• Journal of the Society of Naval Architects of Korea
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• v.35 no.2
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• pp.20-28
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• 1998

To accurately predict the motion of a submergible, the nonlinear structure of dynamic model should be selected and corresponding parameters should be estimated using model test. Providing the model structure, only the values of parameters are unknown and the estimation can thus be formulated as a standard least square problem. Unfortunately, the nonlinear model structure of submersibles is rarely known a prior and method of model structure determination from measurement data of model test should be developed and included as a vital part of the estimation procedure. In this study, the well-known linear least square algorithm for the analysis of model tests and a way to measure the goodness are reviewed, and the identification algorithm based on an orthogonal decomposition method of Gram-Schmidt is extended to combine structure selection and maneuvering coefficients estimation in a very simple and efficient manner. Finally, the efficiency of this algorithm is verified by using simulation and applying to the analysis of model test of a submerged body. As a result, it was verified that this combined method might be very erective in selecting the structure of dynamic model estimating the maneuvering coefficients from measurement fiat of model test.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.11-23
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• 1996

본 연구에서는 선형회귀분석에서 Hadi와 Simonoff의 다중이상점 식별방법을 수정하여 새로운 알고리즘을 제시하였다. Hadi와 Simonoff의 알고리즘 첫 단계에서 이상점일 가능성이 없는 점들의 집합을 추출할 때 가장효과와 편승효과에 영향을 받을 수 있음으로, 이 첫 단계를 수정하였다. 우리는 잔차가 일정한 분산을 갖는 정규분포에 다르다는 가정하에서 잔차의 신뢰구간을 생각하고, 이 구간안에서 잔차의 MAD가 최소인 새로운 모형을 탐색하고, 이를 이상점일 가능성이 없는 점들의 집합을 추출하는데 일용하는 새로운 알로리즘을 제시하였다. 제시된 방법은 실제자료에서 다른 방법에 비해 효율적으로 이상점을 식별할 수 있었다.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.391-401
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• 2011

Consider a cluster, so called a 'son cluster', whose mean and variance is composed of the means and variances of both clusters called as a 'father cluster' and a 'mother cluster'. In this paper, a method for identifying each of three clusters is provided by modeling the relationship with father and mother clusters. Under the normal mixture model, the parameters are estimated via EM algorithm. We were able to overcome the problems of estimation using ECM approximation. Numerical examples show that our method can effectively identify the three clusters, so called a 'family of clusters'.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.387-397
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• 2013

This paper proposes an approach to big data analytics for phon call data. The analytical models for phon call data is composed of the PVPF (Parallel Variable-length Phrase Finding) algorithm for identifying verbal phrases of natural language and the word count algorithm for measuring the usage frequency of keywords. In the proposed model, we identify words using the PVPF algorithm, and measure the usage frequency of the identified words using word count algorithm in MapReduce. The results can be interpreted from various viewpoints. We design and implement the model based HDFS (Hadoop Distributed File System), verify the proposed approach through a case study of phon call data. So we extract useful results through analysis of keyword correlation and usage frequency.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2041-2049
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• 1998

This paper suggests a technique for program segmentation and maintenance using PSDG(Post-State Dependency Graph) that improves the quality of a software by identifying and detecting defects in already fixed source code. A program segmentation is performed by utilizing source code analysis which combines the measures of static, dynamic and semantic slicing when we need understandability of defect in programs for corrective maintanence. It provides users with a segmental principle to split a program by tracing state dependency of a source code with the graph, and clustering and highlighting, Through a modeling of the PSDG, elimination of ineffective program deadcode and generalization of related program segments arc possible, Additionally, it can be correlated with other design modeb as STD(State Transition Diagram), also be used as design documents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.511-517
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• 2006

In this paper, we studied the techniques to analyze the flying safety area focused on the air base rules for military that has been the criteria of the altitude restrictions around the airfield for both civilian and military purposes in Korea. We wanted to present the effective method to analyze the restricted area and to help solving problems that could result recently from the altitude restrictions around the airfield at the beginning of the development projects. To do this we proposed the methods to effectively generate the model of the flying safety area in accordance with the air base rules using 3D GIS techniques and to automatically identify the obstructions caused by the natural and man-made features in those areas. To apply the proposed methods actually to the airfield chosen for the study area, we presented the approaches to generate geospatial informations based on the commercial digital maps and satellite imagery and by generating the flying safety area model, identifying the obstructions, and visualizing the integrated model for the flying safety area analysis we showed the practical usability of the proposed techniques.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.139-156
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• 1992

Recently several authors have introduced iterative methods for detecting time series outliers. Most of these methods are developed under the assumption that an underlying outlier-free model is known or can be identified. Since outliers can distort model identification or even make it impossible, we propose procedure begins with a descriptive data analysis of a time series using distance measures between two observations. Properties of the proposed test statistic are presented. To distinguish the type of an outlier are used transfer function models. An empirical example is given to illustrate the time series modeling procedure.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2088-2097
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• 1999

We propose the software framework construction method that increases reusability through use case extraction and structuring of software system's dynamic behavior of which identifying behavior patterns from software domain models. Most behavior models do not provide a consistent modeling technique for harmonizing user's heterogeneous requirements, and not yet prepared to a detailed optimizing technique for redevelopment and maintenance. Therefore, we need a software reuse framework to support consistency and reusability of existing development models using use cases with functional characteristics. a lattice model is used to this approach for structuring use cases, and the reuse process that can be driven to reusable components is introduced in this paper,.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.205-223
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• 2021

In this paper, we study a method to determine the existence of unit roots by using the adaptive lasso. The previously proposed method that applied the adaptive lasso to the original time series has low power when there is an unknown trend. Therefore, we propose a modified version that fits the ADF regression model without deterministic component using the adaptive lasso to the detrended series instead of the original series. Our Monte Carlo simulation experiments show that the modified method improves the power over the original method and works well in large samples.

• 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.