• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.370-374
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• 2006

SWAT을 구동하기 위해서는 유역의 지형자료와 시계열 자료뿐만 아니라 토지경작과 오염물질의 거동에 관계하는 많은 비공간 데이터가 필요하다. 이와 같이 방대한 자료를 이용하여 효과적으로 SWAT을 구동하기 위하여 GIS 시스템과 SWAT을 연계 운영할 수 있는 프로그램을 개발하고, 이를 실무에 이용하고 있다. 본 연구에서는 HyGIS(과학기술부, 2004)와 SWAT2000 모형의 연계 시스템인 HyGIS-SWAT의 개발을 위하여 HyGIS-SWAT 데이터 모델을 기반으로 하는 시스템의 운영프로세스를 정립하였으며, 이에 따른 데이터베이스를 설계 및 구축 하였다. 또한 SWAT2000 모형의 구동에 필요한 HRU를 계산하기 위한 알고리즘을 개발하였으며, 입력매개변수의 자동계산 모듈을 개발하였다. 연구결과 HyGIS-SWAT의 시범 시스템을 개발할 수 있었으며, HyGIS-SWAT 데이터 모델과 HyGIS-Model 통합시스템의 운영표준은 HyGIS를 이용한 응용프로그램 개발에 효과적으로 이용될 수 있는 것으로 나타났다. 또한 HyGIS-SWAT의 개발과정에서 축적된 기술은 HyGIS와 다양한 수자원 모형의 연계 시스템 개발 시에 기반기술로 이용될 수 있을 것이다.

• Journal of the Korean Society for information Management
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• v.17 no.3
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• pp.147-170
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• 2000

RDF treats all resources independently, that's integrated description model for internet resources description, that provides the model to connect resources that related with the specific resources. This article performs theoretical review of RDF model and syntax specification and RDF schema specification that's a common rule of semanics, structure and syntax those provides search and access in the area of dispersed information environment of internet and Dublin Core that's description element for build metadata. And with this way it's materialized metadata design, schema, DTD of Dublin Core element for building RDF-based metadata that is XML application.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.1
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• pp.185-214
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• 2007

This study intends to design the bibliographic framework based on IFLA-FRBR model for television program materials and to evaluate this in terms of effectiveness of retrieval and usability of the system. The FRBR model supplies mote suitable bibliographic framework of audio-visual material which has a sufficient hierarchical relations and relative bibliographical records. The followings are research methods designed by this study; (1) The experimental metadata system named it FbCS based on FRBR was developed by using the entity-related database and composed of multi-layed and hierarchy. FbCS is developed through benchmarking of a case study for iMMix model in Netherlands based on FRBR. (2) To evaluate effectiveness of retrieval and usability of FbCS, this study made a experiment and survey by user groups of professionals.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.874-878
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• 2006

수자원 분야에서 이용하고 있는 모형을 구동하기 위해서는 대상 지역의 지형특성과 시계열 자료 및 비공간적 매개변수 등의 많은 정보가 필요하다. 이러한 자료들은 다양한 과정을 거쳐서 모형에 입력되며, 모의대상이 복잡한 구조를 가지고 있거나 모형의 구동조건이 변할 경우, 모형의 구동과 결과의 관리를 위해서는 더욱 많은 노력이 필요하게 된다. 따라서 이러한 자료들을 효과적으로 관리하고 운용하는 것은 모형구동의 효율성과 객관성을 유지하는데 매우 중요한 요소가 될 수 있다. 이를 위하여 국내 기술로 개발된 GIS 기반의 수자원시스템인 HyGIS(Hydro Geographic Information System)와 수자원 모형을 연계하여 운영할 수 있는 시스템을 개발하고자 하며, 이를 HyGIS-Model이라고 한다. 본 연구에서는 HyGIS의 시공간 데이터 모델을 소개하고, HyGIS-Model 중 HyGIS와 SWAT2000 모형이 연계된 시스템(HyGIS-SWAT)을 개발하기 위한 데이터 모델링에 대해서 기술하고자 한다. 연구결과 HyGIS 데이터 모델과 HyGIS-Model 통합시스템 운영 표준은 HyGIS-SWAT 데이터 모델링과 시스템 설계에 효과적으로 적용될 수 있었다. 이를 통하여 GIS와 수자원 모형의 연계 시스템을 개발하기 위한 시스템 설계에 대한 기술을 확보할 수 있었으며, GIS를 이용한 수자원 모형의 입력자료의 생성, 운영 및 모형 구동 결과의 관리에 대한 표준적 절차를 수립할 수 있었다.

