• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.80-90
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• 2014

The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.4
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• pp.610-618
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• 2015

The purpose of this study was to analyze differences in emotional labor strategies, burnout, and work engagement according to general characteristics of school foodservice employees as well as verify differential effects of two emotional labor strategies on burnout and work engagement. Our survey was administered to 400 school foodservice employees in Gyeongbuk from March 3 to April 25, 2014. A total of 358 completed questionnaires were returned, and 350 questionnaires were used for final analysis. For verification of mean differences, the mean scores for surface acting, deep acting, burnout, and work engagement were shown to be 2.38/5.00, 3.46, 2.67, and 3.41, respectively. The mean surface acting was significantly different according to cooking certification (P<0.001), turnover number (P<0.001), salary (P<0.001), and school level (P<0.01). The mean deep acting was significantly different according to educational background (P<0.001), cooking certification (P<0.001), employment status (P<0.001), salary (P<0.001), school level (P<0.01), and meal service time (P<0.05). The mean burnout was significantly different according to educational background (P<0.01), cooking certification (P<0.05), employment status (P<0.001), school level (P<0.001), and meal service time (P<0.001). The mean work engagement was significantly different according to cooking certification (P<0.001), employment satus (P<0.001), salary (P<0.001), school level (P<0.01), and meal service time (P<0.05). Verification of causal models found that surface acting and deep acting increased burnout and deep acting, respectively (research model). Additionally, surface acting did not influence work engagement, and deep acting did not influence burnout (alternative models). In other words, we identified that emotional labor strategies have differential influences on burnout and work engagement. Finally, implications and limitations of this study are discussed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.79-92
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• 2019

When asked about the success factors of successful entrepreneurs and celebrities, he says he was lucky. The remarkable fact is that the attitude about luck is different. However, despite the fact that the belief that we believe is lucky is actually a dominant concept, there has not been much scientific verification of luck. In this study, we saw good luck not being determined randomly by the external environment, but by being able to control luck through the internal attributes of individuals. This study is significant that we have empirically elucidated what kind of efforts have gained good luck, whereas previous research has largely ended in vague logic where luck ends up with an internal locus of control among internal entrepreneurial qualities and efforts can make a successful entrepreneur. We introduced the concept of good luck belief to avoid confirmation bias, which is, to interpret my experience in a direction that matches what I want to believe, and used a good luck belief questionnaire in previous studies and tried to verify that those who have a good belief can increase entrepreneurial opportunity capability through planned happenstance skills. The reason for choosing the entrepreneurial opportunity capacity as a dependent variable was based on the conventional research, that is, the process of recognizing and exploiting the entrepreneurial opportunity is an important part of the entrepreneurship research For empirical research, we conducted a questionnaire survey of a total of 332 people, and the results of the analysis turned out that the belief of good luck has all the positive impacts of planned happenstance skills' sub-factors: curiosity, patience, flexibility, optimism and risk tolerance. Second, we have shown that only the perseverance, optimism, and risk tolerance of planned happenstance skills' sub-factors have a positive impact on this opportunity capability. Thirdly, it was possible to judge that the sub-factors of planned happenstance skills, patience, optimism, and risk tolerance, had a meditating effect between belief in luck and entrepreneurial opportunity capability. This study is highly significant in logically elucidating that people in charge of business incubation and education can get the specific direction when planning a training program for successful entrepreneur to further enhance the entrepreneurial opportunity ability, which is an important ability for the entrepreneur's success.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.