• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.147-153
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• 2011

This paper proposes a methodology using a tool based on the branch-parameter continuation power flow (BCPF) in order to restore the power flow solvability in unsolvable contingencies. A specified contingency from a set of transmission line contingencies is modeled, considering the transient analysis and practice in the Korean power system. This tool traces a solution path that satisfies the power flow equations with respect to the variation of the branch parameter. At a critical point, in which the branch parameter can move on to a maximum value, a sensitivity analysis with a normal vector is performed to identify the most effective compensation. With the sensitivity information, the location of the reactive power compensation is determined and the effectiveness of the sensitivity information is verified to restore the solvability. In the simulation, the proposed framework is then applied to the Korean power system.

• Journal of Technology Innovation
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• v.8 no.1
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• pp.73-94
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• 2000

The main objective of this research is to examine the spillover effects of technological knowledge from IT industry to other industrial sectors and, based on empirical findings, to draw policy implications and suggest policy directions. To this end, we divide IT industry into IT equipment and IT service, assuming that these two sub-sectors are considerably different each other in terms of technology knowledge flow. Other industries are classified into 17 different sectors based on the KSIC of 1990. As the proxy measure of technological knowledge, the notion of R&D stock is employed. The Input/output(I/O) Table is used to define the inter-industrial flow pattern and to draw the knowledge flow matrix. As the research methodology, cost function model is employed to gauge the spillover effects of technological knowledge of IT industry. Based on the results of analysis, it is found that the economic impact of technology diffusion also exhibits a different pattern between IT equipment and IT service. The diffusion of IT equipment tends to show labor-substitution effect whereas IT service displays labor-creation effect. This fact should be considered in devising industry, education, and labor policy. The expectations from this research are as follows. First, the sectoral pattern, difference between IT equipment and service in particular, identified from this research may shed light on the sector-specific policy direction. It is emphasized that a sector-specific approach, rather than an aggregate approach, is relevant for formulating IT policy. Second, it is expected that the importance of technology diffusion programs and policy measures are recognized among policy makers in IT industry.

• Nuclear Engineering and Technology
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• v.33 no.2
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• pp.132-144
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• 2001

Fracture mechanics is one of the major areas of the pressurized thermal shock (PTS) evaluation. To evaluate the reactor pressure vessel integrity associated with PTS, PFM methodology demands precise calculation of temperature, stress, and stress intensity factor for the variety of PTS transients. However, the existence of stainless steel cladding, with different thermal, physical, and mechanical property, at the inner surface of reactor pressure vessel complicates the fracture mechanics analysis. In this paper, treatment schemes to evaluate stress and resulting stress intensity factor for RPV with stainless steel clad are introduced. For a reference transient, the effects of clad thermal conductivity and thermal expansion coefficients on deterministic fracture mechanics analysis are examined.

• Journal of Distribution Science
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• v.12 no.7
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• pp.5-12
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• 2014

Purpose - This study identified internationalization factors forKorean SMEs and explored factor differences between AEO and non-AEO authorized companies. Research design, data, and methodology - The study was designed to assess internationalization factors for AEO authorization in Korea through a questionnaire survey and an empirical analysis. The questionnaires were conducted for AEO and Non-AEO authorized companies that were undergoing AEO authorization. The study was conducted through e-mail and AEO manager education classes. Ninety-five questionnaires were collected. We employed the exploratory factor analysis methodology to derive internationalization factors for KoreanSMEs, and explored the factor differences between AEO and Non-AEO authorized companies. Results - AEO authorized companies outperformed Non-AEO authorized companies in R&D and technology. This indicated that AEO authorized companies were recognized as reliable and safe companies by the Korea Customs Service and other Customs services in trade facilitation and customs clearance processes. Conclusions - This study has some implications for AEO authorization and internationalization processes, and involved the empirical analysis of SMEs and the exploratory factor analysis in the internationalization process.

• Genomics & Informatics
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• v.12 no.1
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• pp.21-34
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• 2014

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.

• Journal of Distribution Science
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• v.22 no.6
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• pp.107-114
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• 2024

Purpose: The current research investigates historical and future trends of tourist distribution channels in the tourism services business. The research examines historical patterns, current shifts, and new technologies in electricity distribution to offer insight into the distribution dynamics and advice for companies and regulators. Research design, data and methodology: The research in this case specifically employed the PRISMA approach when it comes to the data collection and research methodology. (PRISMA). The process is specifically made up of four steps, such as (1) Identification of Relevant Studies, (2) Screening and Selection Procedures, (3) Data Synthesis and Analysis, and (4) Reporting of Findings. Results: The fast-changing technology offers all opportunities to innovate the sector of tourism services. These upcoming technologies are not just reconstructing the way customers interact and operate but they are also creating room for development. Besides "the utilization of new technologies such as artificial intelligence, augmented reality, virtual reality, and blockchain, the current state of tourism distribution channels also implies some other possible consequences. Conclusions: These research results show that we should not be reluctant about adopting new technologies, we should expand direct booking systems, promote eco-friendly tourism, and use data analytics in order to provide personalized experiences.

• Journal of Distribution Science
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• v.22 no.4
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• pp.23-35
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• 2024

This study investigates the organizational factors including firm size, adaptive capability, absorptive capability, innovative capability, and executive support to determine internet of things, production and services, and organizational performance. Research design, data, and methodology: A quantitative methodology was employed, involving the distribution of surveys to 460 employees occupying managerial and strategic roles. These individuals have accrued a minimum of one year of experience within 20 leading manufacturing and distribution companies in Thailand, each boasting a workforce exceeding 250 employees. Sampling techniques utilized encompass judgmental, quota, and snowball sampling. Furthermore, analysis of the data was conducted through Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The findings indicate that factors such as firm size, adaptive capability, absorptive capability, and innovative capability exert significant influence on the Internet of Things (IoT). In addition, IoT significantly impacts both production and services. Furthermore, the study highlights the significant influence of production and services on organizational performance. However, the anticipated relationship between executive support and IoT lacks support according to the results. Conclusions: This study highlights the transformative potential of IoT for the manufacturing and distribution sector, paving the way for enhanced efficiency, competitiveness, and sustainability in a rapidly evolving business landscape.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.487-496
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• 2021

The analysis method of the microbiome has been evolving for a very long time, and the industrial field has grown rapidly with the start of human genome analysis 20 years ago. As continuous research continues, related industries have grown together, and among them, Illumina of the US has been leading the popularization of DNA analysis by developing innovative equipment and analysis methods since its establishment in 1998. In this paper, 'AiB Index', 'AiB Chart' using statistical process control and log-scale technique to analyze the gut microbiome analysis methodology and implement an algorithm that can analyze minute changes in the minor strains that can be overlooked in the existing analysis methods. want to implement. From the data analysis point of view, we proposed a platform for analyzing gut microbes that can collect fecal data, match and process gut microbes, and store and visualize the results.