• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.87-108
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• 2001

Recently, the user of mobile and cordless internet is growing rapidly. So the advertising methodologies are appeared on stage through the internet and mobile service. But the current advertisement(AD) service into mobile phones supplies a short-sentence AD using letter-message, and gives mobile phone users an inconvenience by making them first listen to and then check the AD. Until now the AD service using mobile has been on the beginning level. In this study, we suggest the new advertising methodologies and propose both target marketing strategies and demand forecast through the data mining techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1142-1149
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• 2003

Various organizational factors effect successful implementation of IT enabled business transformation. Among them, the most critical success factor is deemed to overcoming change management problem. Lots of studies have been made on Implementation methodologies and business process formalizations to encourage organizational members to accept new business process changes. However, the logic or process redesign still depends on qualitative problem solving techniques mostly depending on basically human intuition such as brainstorming. cause-and-effect analysis. and so on. In this paper, we focused on developing analytic framework to design to-be business process structure. which can complement qualitative problem solving procedures. With effective use of IT as an enabler, we provide algorithmic framework applicable to designing various business process changes such as process automation, business process resequencing, and more radical process integration. The framework follows dynamic programming approach in the literature, which is based on the decision making paradigm of organizations to abstract business processes as quantitative decision models. As such, our research ran fill the gap of limited development of theory based analytic methodologies for business process design, by providing objective rationale to reach the consensus among the organizational members including senior management.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.21-45
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• 2003

Various organizational factors effect successful implementation of IT enabled business transformation. Among them, the most critical success factor is deemed to overcoming change management problem. Lots of studies have been made on implementation methodologies and business process formalizations to encourage organizational members to accept new business process changes. However, the logic of process redesign still depends on qualitative problem solving techniques mostly depending on basically human intuition such as brainstorming, cause-and-effect analysis, and so on. In this paper, we develop algorithmic procedure applicable to designing various business process changes such as process automation, business process resequencing, and more radical process integration. The framework is employed from dynamic programming approach in the literature, which is based on the decision making paradigm of organizations to abstract business processes as quantitative decision models. As such, our research can fill the gap of limited development of theory based analytic methodologies for business process design, by providing objective rationale to reach the consensus among the organizational members including senior management.

• Survey Research
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• v.2 no.1
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• pp.87-108
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• 2001

Recently, the user of mobile and cordless internet is growing rapidly. So the advertising methodologies are appeared on stage through the internet and mobile service. But the current advertisement(AD) service into mobile phones supplies a short-sentence AD using letter-message, and gives mobile phone users an inconvenience by making them first listen to and then check the AD. Until now the AD service using mobile has been on the beginning level. In this study, we suggest the new advertising methodologies and propose both target marketing strategies and demand forecast through the data mining techniques.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.145-153
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• 2021

Data imputation is a crucial issue in data analysis because quality data are highly correlated with the performance of AI models. Particularly, it is difficult to collect quality time-series data for uncertain situations (for example, electricity blackout, delays for network conditions). Thus, it is necessary to research effective methods of time-series data imputation. Many studies on time-series data imputation can be divided into 5 parts, including statistical based, matrix-based, regression-based, deep learning (RNN and GAN) based methodologies. This study reviews and organizes these methodologies. Recently, deep learning-based imputation methods are developed and show excellent performance. However, it is associated to some computational problems that make it difficult to use in real-time system. Thus, the direction of future work is to develop low computational but high-performance imputation methods for application in the real field.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.307-313
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• 2024

This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.419-425
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• 2016

This study analyzed research methodologies based on network pharmacology to build a new system architecture optimized for Korean Medicine research. Results form studies using network pharmacology were collected and analyzed to evaluate the strength and weakness. Finally, an improved system architecture was proposed. Whether the predicted effects of drugs or herbs from network pharmacological analyses were in agreement with those from conventioanl knowledge of Korean Medicine was evaluated. These results were used to verify the applicability of research methodologies to the modern pharmacology and Korean Medicine respectively. Eighteen papers using TCMSP were collected and analyzed. The results suggest that the research methodology based on network pharmacology is appropriate only for the modern pharmacology but not for Korean Medicine. Information about compound-compound, herb-herb and drug-compound interactions need to be considered. We propose the modified system architecture with those information.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.4
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• pp.517-536
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• 2020

The radionuclide inventory in radioactive waste from nuclear power plants should be determined to secure the safety of final repositories. As an alternative to time-consuming, labor-intensive, and destructive radiochemical analysis, the indirect scaling factor (SF) method has been used to determine the concentrations of difficult-to-measure radionuclides. Despite its long history, the original SF methodology remains almost unchanged and now needs to be improved for advanced SF implementation. Intense public attention and interest have been strongly directed to the reliability of the procedures and data regarding repository safety since the first operation of the low- and intermediate-level radioactive waste disposal facility in Gyeongju, Korea. In this review, statistical methodologies for SF implementation are described and evaluated to achieve reasonable and advanced decision-making. The first part of this review begins with an overview of the current status of the scaling factor method and global experiences, including some specific statistical issues associated with SF implementation. In addition, this review aims to extend the applicability of SF to the characterization of large quantities of waste from the decommissioning of nuclear facilities.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.25-36
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• 2023

Nuclear power plants have recognized the importance of nuclear cybersecurity. Based on regulatory guidelines and security-related standards issued by regulatory agencies around the world including IAEA, NRC, and KINAC, nuclear operating organizations and related systems manufacturing organizations, design companies, and regulatory agencies are considering methods to prepare for nuclear cybersecurity. Cryptographic algorithms have to be developed and applied in order to meet nuclear cybersecurity requirements. This paper presents methodologies for validating cryptographic algorithms that should be continuously applied at the critical control system of I&C in NPPs. Through the proposed schemes, validation programs are developed in the PLC, which is a critical system of a NPP's I&C, and the validation program is verified through simulation results. Since the development of a cryptographic algorithm validation program for critical digital systems of NPPs has not been carried out, the methodologies proposed in this paper could provide guidelines for Cryptographic Module Validation Modeling for Control Systems in NPPs. In particular, among several CMVP, specific testing techniques for ECB mode-based block ciphers are introduced with program codes and validation models.

• Environmental Engineering Research
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• v.16 no.2
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• pp.69-79
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• 2011

Rapid population growth and land use changes have severely degraded streams across the United States. In response, there has been a surge in the number of stream restoration projects, including stream restoration for mitigation purposes. Currently, most projects do not include evaluation and monitoring, which are critical in the success of stream restoration projects. The goal of this study is to review the current status of assessment methodologies and restoration approaches for streams in Virginia, with the aim of assisting the restoration community in making sound decisions. As part of the study, stream restoration projects data from a project in Fairfax County, Virginia was assessed. This review revealed that the stream assessment methodologies currently applied to restoration are visuallybased and do not include biological data collection and/or a method to incorporate watershed information. It was found from the case study that out of the twenty nine restoration projects that had occurred between 1995 and 2003 in Fairfax County, nineteen projects reported bank stabilization as a goal or the only goal, indicating an emphasis on a single physical component rather than on the overall ecological integrity of streams. It also turned out that only seven projects conducted any level of monitoring as part of the restoration, confirming the lack of evaluation and monitoring. However, Fairfax County has recently improved its stream restoration practices by developing and incorporating watershed management plans. This now provides one of the better cases that might be looked upon by stakeholders when planning future stream restoration projects.