• Publications of The Korean Astronomical Society
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• v.39 no.1
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• pp.1-12
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• 2024

In this study, we investigated the time signal devices of Deungnu (circa 1270) and Gungnu (1354), the water clocks produced during the Yuan Dynasty (1271-1368). These clocks influenced Heumgyeonggaknu (1438) of the Joseon Dynasty (1392-1910), exemplifying the automatic water clocks of the Yuan Dynasty. Deungnu, Gungnu, and Heumgyeonggaknu can be considered as automatic mechanical clocks capable of performances. The Jega-Yeoksang-Jip (Collection of Calendrical and Astronomical Theories of Various Chinese Masters) contains records of Deungnu extracted from the History of the Yuan Dynasty. We interpreted these records and analyzed reproduction models and technical data previously produced in China. The time signal device of Deungnu featured a four-story structure, with the top floor displaying the four divine constellations, the third floor showcasing models of these divinities, the second floor holding 12-h jacks and a 100-Mark ring, and the first floor with four musicians and a 100-Mark Time-Signal Puppet providing a variety of visual attractions. We developed a 3D model of Deungnu, proposing two possible mechanical devices to ensure that the Time-Signal Puppet simultaneously pointed to the 100-Mark graduations in the east, west, south, and north windows: one model reduced the rotation ratio of the 100-Mark ring to 1/4, whereas the other model maintained the rotation ratio using four separate 100-Mark rings. The power system of Deungnu was influenced by Suunuisangdae (the water-driven astronomical clock tower) of the Northern Song Dynasty (960-1127); this method was also applied to Heumgyeonggaknu in the Joseon Dynasty. In conclusion, these automatic water clocks of East Asia from the 13th to 15th centuries symbolized creativity and excellence, representing scientific devices that were the epitome of clock-making technology in their times.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.209-223
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• 2024

Purpose - This article analyzes the impact of macroeconomic variables of the United States, China, and Korea on KOSPI and VKOSPI, in that United States and China have a great influence on Korea, having an export-driven economy. Design/methodology/approach - The influence of US, China, and Korea interest rates, industrial production index, consumer price index, US employment index, Chinese real estate index, and Korea's foreign exchange reserves on KOSPI and VKOSPI is analyzed on monthly basis from Jan 2012 to Aug 2023, using multifactor model. Findings - The KOSPI showed a positive relationship with the U.S. industrial production index and Korea's foreign exchange reserves, and a negative relationship with the U.S. employment index and Chinese real estate index. The VKOSPI showed a positive relationship with the Chinese consumer price index, and a negative relationship with the U.S. interest rates, and Korean foreign exchange reserves. Next, dividing the analysis into two periods with the Covid crisis and the analysis by country, the impact of US macroeconomic variables on KOSPI was greater than Chinese ones and the impact of Chinese macroeconomic variables on VKOSPI was greater than US ones. The result of the forward predictive failure test confirmed that it was appropriate to divide the period into two periods with economic event, the Covid Crisis. After the Covid crisis, the impact of macroeconomic variables on KOSPI and VKOSPI increased. This reflects the financial market co-movements due to governments' policy coordination and central bank liquidity supply to overcome the crisis in the pandemic situation. Research implications or Originality - This study is meaningful in that it analyzed the effects of macroeconomic variables on KOSPI and VKOSPI simultaneously. In addition, the leverage effect can also be confirmed through the relationship between macroeconomic variables and KOSPI and VKOSPI. This article examined the fundamental changes in the Korean and global financial markets following the shock of Corona by applying this research model before and after Covid crisis.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.230-236
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• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.941-950
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• 2024

This study employed network analysis techniques to examine the relationships between spatiotemporal factors associated with crimes in public restrooms, drawing on diverse relevant data sources. We then evaluated the relative importance of these factors in crime occurrence. Variables related to crime incidence were identified, and their interconnectedness was assessed for network analysis, resulting in a data-driven network model with complex relational structures. The network model contributed to calculating the weight of each factor and identifying key elements. The location of public restrooms, usage time, surrounding environment, and facility conditions emerged as crucial factors in crime occurrence, with lighting quality and local security status showing high weightings. These findings can be utilized to prioritize interventions in public restroom design and management to enhance safety. The network analysis methodology demonstrated its potential in proposing crime prevention measures for public spaces, including restrooms, and contributing to the creation of safer public environments.

