• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.185-188
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• 2010

온톨로지 간 이질성 문제를 해결하고 상호운용성을 향상시키기 위한 연구가 진행되어 왔으며, 최근 XMDR에 기반한 온톨로지 구축 방법이 제안되었으나 기존 연구와의 비교 평가가 부족하여 장점을 정확하게 보이지 못하였다. 따라서 이 논문에서는 XMDR 기반 온톨로지 구축 방법의 장점을 보다 명확하게 보이기 위해 정량적인 평가를 수행한다. 이를 위해 실제 온톨로지를 구축하고, 구축된 온톨로지는 온톨로지 참조 기반 온톨로지 구축 방법, 사전 참조 기반 온톨로지 구축 방법, 기존 방법론을 이용한 온톨로지 구축 방법을 평가 대상으로 하여 5가지 평가 지표로 분석된다. 평가 지표로는 구축된 온톨로지의 어휘 및 구조의 일관성 비교를 위하여 어휘 및 구조의 빈도수 평균과 엔트로피를 사용하고 구축 비용의 평가를 위하여 각 온톨로지의 구축 시간을 사용한다. 이러한 실험 및 평가의 결과로써, 온톨로지 참조 기반의 온톨로지 구축 방법은 다른 온톨로지 구축 방법들에 비해 온톨로지 어휘 및 구조가 일관적이고 효율적임을 보인다.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.737-746
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• 2013

The Bayesian method can be applied successfully to the estimation of the censored regression model introduced by Tobin (1958). The Bayes estimates show improvements over the maximum likelihood estimate; however, the performance of the Bayesian interval estimation is questionable. In Bayesian paradigm, the prior distribution usually reflects personal beliefs about the parameters. Such subjective priors will typically yield interval estimators with poor frequentist properties; however, an objective noninformative often yields a Bayesian procedure with good frequentist properties. We examine the performance of frequentist properties of noninformative priors for the Tobit regression model.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.108-115
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• 2001

In this paper we are attempted to quantitative classification of the three object color regions on a RGB image using of an improved ML(Maximum Likelihood) classification method. A RGB color image consists of three bands i.e., red, green and blue. Therefore it has a 3 dimensional structure in view of the spectral and spatial elements. The 3D structural yokels were projected in RGB cube wherefrom the ML method applied. Between the conventionally and easily usable Box classification and the statistical ML classification based on Bayesian decision theory, we compared and reviewed. Using the ML method we obtained a good segmentation result to classify positive cell nucleus, negative cell Nucleus and background un a immuno-histological breast carcinoma image. Hopefully it is available to diagnosis and prognosis for cancer patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.556-563
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• 2008

One of the most important issues of managing a supply chain is to determine the inventory level whenever shortage is permitted and vendor is responsible fur management of the both buyer and supplier's inventory. We present two vendor managed inventory models in the form of two-echelon supply chain models for: 1) one buyer-one supplier problem, and 2) two buyers- one supplier problem. We assume that shortage is permitted. The proposed methods of this paper provides a simple condition, which makes it easy to decide when and how vendor managed inventory model costs less than traditional one. The paper is supported with some numerical examples to show the implementation of the proposed methods.

• Journal of Energy Engineering
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• v.2 no.1
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• pp.45-53
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• 1993

This study was directed to an inquiry into a methodology for prioritizing and evaluating R & D alternatives for fuel cell technology, that can provide information for use in future decisions under the current uncertainty. A case study was performed for three cases of fuel cell development under the assumption that basic input data are same. The three cases are the case considering sequential R & D schedule only(Case 1), the case considering equivalent and excluding subprojects(Case 2), and the case allowing parallel efforts for each phase(Case 3). The following results were obtained; the probabilities of success for R & D phases in parallel projects are correlated, the probability of success for each project increases through Case 1, Case 2 and Case 3 successively and the expected dates of commercialization were notably shortened in Case 3.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.923-929
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• 2008

Due to the increasing need for 3D spatial data, modeling of topography and artificial structures plays an important role in three-dimensional Urban Analysis. This study suggests a methodology for solving the problem of calculation for the extraction of building boundary, minimizing the user's intervention, and automatically extracting building boundary, using the LIDAR data. The methodology suggested in this study is characterized by combining the merits of the point-based process and the image-based process. The procedures for extracting building boundary are three steps: 1) LIDAR point data are interpolated to extract approximately building region. 2) LIDAR point data are triangulated in each individual building area. 3) Extracted boundary of each building is then simplified in consideration of its area, minimum length of building.The performance of the developed methodology is evaluated using real LIDAR data. Through the experiment, the extracted building boundaries are compared with digital map.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.12 no.1
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• pp.19-35
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• 2014

A parametric sensitivity to the annual exposure dose rate to the farming exposure group has been probabilistically carried out for three principal elements associated with the nuclide transport behavior in the near-field of the pyroprocessed waste repository system. Credit time for both metal and ceramic containers, annual nuclide release rete, and the degree of loss of bentonite buffer around the container are selected as the elements and investigated for important nuclides. All the elements are shown to be sensitive to the results. Methodology studied through this study and the results are expected to make a good feedback to the repository design. As a follow-up study, separated in Part 2, the A-KRS will be deterministically assessed and then compared among each other with the normal, the worst, and the best case scenarios associated with their extreme values these elements could have.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.495-509
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• 2015

We study a classification problem of significant differences in the proportion of two groups known as the unbalanced classification problem. It is usually more difficult to classify classes accurately in unbalanced data than balanced data. Most observations are likely to be classified to the bigger group if we apply classification methods to the unbalanced data because it can minimize the misclassification loss. However, this smaller group is misclassified as the larger group problem that can cause a bigger loss in most real applications. We compare several classification methods for the unbalanced data using sampling techniques (up and down sampling). We also check the total loss of different classification methods when the asymmetric loss is applied to simulated and real data. We use the misclassification rate, G-mean, ROC and AUC (area under the curve) for the performance comparison.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.163-172
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• 2020

Recently, concepts of the Fourth Industrial Revolution technologies such as artificial intelligence, big data, and cloud computing have been introduced and the limits of individual or team development policies are being reviewed. Also, a lot of latest technology source codes have been opened to the public, and related studies are being conducted based on them. Meanwhile, the company is applying the strengths of the open source software development methodology to proprietary software development, and publicly announcing support for open source development methodology. In this paper, we introduced several software development methodology such as open source model, inner source model, and the similar DevOps model, which have been actively discussed recently, and compared their characteristics and components. Rather than claiming the excellence of a specific model, we argue that if the software development policy of an individual or affiliated organization is established according to each benefit, they will be able to achieve software quality improvement while satisfying customer requirements.

• Land and Housing Review
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• v.14 no.3
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• pp.145-155
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• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.