• 제목/요약/키워드: Data Modelling

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The Design and Implementation Methodology of Multilevel Secure Data Model Using Object Modelling Technique (객체 모델링 기법을 이용한 다단계 보안 데이터 모델의 설계와 구현 방안)

  • 심갑식
    • Journal of the Korea Institute of Information Security & Cryptology
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    • 제8권3호
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    • pp.49-62
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    • 1998
  • 본 논문은 객체 모델링 기법을 이용하여 다단계 보안 데이터베이스 응용에 대한 구조적 특징을 표현하기 위한 모델을 제시한다. 즉, 응용 영역에 대한 데이터와 보안 의미르 통합한다. 이는 응용 영역의 데이터에 대한 불법적 유출이나 수정을 방지하는 도구가 된다 . 개발한 도구를 기초로 한 구현 모델에서는 다단계 데이터베이스를 단일 보안등급 데이터베이스들로 분해한다. 인스턴스뿐만 아니라 스키마도 보호하며 속성값 다중 인스턴스화 기법을 이용하여 커버 스토리를 표현한다. 그리고 그 모델에서의 생성, 검색, 삭제, 그리고 갱신과 같은 연산 의미를 설명한다.

Numerical analysis of sedimentary compaction: Implications for porosity and layer thickness variation (수치해석적 다짐 작용 연구: 공극률과 퇴적층 두께 변화에 미치는 영향)

  • Kim, Yeseul;Lee, Changyeol;Lee, Eun Young
    • Journal of the Geological Society of Korea
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    • 제54권6호
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    • pp.631-640
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    • 2018
  • To understand the formation and evolution of a sedimentary basin in basin analysis and modelling studies, it is important to analyze the thickness and age range of sedimentary layers infilling a basin. Because the compaction effect reduces the thickness of sedimentary layers during burial, basin modelling studies typically restore the reduced thickness using the relation of porosity and depth (compaction trend). Based on the compilation plots of published compaction trends of representative sedimentary rocks (sandstone, shale and carbonate), this study estimates the compaction trend ranges with exponential curves and equations. Numerical analysis of sedimentary compaction is performed to evaluate the variation of porosity and layer thickness with depth at key curves within the compaction trend ranges. In sandstone, initial porosity lies in a narrow range and decreases steadily with increasing depth, which results in relatively constant thickness variations. For shale, the porosity variation shows two phases which are fast reduction until ~2,000 m in depth and slow reduction at deeper burial, which corresponds to the thickness variation pattern of shale layers. Carbonate compaction is characterized by widely distributed porosity values, which results in highly varying layer thickness with depth. This numerical compaction analysis presents quantitatively the characteristics of porosity and layer thickness variation of each lithology, which influence on layer thickness reconstruction, subsidence and thermal effect analyses to understand the basin formation and evolution. This work demonstrates that the compaction trend is an important factor in basin modelling and underlines the need for appropriate application of porosity data to produce accurate analysis outcomes.

A Framework for Building Reconstruction Based on Data Fusion of Terrestrial Sensory Data

  • Lee, Impyeong;Choi, Yunsoo
    • Korean Journal of Geomatics
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    • 제4권2호
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    • pp.39-45
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    • 2004
  • Building reconstruction attempts to generate geometric and radiometric models of existing buildings usually from sensory data, which have been traditionally aerial or satellite images, more recently airborne LIDAR data, or the combination of these data. Extensive studies on building reconstruction from these data have developed some competitive algorithms with reasonable performance and some degree of automation. Nevertheless, the level of details and completeness of the reconstructed building models often cannot reach the high standards that is now or will be required by various applications in future. Hence, the use of terrestrial sensory data that can provide higher resolution and more complete coverage has been intensively emphasized. We developed a fusion framework for building reconstruction from terrestrial sensory data, that is, points from a laser scanner, images from digital camera, and absolute coordinates from a total station. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large complex existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS with reasonable resources.

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Applications of the ANFIS and LR in the prediction of strain in tie section of concrete deep beams

  • Mohammadhassani, Mohammad;Nezamabadi-pour, Hossein;Jameel, Mohammed;Garmasiri, Karim
    • Computers and Concrete
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    • 제12권3호
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    • pp.243-259
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    • 2013
  • Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.

