• Computers and Concrete
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• v.19 no.2
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• pp.143-152
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• 2017

This paper has numerically investigated the changes of loading-induced stress in concrete with the corrosion time in the sulfate-containing environment. Firstly, based on Fick's law and reaction kinetics, a diffusion-reaction equation of sulfate ion in concrete is proposed, and it is numerically solved to obtain the spatial and temporal distribution of sulfate ion concentration in concrete by the finite difference method. Secondly, by fitting the existed experimental data of concrete in sodium sulfate solutions, the chemical damage of concrete associated with sulfate ion concentration and corrosion time is quantitatively presented. Thirdly, depending on the plastic-damage mechanics, while considering the influence of sulfate attack on concrete properties, a simplified chemo-mechanical damage model, with stress-based plasticity and strain-driven damage, for concrete under axial loading and sulfate attack is determined by introducing the chemical damage degree. Finally, an axially compressed concrete prism immersed into the sodium sulfate solution is regarded as an object to investigate the time-varying stress in concrete subjected to the couplings of axial loading and sulfate attack.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.817-825
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• 2022

A recent benchmarking effort, under the auspices of the Organization for Economic Cooperation and Development (OECD) Nuclear Energy Agency (NEA), has been made to evaluate the current state of modeling and simulation tools available to model fluoride salt-cooled high temperature reactors (FHRs). The FHR benchmarking effort considered in this work consists of several cases evaluating the neutronic parameters of a 2D prismatic FHR fuel assembly model using the participants' choice of simulation tools. Benchmark participants blindly submitted results for comparison with overall good agreement, except for some which significantly differed on cases utilizing a molybdenum-bearing control rod. Participants utilizing more recently updated explicit isotopic cross sections had consistent results, whereas those using elemental molybdenum cross sections observed reactivity differences on the order of thousands of pcm relative to their peers. Through a series of supporting tests, the authors attribute the differences as being nuclear data driven from using older legacy elemental molybdenum cross sections. Quantitative analysis is conducted on the control rod to identify spectral, reaction rate, and cross section phenomena responsible for the observed differences. Results confirm the observed differences are attributable to the use of elemental cross sections which overestimate the reaction rates in strong resonance channels.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Ocean and Polar Research
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• v.44 no.4
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• pp.269-285
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• 2022

The interaction between ocean and ice shelf is a critical physical process in relation to water mass transformations and ice shelf melting/freezing at the ocean-ice interface. However, it remains challenging to thoroughly understand the process due to a lack of observational data with respect to ice shelf cavities. This is the first study to simulate the variability and circulation of water mass both overlying the continental shelf and underneath an ice shelf and an ice tongue in the Terra Nova Bay (TNB), East Antarctica. To explore the properties of water mass and circulation patterns in the TNB and the corresponding effects on sub ice shelf basal melting, we explicitly incorporate the dynamic-thermodynamic processes acting on the ice shelf in the Regional Ocean Modeling System. The simulated water mass formation and circulation in the TNB region agree well with previous studies. The model results show that the TNB circulation is dominated by the geostrophic currents driven by lateral density gradients induced by the releasing of brine or freshwater at the polynya of the TNB. Meanwhile, the circulation dynamics in the cavity under the Nansen Ice shelf (NIS) are different from those in the TNB. The gravity-driven bottom current induced by High Salinity Shelf Water (HSSW) formed at the TNB polynya flows towards the grounding line, and the buoyance-driven flow associated with glacial meltwater generated by the HSSW emerges from the cavity along the ice base. Both current systems compose the thermohaline overturning circulation in the NIS cavity. This study estimates the NIS basal melting rate to be 0.98 m/a, which is comparable to the previously observed melt rate. However, the melting rate shows a significant variation in space and time.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.4
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• pp.15-25
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• 2012

