• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.277-283
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• 1989

For the battery energy storage system (BESS), battery is one of most important parts. Various new type batteries for load shifting are under developing. The lead acid battery technology status such as structure, charge and discharge characteristics, life cycle etc. is reviewed and research trend is also introduced.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2008.05a
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• pp.197-198
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• 2008

• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.64-64
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.747-748
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2003.10a
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• pp.71-71
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• 2003

• Proceedings of the Materials Research Society of Korea Conference
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• 1998.08a
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• pp.101.4-101
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• 1998

• Progress in Superconductivity and Cryogenics
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• v.9 no.3
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• pp.62-66
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• 2007

Electromagnetic forces (Attractive and repulsive force), interacting between current leads show different tendency according to the arrangement of current leads on the top flange of the cryostat and the distance of each lead. Especially in case of high-current electric power devices or high-field magnets, optimal arrangement of current leads becomes one of the safety issues to be considered for minimizing the electromagnetic for ce acting on them. In this paper, we suggest an optimal arrangement method with three pairs of current leads for a 24kV class 650A superconducting fault current limiter (SFCL) system which has a probability of unpredicted fault currents(i.e, 20kA).

• Korean Journal of Materials Research
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• v.15 no.10
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• pp.639-643
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• 2005

Lead-free $[Bi_{0.5}(Na_{1-x}K_x)_{0.5}TiO_3](x=0\~1.0)$ ceramics were prepared using a solid state reaction method and their structural and electrical characteristics were investigated. X-ray investigations indicated that the rhombohedral-tetragonal morphotropic phase boundary(MPB) of the $[Bi_{0.5}(Na_{1-x}K_x)TiO_3$ ceramics exists in the range of $x=0.16\~0.20$. The optimum values of piezoelectric constant$(d_{33})$, dielectric constant, and electromechanical coupling factor $(k_p)$ were obtained at $x=0.16\~0.20$ of the MPB region.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.389-401
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• 2012

This paper derives a benchmark dose(BMD) and its 95% lower confidence limit(BMDL) using a semi-parametric regression model for small lead based changes in attention-deficit hyperactivity disorder(ADHD) scores in the first wave of the Children's Health and Environment Research(CHEER) survey data, which have been regularly collected in South Korea since 2005. Ha et al. (2009) showed that the appearance of ADHD symptoms had a borderline trend of increasing with the blood lead concentration. Butdz-J${\o}$rgensen (EFSA, 2010a) derived the BMDL of lead corresponding to a benchmark region of 1 full intelligent quotient (IQ) score using the raw data in Lanphear et al. (2005, EHP). European Food Safety Authority (EFSA, 2010b) determined the BMDL of $1.2{\mu}g/dl$ as a reference point for the characterization of lead when assessing the risk of the intellectual deficit measured by IQ scores. Kim et al. (2011) indicated that an even lower BMDL could be obtained based on the ADHD score; however, the BMDLs depended heavily upon the model assumptions. We show in this paper that a semi-parametric approach resolves the model dependence of BMDLs.

• 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).