• Korean Journal of Pharmacognosy
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• v.17 no.2
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• pp.123-128
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• 1986

The determination of berberine in Berberis koreana was examined by the high performance liquid chromatography using ${\mu}-Bondapak$ $C_{18}$ column and $CH_3CN/phosphate$ buffer (pH 5.2) (60/40) as a mobile phase. The seasonal variations of berberine contents in Berberis koreana were as follows; 1) In roots, the average berberine content was 0.94% with the highest level of 1.32% in October. 2) In stems, their average berberine content was about 0.1% and in March, April, October and November, the contents were relatively high. 3) In leaves, however, the content was as low as 0.005%. According to the experimental results obtained, we found that Berberis koreana roots can serve as the useful natural plant resources for the berberine and October is the optimal season for the collection.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.4 no.3
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• pp.204-216
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• 1992

In most cases, EER(Energy Efficiency Ratio) is available to present energy efficiency of air-conditioners. But, EER is not adapt to measure energy efficiency at actual life environment because it is based on fixed temperature and humidity contditions. To overcome this disadvantage, there is need to introduce SEER(Seasonal Energy Efficiency Ratio) established at time varient temperature and humidity conditions. In this paper, SEER measurement method and conditions based on actual life environment of the country is introduced, and discussed SEER value about two air-conditioner type, that is, non inverter air-conditioner and inverter air-conditioner. As a result of, inverter air-conditioner was superior to non inverter air-conditioner at cooling seasonal energy efficiency.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.231-242
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• 2016

X-13-ARIMA (a popular time series analysis software) provides $3{\times}3$, $3{\times}5$, $3{\times}9$, $3{\times}15$ moving average filters for seasonal adjustment. However, there has been questions on their performance and the need for new filters is a constant topic due to Korean economic time series often containing higher irregularity and more various seasonality than other countries. In this study, two newly developed seasonal moving average filters, $3{\times}7$ and $3{\times}11$, are introduced. New filters were implemented in X-13-ARIMA and applied to 15 economic time series to demonstrate their suitability and reliability. The result shows that some series are more stable when using new seasonal moving average filters. More accurate time series analyses would be possible if newly proposed filters are used together with existing filters.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.117-124
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• 2009

If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

• Journal of the Korean Solar Energy Society
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• v.27 no.2
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• pp.11-17
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• 2007

This study evaluates the seasonal performances of the horizontal-type geothermal heat exchanger(HGHEX) installed into the foundation slabs of the complex building located at Seoul. The geothermal system is consisted with totally 31,860m long HGHEX, 16 GSHPs (Ground-source Heat Pump) and 8 circulation pumps. This system supplies cooling and heating to the lobby(F1) and the common spaces(BF1). The average heat exchange temperature differences are $2.7^{\circ}C\;and\;2.5^{\circ}C$ in the summer, $1.5^{\circ}C\;and\;0.5^{\circ}C$ in the winter for the F1 and BF1 respectively. From these results, approximately 400Gcal and 180Gcal of geothermal energy are assumed to have been used during the summer and winter seasons respectively. As a conclusion, the geothermal system is reviewed as a effective utility for heating and cooling at the point of seasonal performances.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• International Journal of Industrial Entomology and Biomaterials
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• v.9 no.2
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• pp.193-198
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• 2004

Seasonal effects of the newly evolved bivoltine hybrid namely CSR$_2$${\times}$SR$_{5}$, SR$_1$ ${\times}$SR$_4$ and control hybrid KA${\times}$NB$_4$D$_2$ along with their parents SR$_1$, SR$_4$, SR$_{5}$, CSR$_2$ KA and NB$_4$D$_2$ were evaluated during different seasons of the year to understand genotype and environment interactions. Data were collected on five economic importance namely, pupation rate, cocoon yield, cocoon weight, cocoon shell ratio and filament length of the lines, hybrids and the control breeds/hybrid in three different seasons i.e., Pre-Monsoon, Monsoon and post-monsoon and subjected to relevant statistical methods. Seasonal performance of CSR$_2$, SR$_1$, SR$_4$ and SR$_{5}$ revealed superiority over control breeds KA and NB$_4$D$_2$. Both the hybrids i.e., CSR$_2$${\times}$SR$_{5}$ and SR$_1$${\times}$SR$_4$ performed well under diversified environmental conditions of tropical climate in a year indicating overall stability. These hybrids revealed highly significant (P < 0.01) variations for majority of the traits studied over the control hybrid KA${\times}$NB$_4$D$_2$.$.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.650-663
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• 2011

The purpose of this study was to investigate the satisfaction of service quality for seasonal visitors of Korea National Arboretum using Importance-performance analysis. Data were obtained from visitors of Korea National Arboretum on spring, summer, autumn. Results of analysis showed that all season visitor's satisfaction was high. The IPA was undertaken with total 24 variables about service quality of arboretum. Results of IPA was founded that summer visitors want to improve some services (information facilities, forest museum, conveniences, etc). Autumn visitors, on the other hand, thought that some services (kindness, active responsiveness, variety of plant, etc) are overinvested. As a results, there is differences between importance and satisfaction about seasonal service quality.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.293-304
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• 2024

In this paper, efficient and reliable methodologies for conducting sea trials to evaluate the performance of hull-mounted sonar systems is discussed. These systems undergo performance verification during ship construction via sea trials. However, the evaluation procedures often lack detailed consideration of variabilities in detection performance due to seabed topography, seasonal factors. To resolve this issue, temperature and salinity structure data were collected from 1967 to 2022 using ARGO floats and ocean observers data. The paper proposes an efficient and reliable sea trial method incorporating Bellhop modeling. Furthermore, a machine learning model applying a Physics-Informed Neural Networks was developed using the acquired data. This model predicts the sound speed profile at specific points within the sea trial area, reflecting seasonal elements of performance evaluation. In this study, we predicted the seasonal variations in sound speed structure during sea trial operations at a specific location within the trial area. We then proposed a strategy to account for the variability in detection performance caused by seasonal factors, using results from Bellhop modeling.