• Journal of Ocean Engineering and Technology
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• v.19 no.4 s.65
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• pp.35-41
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• 2005

There has been increasing offshore oil exploration, drilling, and production activities, as well as a huge amount of petroleum being transported by tankers and pipelines through the ocean and costal environment. Assessment must be made of the potential risk of damage resulting from the exploration, development and transportation activities. This is achieved through predictive impact evaluations of the fate of hypothetical or real oil spills. VVhen an oil spill occurs, planning and execution of cleanup measures also require the capability to forecast the short-term and long-term behavior of the spilled oil. A great amount of effort has been spent by government agencies, oil industries, and researchers over the past decade to develop more realistic models for oil spills. Numerous oil spill models have been developed and applied, most of which attempt to predict the oil spill fate and behavior. For an actual contingency planning, the oil fate and behavior model should be combined with an oil spill incident model, an environmental impact and risk model and a contingency planning model. The purpose of this review study is to give an overview of existing oil spill models that deal with the physical, chemical, biological, and socia-economical aspects of the incident, fate, and environmental impact of oil spills. After reviewing the existing models, future research needs are suggested. In the study, available oil spill models are separated into oil spill incident, oil spill fate and behavior, environmental impact and risk, and contingency planning models. The processes of the oil spill fate and behavior are reviewed in detail and the characteristics of existing oil spill fate and behavior models are examined and classified so that an ideal model may be identified. Finally, future research needs are discussed.

• Journal of the Korean Society of Costume
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• v.52 no.2
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• pp.31-44
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• 2002

This study was intended to inquire into Check Pattern. The purpose of this study attempted to make a systematic investigation of the characteristic of the Check Pattern, the checker using vertical and horizontal lines which was the universal plastic element and inquire into it in terms of era, designers and combined work. By doing so, this study attempted to Investigate the phase of the Check Pattern in world fashion and further forecast the future of checker design applicable to the 21th century fashion. The result can be summarized as follows : 1. Mondrim's neo-plasticism has not only had a great influence on Op Art and Minimalism work but is deeply related to fashion and textile design. Mondrian used vertical and horizontal line ad the dualistic element. 2. The checker is estimated to have been used since the Etruian times, though uncertain. and largely divided into the Madras check and Scotland Check. Though the origin of the tartan representation of the Scotland check can not be accurately found out, it began to emerge in around the 13th century. 3. Check Pattern has began to be widely used with the development of the textile industry since 1826 and been used in every typical Sihoutto appearing in each era up to the present. And Check Pattern is used most designer in the world, who represent their own personality in their works. This study could find out that the checker is the element of Infinite applicability in the future. It is expected that the sophisticated and beautiful design using the chocker will be presented by many korean designer though the overall and systematic study of the checker.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.156-162
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• 2001

In order to monitor local climatic information, twelve automated weather stations (AWS) were installed in alpine area by the Alpine Agricultural Experiment Station, Rural Development Administration (RDA), at the field of major crop located in around highland area, and collected data from 1993 to 2000. Hourly measurements of air and soil temperature (underground 10 cm,20 cm), relative humidity, wind speed and direction, precipitation, solar radiation and leaf wetness were automatically performed and the data could be collected through a public phone line. Datalogger was selected as CR10X (Campbell scientific, LTD, USA) out of consideration for sensers' compatibility, economics, endurance and conveniences. All AWS in alpine area were combined for net work and daily climatic data were analyzed in text and graphic file by program (Chumsungdae, LTD) on 1 km $\times$ 1 km grid tell basis. In this analysis system, important multi-functionalities, monitoring and analysis of local climatic information in alpine area was emphasized. The first objective was to obtain the output of a real time data from AWS. Secondly, daily climatic normals for each grid tell were calculated from geo-statistical relationships based on the climatic records of existing weather stations as well as their topographical informations. On 1 km $\times$ 1 km grid cell basis, real time climatic data from the automated weather stations and daily climatic normals were analyzed and graphed. In the future, if several simulation models were developed and connected with this system it would be possible to precisely forecast crop growth and yield or plant disease and pest by using climatic information in alpine area.

• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.10
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• pp.1-7
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• 2009

Nowadays there are many studies and there's huge development about RFID and WSN which have great developmental potential to many kinds of applications. In particular, the healthcare field is expected to could be securing international competitive power in u-Healthcare and combined medical treatment industry and service. More and more real time application apply RFID and WSN technology to identify, data collect and locate objects. Wide deployment of RFID and WSN will generate an unprecedented volume of primitive data in a short time. Duplication and redundancy of primitive data will affect real time performance of application. Thus, emerging applications must filter primitive data and correlate them for complex pattern detection and transform them to events that provide meaningful, actionable information to end application. In this paper, we design a complex event processing system. This system will process RFID and WSN primitive data and event and perform data transformation. Integrate RFID and WSN system had applied each now in medical treatment through this study and efficient data transmission and management forecast that is possible.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.325-334
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• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.448-451
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• 2008

Three methods of nozzle flow analysis, frozen-equilibrium, shifting-equilibrium and non-equilibrium approaches, were used to rocket nozzle flow, those were coupled with the methods of computational fluid dynamics code. For a design of high temperature rocket nozzle, chemical equilibrium analysis which shares the same numerical characteristics with frozen flow analysis can be an efficient design tool for predicting maximum thermodynamic performance of the nozzle. Frozen fluid analysis presents the minimum performance of the nozzle because of no consideration for the energy recovery. On the other hand, the case of chemical-equilibrium analysis is able to forecast the maximum performance of the nozzle due to consideration for the energy recovery that is produced for the fast reaction velocity compared with velocity of moving fluid. In this study, using the chemical equilibrium flow analysis code that is combined the modified frozen-equilibrium and the chemical-equilibrium. In order to understand the thermochemical characteristic components and the accompanying energy recovery, shifting-equilibrium flow analysis was carried out for the 30 $ton_f$-class KARI liquid rocket engine nozzle together with frozen flow. The performance evaluation based on the 30 $ton_f$-class KARI LRE nozzle flow analyses will provide an understanding of the thermochemical process in the nozzle and performances of nozzle.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.