• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.89-98
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• 2013

This paper presents a complete survey of recent techniques that are applied in the field of real-time Java computing. It focuses on the issues that are especially important for hard real-time applications, which include time predictable garbage collection, worst-case execution time analysis of Java programs, real-time Java threads scheduling and compiler techniques designed for real-time purpose. It also evaluates experimental frameworks that can be used for researching real-time Java. This overview is expected to help researchers understand the state-of-the-art and advance the research in real-time Java computing.

• Journal of Korea Technology Innovation Society
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• v.4 no.3
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• pp.275-290
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• 2001

The frameworks for evaluation of national R&D programs reflect their various political and administrative cultures(Gibbons & Georghiou, 1987), and the structure of national R&D system. In this research the core research questions are : what is god evaluation\ulcorner What is being evaluated, by whom, by which criteria, for whom and what purpose\ulcorner In order to examine these general aims and answer these questions, in detail several objectives can be proposed on the process of this research. In this research, the national R&D programs will be considered in terms of the interface between evaluation and the wider policy-making process. The programs for case study are the Alvey program(UK), the Advanced Technology Program (US) and the Framework program(EU) . One of the characteristics of these programs is the well established evaluation systems they have. From the comparative analysis, we can withdraw some useful implication for Korean evaluation practice for national R&D programs. Evaluation system is social process and the way in which it is organized is critical to its outcome.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.8 s.179
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• pp.2091-2099
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• 2000

The object of the present study is to present an adaptive wavelet-Galerkin method for the analysis of thin-walled box beam. Due to good localization properties of wavelets, wavelet methods emerge as alternative efficient solution methods to finite element methods. Most structural applications of wavelets thus far are limited in fixed-scale, non-adaptive frameworks, but this is not an appropriate use of wavelets. On the other hand, the present work appears the first attempt of an adaptive wavelet-based Galerkin method in structural problems. To handle boundary conditions, a fictitous domain method with penalty terms is employed. The limitation of the fictitious domain method is also addressed.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.3
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• pp.375-382
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• 2011

The usages of CFD (Computational Fluid Dynamics) which is simulating turbulent flows in CSTRs (Complete Stirrer Tank Reactors) have been reported. Considering model strategies and simulation techniques, this paper is focused on the turbulence models. The results of this study would suggest multiple reference frameworks relevant to rotational flow simulation. Specifically, the analysis of turbulence dissipation rates referred to this study would solve the relevant environmental engineering problem and would be beneficial to the CFD in CSTRs using mechanical mixer.

• Korean Management Science Review
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• v.26 no.3
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• pp.101-115
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• 2009

In this paper, we propose a technology mining framework for mobile communication industry. We develop a two phase approach of new technology identification and service enhancement. The new technology identification process consists of R&D issues analysis, technology theme design, and emerging technology sampling. On the other hand, existing service enhancement process has technology landscaping, keyword based search, and technological growth analysis. By implementing these two phase frameworks, we develop a technology portfolio for mobile communication industry.

• Korean Journal of Computational Design and Engineering
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• v.15 no.6
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• pp.403-410
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• 2010

This paper is aimed at the development of a theoretical framework that addresses practical applications of generative design system that have been observed in architectural practice. Existing theoretical frameworks are not aimed at addressing this specific use of parametric tools but do provide a set of key themes. Based on these themes a generative design system is presented here as a means for tackling architectural design development tasks. This is then used in order to examine a case study; the generative design system tasks involved in the design development and documentation of the Olympic Stadium in Germany. The findings from this examination are used to discuss proposals and implications for a practical framework for generative design in architecture.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.74-82
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• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.457-463
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• 2019

The blockchain and crypto currency has become one of the most essential components of a communication network in the recent years. Through communication networking, we browse the internet, make VoIP phone calls, have video conferences and check e-mails via computers. A lot of researches are being conducting to address the blockchain and crypto currency challenges in communication networking and provide corresponding solutions. In this paper, a diverse kind of novel research works in terms of mechanisms, techniques, architectures, and frameworks have been proposed to provide possible solutions against the existing challenges in the communication networking. Such novel research works involve thermal load capacity techniques, intelligent sensing mechanism, secure cloud computing system communication algorithm for wearable healthcare systems, sentiment analysis, optimized resources.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.