A Comparison of Optimization Algorithms: An Assessment of Hydrodynamic Coefficients

  • Kim, Daewon
    • Journal of the Korean Society of Marine Environment & Safety
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    • 제24권3호
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    • pp.295-301
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    • 2018
  • This study compares optimization algorithms for efficient estimations of ship's hydrodynamic coefficients. Two constrained algorithms, the interior point and the sequential quadratic programming, are compared for the estimation. Mathematical optimization is designed to get optimal hydrodynamic coefficients for modelling a ship, and benchmark data are collected from sea trials of a training ship. A calibration for environmental influence and a sensitivity analysis for efficiency are carried out prior to implementing the optimization. The optimization is composed of three steps considering correlation between coefficients and manoeuvre characteristics. Manoeuvre characteristics of simulation results for both sets of optimized coefficients are close to each other, and they are also fit to the benchmark data. However, this similarity interferes with the comparison, and it is supposed that optimization conditions, such as designed variables and constraints, are not sufficient to compare them strictly. An enhanced optimization with additional sea trial measurement data should be carried out in future studies.

A Study on the Modelling of Combustion in a Small DI Diesel Engine (소형 DI 디젤 기관의 연소 모델링에 관한 연구)

  • Koh, D.K.;Kim, K.H.;Jang, S.H.;Ahn, S.K.
    • Journal of Power System Engineering
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    • 제2권2호
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    • pp.20-26
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    • 1998
  • Heat release data were obtained by analysis of cylinder diagrams from a test engine, naturally-aspirated small-size four-stroke DI diesel engine. These data were used to decide empirical coefficients of Whitehouse-Way's model, single zone combustion model. Finally, the comparison of calculated with experimental results was performed, and the accuracy of calculated versus experimental data of the model in predicting engine heat release and cylinder pressure was demonstrated.

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Random Effects Models for Multivariate Survival Data: Hierarchical-Likelihood Approach

  • Ha Il Do;Lee Youngjo;Song Jae-Kee
    • Proceedings of the Korean Statistical Society Conference
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    • 한국통계학회 2000년도 추계학술발표회 논문집
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    • pp.193-200
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    • 2000
  • Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

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SAR RETURN SIGNAL SYNTHESIS IN TIME-SPATIAL DOMAIN

  • Shin Dongseok;Kim Moon-Gyu;Kwak Sunghee
    • Proceedings of the KSRS Conference
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    • 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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    • pp.729-732
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    • 2005
  • This paper describes a time-spatial domain model for simulating raw data acquisition of space-borne SAR system. The position, velocity and attitude information of the platform at a certain time instance is used for deriving sensor-target model. Ground target is modelled by a set of point scatters with reflectivity and two-dimensional ground coordinates. The signal received by SAR is calculated for each slow and fast time instance by integrating the reflectivity and phase values from all target point scatters. Different from frequency domain simulation algorithms, the proposed time domain algorithm can provide fully physical modelling of SAR raw data simulation without any assumptions or approximations.

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A Joint Frailty Model for Competing Risks Survival Data (경쟁위험 생존자료에 대한 결합 프레일티모형)

  • Ha, Il Do;Cho, Geon-Ho
    • The Korean Journal of Applied Statistics
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    • 제28권6호
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    • pp.1209-1216
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    • 2015
  • Competing-risks events are often observed in a clustered clinical study such as a multi-center clinical trial. We propose a joint modelling approach via a shared frailty term for competing risks survival data from a cluster. For the inference we use the hierarchical likelihood (or h-likelihood), which avoids an intractable integration. We derive the corresponding h-likelihood procedure. The proposed method is illustrated via the analysis of a practical data set.

A Logical Design Methodology for Relational Databases Using the MAO (Multiple Aspects-based Object) Model (MAO 모델을 사용한 관계 데이타베이스의 논리적 설계방법론)

  • Cho, Dong-Young;Baik, Doo-Kwon;Hwang, Chong-Sun
    • Journal of the Korean Operations Research and Management Science Society
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    • 제16권2호
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    • pp.49-64
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    • 1991
  • In this paper, we present a stepwise design methodology for relational databases using a new conceptual data model, the MAO(Multiple Aspects-based Object) model. Our methodology consists two steps : first, data requirements are conceptualized using the MAO model with concepts such as objects types and aspect types : second, the MAO model is transformed into the third normal form inn the relational model supported by commercial DBMs. A top-down approach is used for the MAO modelling in the first, step, and the transformation process in the second step can be automated. Our methodology supports easier and more database design of real world than other methodologies using existing data models.

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