• Korean Association of Arts Management
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• no.53
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• pp.207-247
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• 2020

is a project for the senior generation with humanistic knowledge to become a mentor and communicate with them to present the wisdom and direction of life to the new generations of mentees based on various life experiences. has been expanding since 2015, starting with the pilot operation in 2014. In general, projects such as these are assessed to establish effectiveness indicators to verify effectiveness and to establish project management and development strategies. However, most of the evaluations have been conducted quantitatively and qualitatively based on the short-term duration of the project. Therefore, in the case of continuous projects such as , especially in the field of culture and arts where long-term effectiveness verification is required, the short-term evaluation is difficult to predict and judge the actual meaningful effects. In this regard, tried to examine the qualitative change of key participants in this project through the 2017 and 2018 image tracking survey. For this purpose, we adopted qualitative research methodology through interview video shooting, field shooting, and value coding as a research method suitable for the research subject. To analyze the results, first, the interview images were transcribed, keywords were extracted, value encoding works were matched with human psychological values, and the theoretical method was used to identify changes and to derive the meaning. In fact, despite the fact that the study conducted in this study was a follow-up survey, it remained a limitation that it analyzed the changed pattern in a rather short time of 2 years. However, this study systemized the specific methodology that researchers should conduct for follow-up and provided the flow of research at the present time when there is hardly a model for follow-up in the field of culture and arts education business in Korea as well as abroad. Significance can be derived from this point. In addition, it can be said that it has great significance in preparing the detailed system and case of comparative analysis methodology through value coding.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.30-36
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• 2022

In commercial transactions of energy sources using hydrogen charging stations, high-accuracy flow meters are needed to prevent accidents such as overcharging due to inaccurate measurements and to ensure transparency in hydrogen commercial transactions through accurate measurements. This research developed a Corioli-type flowmeter prototype and conducted a risk assessment to prevent accidents during a process change comparison experiment for existing charging stations to verify the measurement performance. A process change section was defined for the installation of measurement facilities for empirical experiments and HAZOP was conducted. In addition, JSA was also conducted to secure the safety of experimenters, such as preventing valve mis-opening during empirical experiments. Measures were established to improve the risk factors derived through HAZOP, and work procedures were established to minimize human errors and ensure the safety of workers through JSA. The design change and system manufacturing for the installation of the metering system were completed by reflecting the risk assessment results, and safety could be confirmed through the performance comparison test of the developed meter prototype. The developed prototype flow meter showed a total of 30 flow measurements under the operating conditions of 70 MPa, and the average error was -1.58% to 3.96%. Such a metering error was analyzed to have the same performance as a flow meter installed and operated for commercial use.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.25-37
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• 2022

Climate change is becoming increasingly unpredictable. This has led to changes in various systems such as ecosystems, human life and hydrological cycles. In particular, the recent unpredictable climate change frequently causes extreme droughts and torrential rains, resulting in complex water resources disasters that cause water pollution due to inundation and retirement rather than primary disasters. SWAT was used as a watershed model to analyze future runoff and pollutant loads. The climate scenario analyzed the RCP4.5 climate scenario of the Meteorological Agency standard scenario (HadGEM3-RA) using the normal quantitative mapping method. Runoff and pollutant load analysis were performed by linkage simulation of climate scenario and watershed model. Finally, the results of application and verification of linkage model and analysis of future water quality change due to climate change were presented. In this study, we simulated climate change scenarios using artificial neural networks, analyzed changes in water temperature and turbidity, and compared the results of dams with artificial neural network results through W2 model, a reservoir water quality model. The results of this study suggest the possibility of applying the nonlinearity and simplicity of neural network model to Hapcheon dam water quality prediction using climate change.

• Journal of Mushroom
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• v.20 no.1
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• pp.22-28
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• 2022

In order to provide basic data for preparing management standards and to verify the safety of the Chinese oak mushroom-derived moisturizing medium-which is synthesized and imported in large quantities-the presence of 321 residual pesticides, 7 heavy metals, and 3 radioactive materials was analyzed in the moisturizer samples. Examination of residual pesticides in seven moisturizing medium samples prepared using the Chinese oak mushroom and three domestic sawdust samples used for mushroom culture revealed the presence of cypermethrin and iprodione in three moisturizer samples, but the contents of these pesticides were below the standard limits. Zn was detected in ten samples, Cu was detected in nine samples, and Ni was detected in four samples, but their contents were below the standard limits. Pb, Cd, Cr, and Hg were not detected in any sample. No radioactive materials were detected in the samples. In addition, fruiting bodies of the oak mushroom were observed in each medium. Examination did not reveal the presence of any residual pesticides or harmful compounds. In this study, the use of the moisturizing medium prepared using the Chinese oak mushroom was found to be safe. As residual pesticides, heavy metals, and radioactivity-even in trace amounts-remain concentrated in the human body, continuous verification of the safety of hazardous substances and pollutants during the systematic cultivation and management of these mushrooms is required.

