• Asian-Australasian Journal of Animal Sciences
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• v.10 no.1
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• pp.70-74
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• 1997

Data on 2,571 lactation records of Nili-Ravi buffaloes from four institutional herds and four field recording centers were analyzed under an animal model to see the effect of season definition on the error variance of the fitted model. Herd-year-season(HYS) was the main fixed effect along with permanent environment, breeding value and residuals as the random effects. All known relationships among the animals were considered. The error variance differed for various HYS combinations. It was minimum when then months were not grouped into seasons. The four or Five season scenarios were better than the two season scenarios. The average number of lactations represented in a HYS combination varied widely from 6 to 28. Very few subclasses for a given HYS combination warrants the use of fewer seasons for animal model evaluation of buffaloes.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.1
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• pp.75-78
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• 1997

Season is very important as it defines the contemporaries for sire and cow evaluation. An attempt is made for defining season for animal model evaluation of Sahiwal animals, using 1,227 records from 730 cows. Cows were required to have a lactation length of 305-days. Ten different combinations of months for two, four, five or other seasons were tried. The other fixed effect in the model was age defined within parity. The random effects were permanent environment and animal's breeeding value along with the residual effects. A single trait animal model was used where all known relationships of an animal were incorporated in a relationship matrix. The error variance from the fitted model decreased as the number of year-season combinations increased, indicating a month-year model to be more appropriate. This, on the other hand, decreased the number of contemporaries for certain subclasses to a minimum of one, making the bull comparisons invalid. Use of a two season scenario, with winter (November through February) and summer (March through October) was better than the other combinations in terms of error variance of the fitted model and the number of lactations represented in any year-season subclass.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.118-118
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• 2020

This study investigates the change of flow characteristics over 10 Asian river basins in the past 30 years (1976-2005). The variation is estimated from The Soil and Water Assessment Tool (SWAT) model outputs based on reanalysis data which was bias-corrected for Asian monsoon reagion. The model was firstly calibrated and validated using observed data for daily streamflow. Four statistical criteria were applied to evaluate the model performance, including Coefficient of determination (R2), Nash - Sutcliffe model efficiency coeffi cient (NSE), Root mean square error-observations standard deviation ratio (RSR), and Percentage Bias (PBIAS). Then parameters of the model were applied for the historical period 1976-2005. The estimates show a temporal non-considerable increasing rate of daily streamflow in most of the basins over the past 30 years. The difference of monthly discharge becomes more significant during the months in the wet season (June to September) in all basins. The seasonal runoff shows significant difference in Summer and Autumn, when the rainfall intensity is higher. The line showing averaged runoff/rainfall ratio in all basins is sharp, presenting high variation of seasonal runoff/rainfall ratio from season to season.

• The Research Journal of the Costume Culture
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• v.13 no.2 s.55
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• pp.221-239
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• 2005

The purpose of this study was to analyse the fashion style expressed in women magazine advertisements. The method of this study was used content analysis with 692 samples in women magazines ('Woman Sense', 'Yeosung Dong-A', and 'Jubu Saeng-hwal') which were issued in March, June, September, and December from 1998 to 2002. The data analysis were divided nine elements: 1. silhouette, 2. color, 3. pattern, 4. length of skirt & slacks, 5. adjustment, 6. breadth of collar lapel, 7. shoes, 8. make-up, 9. hair style. The results of this study were as follows: 1. silhouette : The four seasons were expressed in square silhouette. 2. Color : The spring, summer, and winter seasons were expressed in white color, while the fall season was expressed in neutral color. 3. Pattern : The four seasons were expressed in plain pattern. 4. Length of skirt & slacks : The four seasons were expressed in various length. 5. Adjustment : The four seasons were expressed in single button. 6. Breadth of collar lapel : The spring, summer, and winter seasons were expressed in small breadth, while the fall season was expressed in middle breadth. 7. Shoes : The four seasons were expressed in high-heeled shoes. 8. Make-up : The four seasons were expressed in light tone. 9. Hair style : The four seasons were expressed in up-style.

• The Journal of Fisheries Business Administration
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• v.54 no.2
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• pp.31-44
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• 2023

This study examines the demand system of shrimp imported from top four countries and domestically produced by using AIDS (Almost Ideal Demand System) model. Top four import countries are Vietnam, Ecuador, China, and Malaysia based on the value of imports in 2021. As results of the analysis, the demand system of shrimp turn out to be below. First, the relationship of domestic shrimp and imported shrimp (Ecuadorian and Vietnamese) is identified as complements or substitutes depending on whether the income effect is considered. This result implies that imported shrimp supplements domestic supply against excess demand while homogeneous shrimp products competes with domestic shrimp in fish market. Second, the relationship among imported shrimps turned out to be both substitutes and complements. Especially, the Vietnamese shrimp is complementary with Chinese and Malaysian shrimp, but substitutes of Ecuadorian. It is assumed that adjoining Asian countries shares similar shrimp species and processing system which differentiates from Ecuadorian. Finally, the study included quarter as dummy variable and GDP as instrumental variable of expenditure in the model. The result confirmed that domestic shrimp is highly on demand during the main production season while imported shrimp is mainly demanded during the rest of the season.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• Asian Journal of Atmospheric Environment
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• v.4 no.2
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• pp.89-96
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• 2010

To identify the domains that have the greatest impacts on air quality at the surface, both the upwind and downwind areas of Seoul were determined by season using refined wind fields. Four consecutive days were selected as the study period typical of each season. The mesoscale meteorology of the study period was reproduced by using the MM5 prognostic meteorological model (PSU/NCAR Mesoscale Model) with horizontally nested grids. The gridded meteorological field, which was used on the study area of $242\;km{\times}226\;km$ with grid spacing of 2 km, was generated by using the CALMET diagnostic meteorological model. Upwind and downwind areas of Seoul were determined by calculating 24-hour backward and forward air parcel trajectories, respectively, with u, v, and w velocity vectors. The results showed that the upwind and downwind areas were extended far to the northwest and the southeast as a result of high wind speeds in the spring and winter, while they were restricted on the fringe of Seoul in the summer and fall.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.359-369
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• 2024

Korea prepares for potential floods by designating June 21st to September 20th as the flood season. However, many dams in Korea have suffered from extreme floods caused by different climate patterns, as in the case of the longest consecutive rain of 54 days in the 2020's flood season. In this context, various studies have tried to develop novel methodologies to reduce flood damage, but no study has ever dealt with the validity of the current statutory flood season thus far. This study first checked the validity of the current flood season through the observation data in the 21st century and proved that the current flood season does not consider the effects of increasing precipitation trends and the changing regional rainfall characteristics. In order to deal with these limitations, this study suggested seven new alternative flood seasons in the research area. The rigid reservoir operation method (ROM) was used for reservoir simulation, and the long short-term memory (LSTM) model was used to derive predicted inflow. Finally, all alternatives were evaluated based on whether if they exceeded the design discharge of the dam and the design flood of the river. As a result, the floods in the shifted period were reduced by 0.068% and 0.33% in terms of frequency and duration, and the magnitude also decreased by 24.6%, respectively. During this period, the second evaluation method also demonstrated that flood decreased from four to two occurrences. As the result of this study, the authors expect a formal reassessment of the flood season to take place, which will ultimately lead to the preemptive flood response to changing precipitation patterns.