to the people intending to change their own hair color design and also providing the guide line to the cosmetic circles for developing new hair color design and promoting sales by statistically analyzing seasonal changes of hair colors puplished on women's magazines(Vogue Korea, Estetica Korea, Woman Chosun, Ce.ci) from 2003 to 2006. The researching methods were as follows; (1) hair colors published on women's magazines from september 2003 to August 2006 were measured by N.C.S. color reader(4 magazines $\times$10 main hair colors/magazine $\times$ 12 months $\times$ 3 years = 1,440 colors). (2) N.C.S. tone is made of percentage, so measured values and chromas were statistically analyzed by mean, standard deviation, and seasonal deferences were statistically analyzed by t-test and specified on high significant values. But hues were not made of percentage, so these were statistically analyzed by cross tabulation analysis, $x^2$ -test and specified on high significant values. These all had been analyzed by SPSS program(ver. 11.0). The results were as follows; (1) Usually seasonal changes of hair values were significant, specially in foreign licensed magazines, and bright values appeared in S/S and dark values in F/W. (2) Seasonal changes of hair hues were significant only on foreign women's magazines. Therefore seasonal changes of korean hair colors were not significant compared by foreign hair colors because of hardness of color changes of dark black hair and hair damages by hair tints and bleaches and trends of well being and hair care. But hair color changes have been developed gradually and will developed furthermore. So korean hair cosmetic circles have to present hair color trends deferenciated by seasons. And S/S hair values have to be brignt and F/W have to be dark. And new seasonal hair hues matched by korean have to be developed and presented.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.77-90
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• 1995

The Lagrange multiplier test statistics for seasonal unit roots are derived and the asymptotic distributions of the derived statistics are obtained. The relationship between the derived LM test statistics and the "DHF" type regression t statistics are shown.are shown.

• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.