• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.133-142
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• 2023

In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of "deterministic non-periodic flow", and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.

• Journal of the Korean Solar Energy Society
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• v.23 no.4
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• pp.37-43
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• 2003

The aim of this research is to study the feasibility of the solar(hot fluid) driven $NH_3/H_2O$ absorption chiller, made by re-manufacturing of Gas fired $NH_3/H_2O$ absorption chiller. This experimental study is performed with the temperature of the inlet hot fluid of generator. In order to determine the inlet temperature of the generator, which gives maximum COP, the experimental data are obtained with various hot fluid supply temperature in range of $130\sim170^{\circ}C$. Remodeled chiller is operated with periodical cooling effect, which due to mixture subcooled pool boiling, then the COP is evaluated in average. The maximum COP$(\sim0.36)$ is at $160^{\circ}C$. The temperature is stable operation temperature range of typical vacuum collector. It offers a feasibility of solar driven $NH_3/H_2O$ absorption chiller.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2002.04b
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• pp.19-25
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• 2002

Biotechnology in the 21st century will be driven by three emerging technologies: genomics, high-throughput biology, and bioinformatics. These technologies are complementary to one another. A large number of economically important crops are currently subjected to whole genome sequencing. Functional genomics for determining the functions of the genes comprising the given plant genome is under progress by using various means including phenotyping data from transgenic mutants, gene expression profiling data from DNA microarrays, and metabolic profiling data from LC/mass analysis. The aim of plant molecular breeding is shifting from introducing agronomic traits such as herbicide and insect resistance to introducing quality traits such as healthful oils and proteins, which will lead to improved and nutritional food and feed products. Plant molecular breeding is also expected to aim to develop crops for producing human therapeutic and industrial proteins.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2002.04a
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• pp.19-25
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• 2002

Biotechnology in the 21st century will be driven by three emerging technologies: genomics, high-throughput biology, and bioinformatics. These technologies are complementary to one another. A large number of economically important crops are currently subjected to whole genome sequencing. Functional genomics for determining the functions of the genes comprising the given plant genome is under progress by using various means including phenotyping data from transgenic mutants, gene expression profiling data from DNA microarrays, and metabolic profiling data from LC/mass analysis. The aim of plant molecular breeding is shifting from introducing agronomic traits such as herbicide and insect resistance to introducing quality traits such as healthful oils and proteins, which will lead to improved and nutritional food and feed products. Plant molecular breeding is also expected to aim to develop crops for producing human therapeutic and industrial proteins.

• Journal of Plant Biotechnology
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• v.29 no.3
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• pp.145-150
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• 2002

Biotechnology in the 21st century will be driven by three emerging technologies: genomics, high-throughput biology, and bioinformatics. These technologies are complementary to one another. A large number of economically important crops are currently subjected to whole genome sequencing. Functional genomics for determining the functions of the genes comprising the given plant genome is under progress by using various means including phenotyping data from transgenic mutants, gene expression profiling data from DNA microarrays, and metabolic profiling data from LC/mass analysis. The aim of plant molecular breeding is shifting from introducing agronomic traits such as herbicide and insect resistance to introducing quality traits such as healthful oils and proteins, which will lead to improved and nutritional food and feed products. Plant molecular breeding is also expected to aim to develop crops for producing human therapeutic and industrial proteins.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.181-184
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• 2008

The quality of orthoimages mainly depends on the elevation information and exterior orientation (EO) parameters. Since LiDAR data directly provides the elevation information over the earth's surface including buildings and trees, the concept of true orthorectification has been rapidly developed and implemented. If a LiDAR-driven digital surface model (DSM) is used for orthorectification, the displacements caused by trees and buildings are effectively removed when compared with the conventional orthoimages processed with a digital elevation model (DEM). This study sequentially utilized LiDAR data to generate orthorectified digital aerial images. Experimental orthoimages were produced using DTM and DSM. For the preparation of orthorectification, EO components, one of the inputs for orthorectification, were adjusted with the ground control points (GCPs) collected from the LiDAR point data, and the ground points were extracted by a filtering method. The orthoimage generated by DSM corresponded more closely to non-ground LiDAR points than the orthoimage produced by DTM.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.