• Journal of the Korea Institute of Building Construction
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• v.24 no.5
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• pp.541-552
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• 2024

This study introduces a new analytical model known as the Cellular Automaton Method(CAM) designed to predict the degree of deterioration in concrete, taking into account its complex pore structure. The CAM model assesses the impacts of moisture migration, driven by capillary action and pressure differentials at the gas-liquid interface, which are influenced by the distribution of pores. It also evaluates how porosity and diffusion coefficients affect the penetration of chloride ions. The model's application revealed distinct moisture movement patterns in concrete structures, distinguishing between those with porosity levels below and above 40 percent. Additionally, it facilitated a comparison and analysis of chloride ion diffusion phenomena, based on diffusion coefficients in areas penetrated by moisture, against results obtained from the Finite Element Method(FEM). The comparison showed a maximum deviation of only 0.989 percent between the predicted outcomes of the FEM and CAM, demonstrating substantial agreement and validating CAM's efficacy in simulating the diffusion processes of chloride ions within concrete under actual salt damage conditions. Thus, CAM proves to be a reliable tool for modeling and anticipating deterioration in concrete structures exposed to saline environments.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.445-456
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• 2005

The land surface parameters should be determined with sufficient accuracy, because these play an important role in climate change near the ground. As the surface reflectance presents strong anisotropy, off-nadir viewing results a strong dependency of observations on the Sun - target - sensor geometry. They contribute to the random noise which is produced by surface angular effects. The principal objective of the study is to provide a database of accurate surface reflectance eliminated the angular effects from MODIS 250m reflective channel data over Korea. The MODIS (Moderate Resolution Imaging Spectroradiometer) sensor has provided visible and near infrared channel reflectance at 250m resolution on a daily basis. The successive analytic processing steps were firstly performed on a per-pixel basis to remove cloudy pixels. And for the geometric distortion, the correction process were performed by the nearest neighbor resampling using 2nd-order polynomial obtained from the geolocation information of MODIS Data set. In order to correct the surface anisotropy effects, this paper attempted the semiempirical kernel-driven Bi- directional Reflectance Distribution Function(BRDF) model. The algorithm yields an inversion of the kernel-driven model to the angular components, such as viewing zenith angle, solar zenith angle, viewing azimuth angle, solar azimuth angle from reflectance observed by satellite. First we consider sets of the model observations comprised with a 31-day period to perform the BRDF model. In the next step, Nadir view reflectance normalization is carried out through the modification of the angular components, separated by BRDF model for each spectral band and each pixel. Modeled reflectance values show a good agreement with measured reflectance values and their RMSE(Root Mean Square Error) was totally about 0.01(maximum=0.03). Finally, we provide a normalized surface reflectance database consisted of 36 images for 2001 over Korea.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.491-521
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• 2018

This paper aims to uncover success factors in technology transfer of Korean government sponsored research institutes. It presents an analytical framework of technology transfer integrating factors concerning actors, object and process. Qualitative analysis mainly based on In-depth interviews is designed and conducted to examine characteristics, similarities and differences of three types technology transfer including a supply-push model, a demand-pull model, and an interactive model. Empirical results show that capabilities and experiences of researchers, capabilities and willingness of the company and active communications in the technology transfer appear to be important factors. In particular, it was found that technology transfer was led by TLO in the demand-pull model. In addition, the demand-pull and interactive approaches tends to be more effective in the technology development and transfer process compared to the supply-push model. In other words, market-oriented R&D is needed instead of technology-driven R&D. This paper also pointed out that one of the major factors in successful technology transfer is the enhancement of TLO capacity, and the improvement of recognition about TLO.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.120-134
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• 2015

InVEST Water Yield model developed by Natural Capital Project was applied for South Korea to assess domestic forest ecosystem's water provisioning services. The InVEST Water Yield model required 8 input dataset, including six spatial map data and two derived by coefficients. By running the model with relatively easy acquired and modified data, the result of domestic forest ecosystem's water provisioning services was 9,409,622,083 ton using the standard of the year 2011. The result showed similar patterns and distribution of rainfall in 2011, but showed difference when compared with existing researches spatially driven in nationwide statistical analysis results. This difference is assumed to occur with different model mechanism in spatial implementation and statistical analysis. So given that the model is currently still developing, applications should be taken on qualitative perspectives rather than on quantitative perspectives. Additionally, for advancing the application of InVEST water yield model, quantification of suitable input data and comparison using multi-modeling is required.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.284-294
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• 2020

As a solution to local and social problems, the active use of smart city living labs is becoming increasingly important. The answer to solving local and social problems lies in the citizen and the field. The purpose of this study is to establish a smart city living lab model based on citizen participation. In this study, smart city living lab model(4P-SCLLM) based on citizen participation was established through domestic and overseas living lab methodology and case analysis. In order to evaluate the systemicity and specificity of the 4P-SCLLM, a smart city living lab model, we recently compared it with the living lab process in Busan where smart city living lab is applied. As a result of analyzing, the analysis shows similar trends in each stage, and Busan's private sector showed a similar process to 4P-SCLLM On the other hand, public and private sector cooperation and support systems were found to be less than the 4P-SCLLM model And In technology and methodology, the 4P-SCLLM model is analyzed to have a living lab process that incorporates new technologies. In order to maintain the 4P-SCLLM continuously, first, participants and stakeholders need to participate actively and communicate while collaborating on the whole process from start to finish. Second, public awareness needs to be improved. Third, continuous citizenship verification of services is needed. Fourth, citizens' constant participation is needed. Through these implications, this study proposed 4P-SCLLM as a smart city living lab model suitable for the domestic situation.