• Economic and Environmental Geology
• /
• v.41 no.2
• /
• pp.211-217
• /
• 2008

The gravity anomalies that observed by ground and shipborne survey and calculated from GRACE satellite are combined by using spherical cap harmonic analysis (SCHA). In this study, ground gravity data from Korea Institute of Geoscience and Mineral Resource(KIGAM) and shipborne gravity data from National Ocean Research Institute(NORI) and Korea Ocean Research and Development institute(KORDI) were used. L-2 level GRACE Gravity Model (GGM02C) was also used for satellite gravity anomaly. The ground and shipborne surveyed data were combined and gridded using Krigging method with 0.05 degree interval and GRACE data were also gridded using the same method with 0.05 degree to harmonize with the resolution of SCHA that has coefficient up to 80. Generalized Minimal Residual(GMRES) inversion method was implemented for calculating the coefficients of SCHA using the gridded ground and satellite gravity anomalies that had 0 km and 50 km altitude, respectively. The results of inversion method showed good correlation of 0.950 and 0.995 with original ground and satellite data. The gravity anomaly using SCHA satisfies Laplace's equation, therefore, using these SCHA coefficients, gravity anomaly can be calculated at any altitude. In this study, gravity anomaly was calculated from 10 km to 60 km altitude and each altitude, very stable results were shown. The ground and shipborne gravity data that have higher resolution and satellite data in long wavelength are harmonized well with SCHA coefficients and successfully applied in South Korea area. If more continuous survey and muti-altitude surveyed data like airborne data available, more precise gravity anomaly can be acquired using SCHA method.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.251-263
• /
• 2019

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.

• Journal of Bio-Environment Control
• /
• v.30 no.1
• /
• pp.85-93
• /
• 2021

In hydroponic cultivation, in order to investigate the change of lettuce growth and physiologically active substances through CO2 tablet treatment in nutrient solution, we used a solid carbonated tablets commercially available in the Netherlands. The experiment consisted of 0.5-fold, 1-fold, and 2-fold treatment groups with no treatment as a control. As a result, the atmospheric CO2 concentration in the chamber after CO2 tablet treatment showed the highest value at 472.2 µL·L-1 in the 2-fold treatment zone immediately after treatment, and the pH in the nutrient solution decreased the most to pH 6.03 in the 2-fold treatment zone. After that, over time, the CO2 concentration and pH recovered to the level before treatment. Leaf width and leaf area of lettuce showed the highest values of 17.1cm and 1067.14 ㎠ when treated 2-fold with CO2 tablet, while fresh weight and dry weight of the above-ground part were highest at 63.87 g and 3.08 g in 0.5-fold treatment. The root length of lettuce was the longest (28.4 cm) in the control, but there was no significant difference in the fresh weight and the dry weight among the treatments. Apparently, it was observed that the root length of the lettuce was shortened by CO2 tablet treatment and a lot of side roots occurred. In addition, there was a growth disorder in which the roots turned black, but it was found that there was no negative effect on the growth of the above-ground part. As a result of analyzing the bioactive compounds of lettuce by CO2 tablet treatment, chlorogenic acid and quercetin were detected. As a result of quantitative analysis, chlorogenic acid increased by 249% compared to the control in 1-fold treatment, but quercetin decreased by 37%. As a result of comparing the DPPH radical scavenging ability showing antioxidant activity, the control and 0.5-fold treatment showed significantly higher values than the 1-fold and 2-fold treatments. This suggests that carbonated water treatment is effective in increasing the growth and bioactive compounds of hydroponic lettuce.

• Journal of Bio-Environment Control
• /
• v.31 no.1
• /
• pp.67-76
• /
• 2022

This study aimed to determine the photosynthesis and growth characteristics of Peucedanum japonicum T. grown under aquaponics in a plant factory (AP) by comparing those grown under hydroponic cultivation system (HP). The AP system raised 30 fishes at a density of 10.6 kg·m^-3 in a 367.5 L tank, and at HP, nutrient solution was controlled with EC 1.3 dS·m^-1 and pH 6.5. The pH level ranged from 4.0 to 7.1 for the AP system and 4.0 to 7.4 for the HP system. The pH level in the AP began to decrease with an increase in nitrate nitrogen (NO₃-N) and lasted bellower than pH 5.5 for 15-67 DAT. It was found that ammonium nitrogen (NH₄-N) continued to increase even under low pH conditions. EC was maintained at 1.3 to 1.5 dS·m^-1 in both systems. The concentration of major mineral elements in the fish tank was higher than that of the hydroponics, except for K and Mg. There was no significant difference in the photosynthesis characteristics, but the PIABS parameters were 30.4% lower in the AP compared to the HP at the 34DAT and 12.0% lower at the 74DAT. There was no significant difference in the growth characteristics, but the petiole length was 56% longer in the leaf grown under the AP system. While there was no significant difference in the fresh and dry weights of leaf and root, the leaf area ratio was 36.43% higher in the AP system. All the integrated results suggest that aquaponics is a highly-sustainable farming to safely produce food by recycling agricultural by-products, and to produce Peucedanum japonicum as much as hydroponics under a proper fish density and pH level.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.552-560
• /
• 2018

