• Knowledge Management Research
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• v.24 no.4
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• pp.131-152
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• 2023

One of the key domains within a smart tourism city, smart mobility, encompasses advanced transportation means and services rooted in Information and Communication Technology (ICT). This includes shared bicycles, scooters, car-sharing services, smart transportation infrastructure, and more, aiming to surpass limitations of conventional transport and improve the movement of people and goods. It also serves tourists as an affordable and convenient mode of transport between attractions while also enhancing the overall travel experience. This study has defined 'smart tourism mobility' as a form of mobility grounded in ICT, exhibiting exceptional connectivity, serving public interest, and serving as a mode of transport for both residents and tourists in a smart tourism city. The research aimed to outline the scope of smart tourism mobility-related industries through expert Delphi surveys and estimate their economic effects within a smart tourism city. Specifically, this study updated 2015 input-output table and made 2020 regional input-output table of Seoul adopting RAS method and location quotient method. The results showed that the about 2.8 billion KRW investment of Seoul in smart tourism mobility may create more than 4.1 billion KRW in production inducement effect which is expected to create more than 1.6 billion KRW of income-inducing effect, 3.6 billion KRW of value-added-inducing effect, and 54 employment across all industries in Seoul in 2022.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1259-1272
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• 2022

Purpose As part of ongoing efforts to improve the current e-learning center, a survey was conducted regarding user experience and satisfaction to identify areas of improvement. Materials and Methods Radiologists (n = 454/617) and radiology residents (n = 163/617) of the Korean Society of Radiology were asked to answer a survey via email. The questionnaire asked for basic user information as well as user experiences relating to the e-learning center, such as workplace, frequency of use, overall satisfaction levels, reasons for satisfaction or dissatisfaction, and other suggestions for improvement. Results Annual members and all members of the e-learning center reported above average satisfaction levels of 67% and 42%, respectively. Approximately 30% of respondents viewed e-learning center lectures more than 5 times a month, with residents having a particularly high usage frequency. There was a high demand for additional lectures covering more diverse specialties (e-learning for annual members only: n = 28/97, e-learning for all members: n = 72/166), a smoother and more convenient searching platform/interface (n = 37/97 and n = 58/166, respectively), and regular content updates. In addition, many of the members suggested the addition of user-friendly functions such as playback speed control, a way to save viewing history, as well as requests for improved system stability. Conclusion Based on survey results, the educational committee plans to continue its efforts to improve the e-learning center by increasing the quality and quantity of available lectures, and increasing technical support to improve the stability and convenience of the e-learning digital system.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• Economic and Environmental Geology
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• v.48 no.1
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• pp.51-63
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• 2015

I produced a secular variation model of geomagnetic field by using the magnetic component data from four geomagnetic observatories located in Northeast Asia during the years between 1997 and 2011. The Earth's magnetic field varies with time and location due to the dynamics of fluid outer core and the magnetic observatories on the surface measure in time series. To adequately represent the magnetic field or secular variations of the Earth, a spatio-temporal model is required. In making a global model, satellite observations as well as limited observatory data are necessary to cover the regions and time intervals. However, you need a considerable work and time to process a huge amount of the dataset with complicated signal separation procedures. When you update the model, the same amount of chores is demanded. Besides, the global model might be affected by the measurement errors of each observatory that are biased and the processing errors in satellite data so that the accuracy of the model would be degraded. In this study, as considered these problems, I introduced a localized method in modeling secular variation of the Earth's magnetic field over Northeast Asia region. Secular variation data from three Japanese observatories and one Chinese observatory that are all in the INTERMAGNET are implemented in the model valid between 1997 to 2011 with the interval of 6 months. With the resulting model, I compared with the global model called CHAOS-4, which includes the main, secular variation and secular acceleration models between 1997 to 2013 by using the three satellites' databases and INTERMAGNET observatory data. Also, the geomagnetic 'jerk' which is known as a sudden change in the time derivatives of the main field of the Earth, was discussed from the localized secular acceleration coefficients derived from spline models.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.627-636
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• 2020

Debris flow disasters primarily occur in mountainous terrains far from cities. As such, they have been underestimated to cause relatively less damage compared with other natural disasters. However, owing to urbanization, several residential areas and major facilities have been built in mountainous regions, and the frequency of debris flow disasters is steadily increasing owing to the increase in rainfall with environmental and climate changes. Thus, the risk of debris flow is on the rise. However, only a few studies have explored the characteristics of flooding and reduction measures for debris flow in areas designated as steep slopes. In this regard, it is necessary to conduct research on securing independent disaster prevention technology, suitable for the environment in South Korea and reflective of the topographical characteristics thereof, and update and improve disaster prevention information. Accordingly, this study aimed to calculate the amount of debris flow, depending on disaster prevention performance targets for regions designated as steep slopes in South Korea, and develop an independent model to not only evaluate the impact of debris flow but also identify debris barriers that are optimal for mitigating damage. To validate the reliability of the two-dimensional debris flow model developed for the evaluation of debris barriers, the model's performance was compared with that of the hydraulic model. Furthermore, a 2-D debris model was constructed in consideration of the regional characteristics around the steep slopes to analyze the flow characteristics of the debris that directly reaches the damaged area. The flow characteristics of the debris delivered downstream were further analyzed, depending on the specifications (height) and installation locations of the debris barriers employed to reduce the damage. The experimental results showed that the reliability of the developed model is satisfactory; further, this study confirmed significant performance degradation of debris barriers in areas where the barriers were installed at a slope of 20° or more, which is the slope at which debris flows occur.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.1-12
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• 2014