The research on the FPGA CAD tools in academia has been lacking practicality due to the underlying FPGA fabric architecture which is too simple and inefficient to be applied for commercial FPGAs. Recently, the database of placement positions and routing graphs on commercial FPGA architectures has been built, and provided for enabling the academic development of placement and routing tools. To extend the limit of academic CAD tools even further, we have developed the evaluation toolkit for the K-FPGA architecture which is under development. By providing interface for exchanging data with a commercial FPGA toolkit at every step of mapping, packing, placement and routing in the tool chain, the toolkit enables individual tools to be developed without waiting for the results of the preceding step, and with no dependency on the quality of the results, and compared in detail with commercial tools at any step. Also, the fabric primitive library is developed by extracting the prototype from a reporting file of a commercial FPGA, restructuring it, and modeling the behavior of basic gates. This library can be used as the benchmarking target, and a reference design for new FPGA architectures. Since the architecture is described in a standard HDL which is familiar with hardware designers, and read in the tools rather than hard coded, the tools are "data-driven", and tolerable with the architectural changes due to the design space exploration. The experiments confirm that the developed library is correct, and the functional correctness of applications implemented on the FPGA fabric can be validated by simulation. The placement and routing tools are under development. The completion of the toolkit will enable the development of practical FPGA architectures which, in return, will synergically animate the research on optimization CAD tools.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.213-215
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• 2006

This study presents an application of GIS technologies to construct the graphic user interface for 3-dimensional exhibition of the results obtained by ocean hydrodynamic model. In coastal management studies, GIS provide a receptacle for scattered data from diverse sources and an improvement of the 3D visualization of such data. Within the frame of a GIS a variety of analytical, statistical and modeling tools can be applied to transform data and make them suitable for a given application. A 3D hydrodynamic model was driven by time-dependent external forcing such as tide, wind velocity, temperature, salinity, river discharge, and solar radiation under the open boundary condition. The Jinhae Bay was selected as a case study. Here, we have used GeoMania v2.5 GIS software and its 3D Analyst extension module to visualize hydrodynamic model result that were simulated around the Jinhae Bay.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.99-107
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• 2004

This study presents an application of GIS technologies to construct the graphic user interface for 3-dimensional exhibition of the results obtained by ocean hydrodynamic model. In coastal management studies, GIS provide a receptacle for scattered data from diverse sources and an improvement of the 3D visualization of such data. Within the frame of a GIS a variety of analytical, statistical and modeling tools can be applied to transform data and make them suitable for a given application. A 3D hydrodynamic model was driven by time-dependent external forcing such as tide, wind velocity, temperature. salinity, river discharge, and solar radiation under the open boundary condition. The Jinhae bay was selected as a case study. Here, we have used GeoMania v2.5 GIS software and its 3D Analyst extension module to visualize hydrodynamic model result that were simulated around the Jinhae bay.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• Asia Pacific Journal of Business Review
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• v.6 no.2
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• pp.27-60
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• 2022

Previous studies stated that low carbon innovation performance could be influenced by government regulations and the green market, which is the new trend of consumer consumption in the present time, mainly focusing on external factors. Before study augured that low carbon innovation performance could be driven by internal and external factors of cooperation such as institutional pressure, stakeholder pressure, and innovation resources. However, the study of green transformational leadership and organizational environmental culture on low carbon innovation performance is rare, especially in Chinese manufacturing, as well as the effect of influencing factors of TPB model: environmental attitude, subjective norm, and perceived behavior capability on low carbon innovation performance. Previous studies mostly used the TPB model for predicting individual behavior. This study established a theoretical model combining the TPB model with green transformational leadership and organizational environmental culture of Chinese automobile manufacturing on low carbon innovation performance. This study consists of two sections of research methodology: section 1 related to questionnaire design and data collection. We established a questionnaire and distributed it online, targeting responses from the managerial level working in Chinese automobile manufacturing. Eventually, 155 valid questionnaires were used for analysis. Section 2 involved data analysis using statistical software. Reliability and data validity was examined by reliability analysis and factor analysis. Correlations and convergent validity analyses were applied, and structural equation modeling was conducted to test the proposed hypotheses. The findings indicated that green transformational leadership, organizational environmental culture, and essential factors of TPB model; environmental attitude, subjective norm and perceived behavior capability positively affect low carbon innovation performance. In addition, the indirect effect of green transformational leadership was tested and found that organizational environmental culture and TPB factors mediated the relationship between transformational leadership and low carbon innovation performance.