• Korean Journal of Construction Engineering and Management
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• v.14 no.2
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• pp.131-140
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• 2013

Econometric forecast models based on past time-series data have been applied to a wide variety of applications due to their advantages in short-term point estimating. These models are particularly used in predicting the impact of governmental research and development (R&D) programs because program managers should assert their feasibility due to R&D program's huge amount of budget. The construction governmental R&D programs, however, separately make an investment by dividing total budget into five sub-business area. It make R&D program managers difficult to understand how R&D programs affect the whole system including economy because they are restricted with regard to many dependent and dynamic variables. In this regard, system dynamics (SD) model provides an analytic solution for complex, nonlinear, and dynamic systems such as the impacts of R&D programs by focusing on interactions among variables and understanding their structures. This research, therefore, developed SD model to capture the different impacts of five construction R&D sub-business by considering different characteristics of sub-business area. To overcome the SD's disadvantages in point estimating, this research also proposed the method for constructing quantitative forecasting model using qualitative data. Understanding the different characteristics of each construction R&D sub-business can support R&D program managers to demonstrate their feasibility of capital investment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.1-13
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• 2023

This study develops an optimal mobile-communication-based National Highway traffic volume estimation model using an ensemble-based machine learning algorithm. Based on information such as mobile communication data and VDS data, the LightGBM model was selected as the optimal model for estimating traffic volume. As a result of evaluating traffic volume estimation performance from 96 points where VDS was installed, MAPE was 8.49 (accuracy 91.51%). On the roads where VDS was not installed, traffic estimation accuracy was 92.6%.

• Proceedings of the Korean Society for Information Management Conference
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• 2002.08a
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• pp.115-121
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• 2002

인터넷과 웹 기술의 발전은 전자도서관의 목록정보나 목차정보서비스에서 원문정보서비스로의 변화를 주도하고 있다. 그러나 대학에서 생산하는 학문적 가치가 높은 학술지들은 웹을 통해 원문으로 서비스되고 있지만 상이한 OPAC 시스템과 목록을 위한 통일된 메타데이터의 부재로 원문정보를 공유$.$활용하기가 매우 힘든 실정이다. 본 고에서는 각 대학에서 생산하고 구축된 원문정보를 메타데이터 기반의 목록시스템을 이용하여 모든 대학의 학술지 논문정보를 검색하여 공유할 수 있는 분담목록시스템모형을 제시함으로써 대학 학술지 이용의 효율적 방안을 모색 해보고자 한다.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.185-185
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• 2020

기후변화의 영향으로 국지적 규모의 홍수, 가뭄 등의 피해 규모가 증가하고 있으며, 복사에너지 변화에 기인한 전지구적 대류활동의 변화는 단발성 피해에 확산되어 특정 지역의 기후 패턴 변화로 이어질 수 있다. 대류활동의 변화는 국가별 물순환의 변화로 이어질 수 있으며, 이로 인한 수자원의 변동성은 국가적 수자원 이용에 있어 중요한 요소로 작용될 수 있다. 수자원의 중요성으로 인해 국제적인 기관들은 전지구적 대류활동에 기인한 물순환 과정을 파악하고자 노력하였으며, 그 일환으로 GCMs (Global climate modeling) 등과 같은 모형이 개발되었고, 위성을 통한 전지구 강우량 측정망을 구축하였다. 위성을 통한 전구 강우량 자료와 GCMs에서 산출된 대류과정과 연관된 기후변량 자료들은 빅데이터로 구축되어 제한 없이 제공되고 있다. 정상성 강우 모의 기법은 데이터에 한정된 패턴을 반영하는 모형들로서 기후변화로 인한 기후 변동성 증가를 반영하는데 한계가 존재한다. 본 연구에서는 기상 빅데이터 자료를 기반으로 한반도의 강우량과 기상학적 특성을 연관할 수 있는 머신러닝의 일종인 딥러닝 방법을 접목시킨 강우 모의 기법을 적용하였다. 본 연구의 모형은 기후변화로 인한 기상학적 패턴의 변화를 딥러닝 기법을 통해 식별하고 식별된 기상학적 특성에 기반한 한반도의 강우량을 모의할 수 있다. 본 모형은 단기 및 장기 예측 모형과 결합하여 불확실성을 고려한 단/장기 강우량 평가에 활용될 수 있을 것으로 기대된다.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.