• Human Ecology Research
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• v.55 no.1
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• pp.13-26
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• 2017

This study investigated the latent groups depending on married working women's work-family spillover. The effects of factors that determine mental health subgroups and differences were also analyzed. Mixture modeling was applied to the Korean Longitudinal Survey of Women & Families to achieve the research objectives. The major findings of this study were as follows. First, there were four subgroups that could be defined according to the work-family spillover: mid-level spillover group (mid-positive and mid-negative spillover group), high-level spillover group (high-positive and high-negative spillover group), low-level spillover group (low-positive and low-negative spillover group), and high-negative and low-positive spillover group. Second, the results of mixture regression analysis to test the effect of eco-system variables showed that age, academic background, non-traditional family value, number of children, work hours, wage income, and availability of the maternity leave were significant determinants of the latent groups. The probability of classifying in the high-negative and low-positive spillover group increased when women showed a lower academic background and wage income, higher number of children and older age, and longer work hours than others. Third, the high-level spillover group, and the high-level spillover group showed the lowest stress and the lowest depression; however, the low-level spillover group reported the highest stress and the highest depression. Implications, limitations, and future directions were discussed based on the results.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.206-206
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• 1999

• Journal of Environmental Science International
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• v.14 no.9
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• pp.835-842
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• 2005

The three-dimensional eco-hydrodynamic model was applied to estimate the physical process in terms of COD (chemical oxygen demand) and net supply(or decomposition) rate of COD in Kamak Bay to find proper management plan for oxygen demanding organic matters. The estimation results of the physical process in terms of COD showed that transportation of COD is dominant in surface level while accumulation of COD is dominant in bottom level. In the case of surface level, the net supply rate of COD was 0 -0.50 mg/m2/day. The net decomposition rate of COD was 0 -0.04 mg/m2/day in middle level(3 -6m) and 0.05 -0.1 5 mg/m2/day in bottom level(6m -bottom). These results indicates that the biological decomposition and physical accumulation of COD are occurred predominantly at the northern part of bottom level. Therefore, it is important to consider both allochthonous and autochthonous oxygen demanding organic matters in the region.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.313-324
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• 2016

As building information modeling (BIM) is expanding its influence in various fields of architecture, engineering, construction and facility management (AEC-FM) industry, BIM-based automated code compliance checking has become possible prospects. For the automated code compliance checking, requirements in building code need to be processed into explicit representation that enables automated reasoning. This paper aims to develop high-level methods that translate verb phrases into explicit representation. The high-level methods represent conditions, properties, and related actions of the building objects and clarify the core content of the constraints. The authors analyze building permit requirements in Korea Building Code and establish a standardized process of deriving the high-level methods. As a result, 60 kinds of the high-level methods were derived. In addition, method classification, analysis, and application are introduced. This study will contribute to the representation of explicit building code sentences and establishment of the automated building permit system of Korea.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.12
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• pp.663-672
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• 2017

Building information modeling (BIM) technology based on 3D modeling has been applied to the entire domestic construction industry since 2010. It can calculate quantity take-off considering construction productivity at design phase. Based on this, it is possible to improve the reliability of construction cost prediction of design phase in the process of cost estimation. However, Building Information Level (BIL) defined by Ministry of Land, Infrastructure and Transport and Public Procurement Service does not seem to offer doable environment due to the lack of detailed application items. By calculating construction cost that meets Construction Cost Estimate Accuracy by American Association of Cost Engineers (AACE) through quantity take-off and cost estimation based on 3D modeling of BIM technology, a BIL improvement proposal at design phase for Mechanical Contracts and Construction is provided here. Results showed that properties including outline and minimum specification of the main equipment, internal main piping, and internal main duct should be defined from the intermediate design phase to have reliable cost estimation.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.109-112
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• 2001

In this paper, I present a MPEG4 decoding system modeling in SystemC, a new C/C++ based system simulation approach, In the modeling, MPEG4 decoding behavior is modeled and verified. And I partitions the MPEG4 decoding system into several hardware components which will be implemented at low level hardware design flow and I model a synchronized hardware block communication through data ports.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.426-433
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• 2017

This paper describes the development process, the innovative techniques used and insights gained from the latest integrated, full scope, multistate Level 2 PSA analysis conducted at the Leibstadt Nuclear Power Plant (KKL), Switzerland. KKL is a modern single-unit General Electric Boiling Water Reactor (BWR/6) with Mark III Containment, and a power output of $3600MW_{th}/1200MW_e$, the highest among the five operating reactors in Switzerland. A Level 2 Probabilistic Safety Assessment (PSA) analyses accident phenomena in nuclear power plants, identifies ways in which radioactive releases from plants can occur and estimates release pathways, magnitude and frequency. This paper attempts to give an overview of the advanced modeling techniques that have been developed and implemented for the recent KKL Level 2 PSA update, with the aim of systematizing the analysis and modeling processes, as well as complying with the relatively prescriptive Swiss requirements for PSA. The analysis provides significant insights into the absolute and relative importances of risk contributors and accident prevention and mitigation measures. Thanks to several newly developed techniques and an integrated approach, the KKL Level 2 PSA report exhibits a high degree of reviewability and maintainability, and transparently highlights the most important risk contributors to Large Early Release Frequency (LERF) with respect to initiating events, components, operator actions or seismic component failure probabilities (fragilities).