• 한국경영과학회지
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• 제42권1호
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• pp.65-77
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• 2017

The purpose of this paper is to develop a model of level evaluation for data governance that can diagnose and verify level of insufficient part of operating data governance. We expanded the previous study related on attribute indices of data governance and developed a level model of evaluation and items. The model of level evaluation for data governance is the level of evaluation and has items of 400 components. We used previous studies and expert opinion analysis such as the Delphi technique, KJ method in this paper. This study contributes to literature by developing a level evaluation model for data governance at the early phase. This paper will be used for the base line data in objective evidence of performance in the companies and agencies of operating data governance.

• Asia pacific journal of information systems
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• 제2권1호
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• pp.3-15
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• 1992

This paper proposes a new referential model for data retrieval as an alternative to exact matching. While exact matching is an effective data retrieval model, it is based on fairly strict assumptions and limits our capabilities in data retrieval. This study redefines data retrieval to include non-binary data retrieval in addition to binary data retrieval, proposes Double Anchor Preference Model (DAPM), and analyzes its logical charateristics. DAPM supports non-binary data retrieval. Further, it produces the same result as exact matching for the conventional binary data retrieval. These findings show that, at the logical level, the proposed DAPM retains all the desirable features for data retrieval.

• 한국측량학회지
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• 제23권1호
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• pp.31-38
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• 2005

국가 기반시설로서의 도로의 중요성은 상당한 비중을 차지하고 있다. 이에 따른 도로의 신설과 유지관리 비용은 지속적으로 증가하고 있다. 이와 같은 도로관리에 대한 비용 문제를 해결하기 위해서는 기본지리정보를 통한 합리적이고 효율적인 데이터관리가 필요하다. 그러나 현재까지의 기본지리정보 연구는 각 기관별 항목중심으로 연구되어 많은 양의 데이터가 중복구축 되었다. 이에 따라 국토지리정보원에서는 국제표준에 맞는 기본지리정보 지침안을 제시하였다. 본 연구는 동일한 데이터 가공에 대한 비용과 반복구축에 대한 비효율성을 방지하기 위하여 교통분야 기본지리정보를 적용한 도로관리 데이터모델을 제시하였다. 또한 위의 기본지리정보에 의해서 데이터모델을 개발한 사례가 많지 않다. 그렇기 때문에 기본지리정보 데이터모델 지침안이 실제 주제별 데이터모델 구축시 클래스, 속성, 관계 등이 실제 적용하기에 적절한지 고찰하였고 도로관리 데이터모델 실제 적용하였다.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.448-453
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• 2008

This study describes design and development techniques of estimation models for process modeling. One case study is undertaken to design a model using standard gas furnace data. Neural networks (NN) and genetic programming (GP) are each employed to model the crucial relationships between input factors and output responses. In the case study, two models were generated by using 70% training data and evaluated by using 30% testing data for genetic programming and neural network modeling. The model performance was compared by using RMSE values, which were calculated based on the model outputs. The average RMSE for training and testing were 0.8925 (training) and 0.9951 (testing) for the NN model, and 0.707227 (training) and 0.673150 (testing) for the GP model, respectively. As concern the results, the NN model has a strong advantage in model training (using the all data for training), and the GP model appears to have an advantage in model testing (using the separated data for training and testing). The performance reproducibility of the GP model is good, so this approach appears suitable for modeling physical fabrication processes.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.518-523
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• 2008

In this study, we analyzed applicability of object database, designed the concept model based on object-oriented idea for measurement data management, and applied the design model to object database. The concept model composes three sub models Infrastructure managing information model, Infrastructure measurement data model, and Measurement unit model. The process to expand measurement data of new type was executed easily without changing database schema in object database. The process to expand measurement data of new type was executed easily without changing database schema in object database. Therefore, applicability of new technology to infrastructures for building a safety management network of road bridges could be increased with object database system.

• 한국CDE학회논문집
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• 제5권4호
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• pp.293-300
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• 2000

The non-manifold geometric modeling technique is to improve design process and to Integrate design, analysis, and manufacturing by handling mixture of wireframe model, surface model, and solid model in a single data structure. For the non-manifold geometric modeling, Euler operators and other high level modeling methods are necessary. Boolean operation is one of the representative modeling method for the non-manifold geometric modeling. This thesis studies Boolean operations of non-manifold model with the data structure of selective storage. The data structure of selective storage is improved non-manifold data structure in that existing non-manifold data structures using ordered topological representation method always store non-manifold information even if edges and vortices are in the manifold situation. To implement Boolean operations for non-manifold model, intersection algorithm for topological cells of three different dimensions, merging and selection algorithm for three dimensional model, and Open Inventor(tm), a 3D toolkit from SGI, are used.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.713-718
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• 2007

Structural tests often involve a large amount of complicated information. Data models can be used to efficiently organize the information. This paper briefly describes a data model for structural tests including hybrid tests, performed by the Real-Time Multi-Directional (RTMD) facility at the ATLSS Center of Lehigh University in America. The RTMD facility is an equipment site within the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES). The data model is called the Lehigh Model. An overview of classes and attributes of the Lehigh Model is presented, and the Lehigh Model is compared with other data models to show its benefits for structural testing.

• 한국문헌정보학회지
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• 제57권1호
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• pp.213-231
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• 2023

본 연구는 도서관의 데이터 성숙도를 평가할 수 있는 모형(안)을 개발하고 제시하는 것을 목적으로 한다. 이를 위해 데이터 성숙도와 관련된 선행연구를 분석하여 도서관에 적용할 수 있는 성숙도 평가모형을 구성하고자 하였다. 본 연구의 결과 5개 영역의 19개 평가 요소로 구성된 데이터 성숙도 모형을 설계하였고, 성숙도 단계는 5단계로 설정하였다. 향후 데이터 성숙도 평가모형을 이용하여 도서관 빅데이터 사업에 참여하고 있는 도서관의 데이터 성숙도를 측정할 수 있을 것이며, 장기적으로 데이터 기반 도서관 운영 및 데이터 활용 발전 방향성을 제시할 수 있을 것으로 기대할 수 있다.

• 한국수산과학회지
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• 제30권6호
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• pp.964-973
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• 1997

A tidal prism eutrophication model, an one-dimensional intertidal model, is developed to study water quality conditions at small coastal basins and tidal creeks. The model simulates the physical transport processes using the concept of tidal flushing. The concept is simple and straightforward, and thus is ideal for small coastal basins with complex geometry. The model, having twenty-four state variables in the water column, simulates salinity, temperature, dissolved oxygen, three algal groups, and the cycles of carbon, nitrogen, phosphorus and silica. The model is applied to the Lynnhaven Bay, a small coastal basin of Chesapeake Bay in U.S.A. The model is calibrated using the field data collected in 1994, and then is verified using the independently collected data in 1980. The model overall gives a good reproduction of the field data, partly owing to the data collected from the field surveys specifically designed for the model application. This paper presents the procedure, and the results of the model calibration and verification.