It is essential factor for business operation to raise funds effectively. However, in Korea, many start-ups and small businesses have difficulties in fund-raising. In recent years, crowdfunding, a new method for funding a project of individuals or organizations by raising monetary contributions from a large number of people, has been growing up simultaneously with diffusion of social media. Crowdfunding is on early stage in Korea, and the majority of projects are focused on cultural or art categories. There is high proportion of projects that have social value in start-up sector. Crowdfunding in Korea has great potential because success rate of it is much higher than its of advanced countries, although market size is much smaller than them. The purpose of this paper is to propose success strategies of crowdfunding for start-up through case study. 5 crowdfunding platforms of Korea and Kickstarter, the platform of United States were investigated. Then we checked the figures related to the operation of the whole Korean projects on start-up. Finally, we made comparison between the cases of success and failure by analyzing 8 project characteristics. The study shows that it were the differences in trustworthiness and activeness of project creator, value of reward and efforts for interactivity that have great effects on success of the project. Whereas there was no significant influence of societal contribution and sponsor engagement. The thesis provides success strategies of crowdfunding for start-up as follows. Firstly, creator of the project should make support base by enthusiastic activites before launching funding project. Secondly, there should be contents that can easily show the process of business development in the project information. Thirdly, there must be appropriate design of rewards for each amounts of support money. Finally, efforts for interactivity, such as frequent updates, response for comments and SNS posting, should be followed after the launch of the project.

• Journal of Nutrition and Health
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• v.53 no.3
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• pp.271-287
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• 2020

Purpose: Variations in the iodine contents of foods is critical for estimating the iodine intake. This study aimed to update the iodine database of common Korean foods and evaluated the iodine intake in Korean adults. Methods: A list of 855 Korean foods was selected for the updated iodine database. The updated database was established with Version 1 and 2 by applying an average or minimum value for the imputed values. The iodine intake was estimated in 5,927 Korean adults using the data from the 2013-2015 Korea National Health and Nutrition Examination Survey. Results: The analytical values in the updated database were 166 (19.4%), followed in order by 318 (37.2%), 247 (28.9%), and 124 (14.5%) for the adapted, imputed, and missing values, respectively. The median of dietary iodine intake was 352.1 ㎍/day (± 2,166.1) and 343.4 ㎍/day (± 2,161.9) in Version 1 and 2 among the total population. The contribution rates of each food group to the iodine intake were 55.7% for seaweeds, which showed a similar trend in Version 2. When subjects were divided by consumption of seaweeds, the median iodine intake was 495.7 ㎍ in the consumer group, which was almost double (241.2 ㎍) that of the non-consumer group. The proportion of subjects who consumed below the Estimated Average Requirement of iodine was 11.0% in the non-consumer group. In contrast, 11.6% in the consumer group of seaweed consumed above the Upper Level of iodine. When the dietary iodine and urinary iodine were examined, the regression coefficient was 0.11718 in Version 1 and 0.11512 in Version 2 after adjusting for age and sex. Conclusion: This study presented the variation of iodine intake in Korean adults by applying different versions of the iodine database. As the iodine intake can vary due to the highly variable concentrations in the major food sources, an iodine database is necessary to be monitored, and caution should be taken when the database is used in research.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.847-860
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• 2020

The 2-pass DInSAR (Differential Interferometric SAR) processing steps for DEM generation consist of the co-registration of SAR image pair, interferogram generation, phase unwrapping, calculation of DEM errors, and geocoding, etc. It requires complicated steps, and the accuracy of data processing at each step affects the performance of the finally generated DEM. In this study, we developed an improved method for enhancing the performance of the DEM generation method based on the 2-pass DInSAR technique of TanDEM-X bistatic SAR images was developed. The developed DEM generation method is a method that can significantly reduce both the DEM error in the unwrapped phase image and that may occur during geocoding step. The performance analysis of the developed algorithm was performed by comparing the vertical accuracy (Root Mean Square Error, RMSE) between the existing method and the newly proposed method using the ground control point (GCP) generated from GPS survey. The vertical accuracy of the DInSAR-based DEM generated without correction for the unwrapped phase error and geocoding error is 39.617 m. However, the vertical accuracy of the DEM generated through the proposed method is 2.346 m. It was confirmed that the DEM accuracy was improved through the proposed correction method. Through the proposed 2-pass DInSAR-based DEM generation method, the SRTM DEM error observed by DInSAR was compensated for the SRTM 30 m DEM (vertical accuracy 5.567 m) used as a reference. Through this, it was possible to finally create a DEM with improved spatial resolution of about 5 times and vertical accuracy of about 2.4 times. In addition, the spatial resolution of the DEM generated through the proposed method was matched with the SRTM 30 m DEM and the TanDEM-X 90m DEM, and the vertical accuracy was compared. As a result, it was confirmed that the vertical accuracy was improved by about 1.7 and 1.6 times, respectively, and more accurate DEM generation was possible with the proposed method. If the method derived in this study is used to continuously update the DEM for regions with frequent morphological changes, it will be possible to update the DEM effectively in a short time at low cost.