• Journal of the Korean Society of Floral Art and Design
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• no.37
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• pp.33-48
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• 2017

This study examines garden activity and garden plant preferences for development of the garden activity program using community garden aimed for communication and integration for multicultural age. As a result, for members of multicultural society had high portion of floral arrangement and crafts, growing plants in both indoors and outdoors garden for their garden activities, and using plants for cooking, growing plants in both indoors and outdoors garden was of higher portion for native korean. In the garden plant preferences, members of multicultural society liked ornamental plants the best, due to the environmental correspondence between the plant's place of origin and their home country, while native koreans tend to prefer vegetables, reflecting the recent interest in pro-environment crops and rise in demand of urban farming, veranda gardening and weekend farming. In this study, the garden activity program for communication and integration categorized the value of garden activity into three categories; the value of respect for life, the value of consideration through caring, the value of plant ethics, based on the above preference results. The value of respect for life can be achieved by understanding the meaning of life, experiencing the will to live, and understanding the characteristics of plants and me. The value of consideration of caring comes from waiting and nurturing for living things that are different from me and adapting to the environment as a living The value of plant ethics can give us the insights for human relationships, by understanding and experiencing the natural ecosystem and plant co-existing in it. The eight-session garden activity program also went through validity verification process by experts on gardening and multiculture, and the effectiveness of the program was proved.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.21-27
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• 2022

Flexible pressure sensor was developed using low-cost conductive graphite as printed electronics. Flexible pressure sensors are attracting attention as materials to be used in future industries such as medical, games, and AI. As a result of evaluating various electromechanical properties of the printed electrode for flexible pressure sensors, it showed a constant resistance change rate in a maximum tensile rate of 20%, 30° tension/bending, and a simple pulse test. A more appropriate matrix pattern was designed by simulating the electrodes for which this verification was completed. Utilizing the Serpentine pattern, we utilized a process that allows simultaneous fabrication and encapsulation of the matrix pattern. One side of the printed graphite electrode was O₂ plasma surface treated to increase adhesive strength, rotated 90 times, and two electrodes were made into one through a lamination process. As a result of pasting the matrix pattern prepared in this way to the wrist pulse position of the human body and proceeding with the actual measurement, a constant rate of resistance change was shown regardless of gender.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.

• Journal of Ginseng Research
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• v.48 no.1
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• pp.77-88
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• 2024

Background: Lung inflammation occurs in many lung diseases, but has limited effective therapeutics. Ginseng and its derivatives have anti-inflammatory effects, but their unstable physicochemical and metabolic properties hinder their application in the treatment. Panaxadiol (PD) is a stable saponin among ginsenosides. Inhalation administration may solve these issues, and the specific mechanism of action needs to be studied. Methods: A mouse model of lung inflammation induced by lipopolysaccharide (LPS), an in vitro macrophage inflammation model, and a coculture model of epithelial cells and macrophages were used to study the effects and mechanisms of inhalation delivery of PD. Pathology and molecular assessments were used to evaluate efficacy. Transcriptome sequencing was used to screen the mechanism and target. Finally, the efficacy and mechanism were verified in a human BALF cell model. Results: Inhaled PD reduced LPS-induced lung inflammation in mice in a dose-dependent manner, including inflammatory cell infiltration, lung tissue pathology, and inflammatory factor expression. Meanwhile, the dose of inhalation was much lower than that of intragastric administration under the same therapeutic effect, which may be related to its higher bioavailability and superior pharmacokinetic parameters. Using transcriptome analysis and verification by a coculture model of macrophage and epithelial cells, we found that PD may act by inhibiting TNFA/TNFAR and IL7/IL7R signaling to reduce macrophage inflammatory factor-induced epithelial apoptosis and promote proliferation. Conclusion: PD inhalation alleviates lung inflammation and pathology by inhibiting TNFA/TNFAR and IL7/IL7R signaling between macrophages and epithelial cells. PD may be a novel drug for the clinical treatment of lung inflammation.

• 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.