• Korean Journal of Oriental Medicine
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• v.2 no.1
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• pp.485-495
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• 1996

Oriental medicine is a traditional medical are that has maintained its medical system only in Korea, Japan and China, nowadays, it is required to systematize the Oriental medicine modernly as well as prove remedical value of it for public welfare and hygiene, medical studies about geriatric disease and ageing are valued highly in researchs of future medical service which were excuted by Korea, Japan and Germay. consequently, future - estimating project of the Oriental medicine which keeps and accent on research datas that have a curative effect highly must be constructed. in the cause of this, effieient and systematic subject selection should be preceded to accomplish an advisable planning of the Oriental medicine.

• Journal of the Korean Magnetics Society
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• v.15 no.2
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• pp.162-166
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• 2005

자성재료는 각종 전자품에서 빼놓을 수 없는 중요한 소재로서 가전제품, 컴퓨터, 통신설비, 자동차 및 국방산업과 직결되어 있다. 현재 중국은 각종 자성재료의 생산량이 세계 1위이며 자성재료 생산대국과 산업중심의 역할을 하고 있다. 중국 자성재료의 중, 장기 시장 발전 전망이 매우 유망한 바, 중국의 자성재료 생산품의 전세계에서의 인식은 한층 더 올라갈 것으로 본다. 과학기술 독창능력, 기술개조와 기업의 관리수준을 반드시 강화시키고 산업구조를 조절하고 생산품의 등급을 한 단계 상승시켜 중국을 자성재료 대국으로부터 강국으로 진입하도록 인도해야 한다는 전략은 지난 11월초 중국 상해에서 개최한 "중국 자성재료 산업 중장기 발전 전략 포럼"에서 제기된 것이다. 본문에서는 이 포럼에서 발표된 내용과 산업계의 통계자료를 바탕으로 거시적인 각도에서 중국 자성체 산업의 전체적인 현황을 분석하였고 희토류 영구자석 특히 중국의 소결과 본드 NdFeB 자석의 산업현황을 소개했고, 희토류 영구자석에 대한 중국의 연구개발 상황을 소개했으며 자성체 산업발전의 전망에 대한 예측과 분석도 수행하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.84-86
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• 2018

본 연구는 해양수산부가 지원하는 해외항만개발협력사업의 일환으로, 중미국가인 엘살바도르 주요 2개항의 항만개발 타당성조사 및 기본계획 수립을 목적으로 하되 장래 해외항만개발사업에서 참고할 수 있도록 정리하였다. 주요 내용으로는 국가 일반현황 및 지역분석, 인프라 현황 분석, 시장조사 및 물동량 수요추정, 항만 개발규모 및 중장기 개발방향 제시, 경제성 및 재무성 분석을 통해 수원국에 필요한 국가 상위계획 자료를 제공하는 것이며, 연구결과가 계획에만 그치지 않도록 사업시행을 위한 재원조달 방안 및 항만 활성화 방안 등 실질적 사업현실화 방안을 제시하였다.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.81-98
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• 2018

Port logistics is essential for Korea's economy which heavily rely on international trade. Vast amounts of capital and time are consumed for the operation and development of ports to improve their competitiveness. Therefore, it is important to forecast cargo volume in order to establish the optimum level of construction and development plan. Itemized forecasting is necessary for appropriate port planning, since disaggregate approach is able to provides more realistic solution than aggregate forecasting. We introduce a new time series model which is Two-way Seasonality Multiplied Regressive Model (TSMR) to forecast oil cargo volume, which accounts for a large portion of total cargo volume in Korea. The TSMR model is designed to take into account the characteristics of oil cargo volume which exhibits trends with short and long-term seasonality. To verify the TSMR model, existing forecasting models are also used for a comparison reason. The results shows that the TSMR excels the existing models in terms of forecasting accuracy whereas the TSMR displays weakness in short-term forecasting. In addition, it was shown that the TSMR can be applied to other cargoes that have trends with short- and long-term seasonality through testing applicability of the TSMR.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.261-274
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• 2006

This study performs a daily long-term runoff analysis for 30 years to forecast medium- and long-term probabilistic reservoir inflows on the Soyang River basin. Snowmelt is computed by Anderson's temperature index snowmelt model and potenetial evaporation is estimated by Penman-combination method to produce input data for a rainfall-runoff model. A semi-distributed TOPMODEL which is composed of hydrologic rainfall-runoff process on the headwater-catchment scale based on the original TOPMODEL and a hydraulic flow routing model to route the catchment outflows using by kinematic wave scheme is used in this study It can be observed that the time variations of the computed snowmelt and potential evaporation are well agreed with indirect observed data such as maximum snow depth and small pan evaporation. Model parameters are calibrated with low-flow(1979), medium-flow(1999), and high-flow(1990) rainfall-runoff events. In the model evaluation, relative volumetric error and correlation coefficient between observed and computed flows are computed to 5.64% and 0.91, respectively. Also, the relative volumetric errors decrease to 17% and 4% during March and April with or without the snowmelt model. It is concluded that the semi-distributed TOPMODEL has well performance and the snowmelt effects for the long-term runoff computation are important on the study area.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.230-240
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• 2020

Recently, the social debate about the fourth industrial revolution has been actively developed, and it is predicted that the 4th Industrial Revolution will have a great influence on our society, cities, residential and industrial spaces. Especially, it is anticipated that the technological development of the 4th Industrial Revolution will cause a wide range of changes in residential style and culture. Therefore, it is necessary to grasp the direction of future change in advance and proactively respond to future tasks and strategies need. The purpose of this study is to predict the direction and characteristics of the mid - to long - term changes in future housing that will be brought about by the 4th Industrial Revolution and to define future social, spatial and technological impacts and issues and to find policy measures for them. STEEP (V) as a methodology for forecasting future has been used. It is a process of deriving technical and social issues by using Big Data. It collects various keywords and draws out key issues and summarizes social change patterns related to each core issue. The proposed strategy for future housing prediction and countermeasures can be used as a basic data for future directions of housing policy and suggests a process for deriving reasonable and reasonable results from multiple data sets rather than accurate prediction.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.