Since 1960's, the large conglomerates of South Korea have grown due to the corporate-centered, fast-paced growth drive, while the small and medium-sized businesses supported the country's economy as the subordinate structure of these conglomerates. Due to the globalization of the business environments, the focus of competition shifted from competitions between individual companies to one between networks of companies. Therefore, more emphasis is now put on the capabilities of the cooperation networks between companies rather than the capabilities of individual companies. Therefore, in this study, the author examined the influence of the win-win growth strategy elements through cooperation with small and medium-sized businesses upon corporate performance. This study was conducted with the workers of small and medium-sized businesses that have previous cooperation experiences with South Korean conglomerates over the period from March 2 to May 17, 2018. For this, a total of 515 questionnaires were retrieves to obtain the data for analysis. The analysis was conducted using SPSS 22.0 and AMOS 18.0. The analytical processes that were taken included exploratory factor analysis, confirmatory factor analysis, confidence analysis, correlation analysis, and structural equation analysis model. The results of the analysis showed that, first of all, the win-win growth strategy factors that affected the strategic performance, which is a part of cooperate performance were, respectively, harmonization with the goals, production technical support, and quality system. Second, the win-win growth strategy factors that affected the financial performance, which is a part of corporate performance, turned out to be harmonization with the goals, quality system, and incentive. With the results of this study, it was shown that the elements such as harmonization with the goals, production technical support, quality systems, and incentives were key infrastructural factors that affected the corporate performance directly. On the other hand, its implication is that informative or knowledge-related factors, such as joint knowledge creation, do not have their own added values, while they are not too much likely to affect corporate performances for the moment.

• Korean Journal of Remote Sensing
• /
• v.38 no.2
• /
• pp.189-198
• /
• 2022

In this present study, we investigated the effect of the offset correction factor calculation method on the sulfur dioxide (SO₂) column density in the SO₂ retrieval algorithm of the Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020. The GEMS operational SO₂ retrieval algorithm is the Differential Optical Absorption Spectroscopy (DOAS) - Principal Component Analysis (PCA) Hybrid algorithm. In the GEMS Hybrid algorithm, the offset correction process is essential to correct the absorption effect of ozone appearing in the SO₂ slant column density (SCD) obtained after spectral fitting using DOAS. Since the SO₂ column density may depend on the conditions for calculating the offset correction factor, it is necessary to apply an appropriate offset correction value. In this present study, the offset correction values were calculated for days with many cloud pixels and few cloud pixels, respectively. And a comparison of the SO₂ column density retrieved by applying each offset correction factor to the GEMS operational SO₂ retrieval algorithm was performed. When the offset correction value was calculated using radiance data of GEMS on a day with many cloud pixels was used, the standard deviation of the SO₂ column density around India and the Korean Peninsula, which are the edges of the GEMS observation area, was 1.27 DU, and 0.58 DU, respectively. And around Hong Kong, where there were many cloud pixels, the SO₂ standard deviation was 0.77 DU. On the other hand, when the offset correction value calculated using the GEMS data on the day with few cloud pixels was used, the standard deviation of the SO₂ column density slightly decreased around India (0.72 DU), Korean Peninsula (0.38 DU), and Hong Kong (0.44 DU). We found that the SO₂ retrieval was relatively stable compared to the SO₂ retrieval case using the offset correction value on the day with many cloud pixels. Accordingly, to minimize the uncertainty of the GEMS SO₂ retrieval algorithm and to obtain a stable retrieval, it is necessary to calculate the offset correction factor under appropriate conditions.

• Journal of Industrial Convergence
• /
• v.20 no.9
• /
• pp.59-70
• /
• 2022

Background: Diabetes education is an important factor in blood glucose control. Reinforced education is necessary for effective diabetes education. However, it is difficult to provide reinforced diabetes education within Korea's medical environment. Therefore, we want to analyze the effect of continuous diabetes education using mobile health care that can effectively provide repeated education without having to face the patient. Methods: This study is a multicenter, randomized, controlled, pre-post design study conducted to analyze the effect of a continuous diabetes education method. A total of 109 people were registered at five hospitals in south Korea, and they were randomly assigned to the app group (34 people) who received real-time coaching and repetitive training, the logbook group (37 people) who received face-to-face training after writing a blood glucose logbook, and the general group (38 people) who received a one-time diabetes education. The study was conducted for a total of 24 weeks. Twenty-one patients withdrew their consent and failed to perform an HbA1c. A final 88 patients were analyzed. The difference in HbA1c, Self-management behavior, and Quality of life before and after education was analyzed. Results: The study involved 51 (58%) male subjects, mean age was 55.8 years and mean duration of diabetes was 7.6 years. After 24 weeks of intervention, there was no significant difference in self-care behavior and quality of life between the three groups, but the HbA1c of the app group significantly decreased after education compared to the logbook group and the general group (F=4.62, p=.013). Conclusion: It can be seen through the app group that receiving real-time education is more effective in improving blood glucose management and continuous diabetes education is important.

• Journal of Korea Water Resources Association
• /
• v.56 no.10
• /
• pp.631-639
• /
• 2023

Flash drought (FD), characterized by the rapid onset and intensification, can significantly impact ecosystems and induce immediate water stress. A more comprehensive understanding of the causes and characteristics of FD events is required to enhance drought monitoring. Therefore, we investigated the FD events took place over the Korean peninsula using Global Land Data Assimilation System (GLDAS) data from 2012 to 2022. We first detected FD events using the stress-based method (Standardized Evaporative Stress Ratio, SESR), and analyzed the frequency and duration of FDs. The FD events were classified into three cases based on the variations in Actual Evapotranspiration (AET) and potential Evapotranspiration (PET), and spatially analyzed. Results revealed that there are regional disparities in frequency and duration of FDs, with a mean frequency of 6.4 and duration of 31 days. When classified into Case 1 (normal condition), Case 2 (AET-driven), and Case 3 (PET-driven), we found that Case 2 FDs emerged approximately 1.5 times more frequently than those driven by PET (Case 3) across the Korean peninsula. Case 2 FDs were found to be induced under water-limited conditions, and led both AET and PET to be decreased. Conversely, Case 3 FDs occurred under energy-limited conditions, with increase in both. Case 2 FDs predominantly affected the northwestern and central-southern agricultural regions, while Case 3 occurred in the eastern region, characterized by forested land cover. These findings offers insights into our understanding of FDs over the Korean peninsula, considering climate factors, land cover, and water availability.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.5
• /
• pp.17-32
• /
• 2023

The entrepreneurial activities of college students play a significant role in modern economic and social development, particularly as a solution to the changing economic landscape and youth unemployment issues. Introducing innovative ideas and technologies into the market through entrepreneurship can contribute to sustainable economic growth and social value. Additionally, the entrepreneurial intentions of college students are shaped by various factors, making it crucial to deeply understand and appropriately support these elements. To this end, this study systematically explores the importance and impact of role models through a multiple serial mediation analysis. Through a survey of 300 college students, the study analyzed how two psychological variables, growth mindset and entrepreneurial self-efficacy, mediate the influence of role models on entrepreneurial intentions. The presence and success stories of role models were found to enhance the growth mindset of college students, which in turn boosts their entrepreneurial self-efficacy and ultimately strengthens their entrepreneurial intentions. The analysis revealed that exposure to role models significantly influences the formation of a growth mindset among college students. This mindset fosters a positive attitude towards viewing challenges and failures in entrepreneurship as learning opportunities. Such a mindset further enhances entrepreneurial self-efficacy, thereby strengthening the intention to engage in entrepreneurial activities. This research offers insights by integrating various theories, such as mindset theory and social learning theory, to deeply understand the complex process of forming entrepreneurial intentions. Practically, this study provides important guidelines for the design and implementation of college entrepreneurship education. Utilizing role models can significantly enhance students' entrepreneurial intentions, and educational programs can strengthen students' growth mindset and entrepreneurial self-efficacy by sharing entrepreneurial experiences and knowledge through role models. In conclusion, this study provides a systematic and empirical analysis of the various factors and their complex interactions that impact the entrepreneurial intentions of college students. It confirms that psychological factors like growth mindset and entrepreneurial self-efficacy play a significant role in shaping entrepreneurial intentions, beyond mere information or technical education. This research emphasizes that these psychological factors should be comprehensively considered when developing and implementing policies and programs related to college entrepreneurship education.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.26 no.2
• /
• pp.129-152
• /
• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.