• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.156-162
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• 2021

The number of smart farms has increased to save labor in agricultural production as the subsidy become available from central and local governments. The number of illegal greenhouses has also increased, which causes serious issues for the local governments. In the present study, we developed Mask-RCNN model to detect greenhouses based on satellite images. Greenhouses in the satellite images were labeled for training and validation of the model. The Mask-RC NN model had the average precision (AP) of 75.6%. The average precision values for 50% and 75% of overlapping area were 91.1% and 81.8%, respectively. This results indicated that the Mask-RC NN model would be useful to detect the greenhouses recently built without proper permission using a periodical screening procedure based on satellite images. Furthermore, the model can be connected with GIS to establish unified management system for greenhouses. It can also be applied to the statistical analysis of the number and total area of greenhouses.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.23-34
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• 2009

In the railroad rolling stock depot, long-term maintenance tasks is done regularly every two or four year basis to maintain the functionality of equipments and rolling stock body or for the repair operation of the heavily damaged rolling stocks by fatal accidents. This paper addresses the computer simulation model building for the rolling stock maintenance shop for the CDC(Commuter Diesel Car) and Generator Car planned to be constructed at Daejon Rolling Stock Depot, which will be moved from Yongsan Rolling Stock Depot. We evaluated the processing capacity of two layout design alternatives based on the maintenance process chart through the developed simulation models. The performance measures are the number of processed cars per year, the cycle time, shop utilization, work in process and the average number waiting car for input. The simulation result shows that one design alternative outperforms another design alternative in every aspect and superior design alternative can process total 340 number of trains per year 15% more than the proposed target within the current average cycle time.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.325-340
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• 2022

Amid the unprecedented situation of COVID-19 around the world, online education has established itself as an essential element in the era of zero contact and the importance of various content and changes of the system that are appropriate for the era of the 4^th industrial revolution has increased. Although universities are making their efforts to combine ICT technologies and design and achieve new systems, the recognition and atmosphere for establishing the cloud computing system are falling short. The purpose of this research importance of success factors of "Building a cloud computing system of cyber university in Korea" by classifying the work characteristics and scale, and to derive and analyze the importance cloud rankings considering the organization and individual dimension. Therefore, this study has drawn 14 major factors in the previous researches and models through the survey on experts with knowledge related to the cloud computing. The analysis was conducted to see what differences there are in factors for the successful establishment of the cloud computing system using AHP. It is expected that the factors for success presented through this study would be used as systemic strategies and tools for the purpose of drawing factors for the success of establishing the private cloud computing system for the higher education institutions and public information systems.

• Korea Trade Review
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• v.43 no.1
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• pp.199-221
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• 2018

The internationalization of venture firms is of importance resulting from the increased opportunities as well as the entry barriers faced in this process. This study is to investigate barrier factors affecting Korean venture firms' internationalization. Among these factors, we measured relative weights using Analytic Hierarchy Process (AHP). Literatures regarding global firm's internationalization was reviewed, and barriers were classified environment, corporate and CEO barriers. Multiple variables were used in order to measure their relative significance. We surveyed executives of venture firms comprising two groups: first, 15 global venture firms, and secondly, gradually internationalizing firms. The two groups show contradictory results as environment barriers are the primary for born-global ventures and corporate barriers are the primary for gradually internationalizing ventures. Concerning corporate barriers, the lack of marketing capability is the highest barrier for both groups. The CEO barrier is the lowest for both groups. Regarding corporate barriers, gradually internationalizing ventures considers the risk of business as highest, whereas born-global ventures features the lack of R&D capacity as highest Results of this paper imply that firms should consider priority and sequential approaches in facing environment, corporate and CEO barriers.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.207-228
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• 2023

After COVID-19, organizations have widely adopted platforms such as zoom or developed their proprietary online real-time systems for remote work, with recent forays into incorporating the metaverse for meetings and publicity. While ongoing studies investigate the impact of avatar customization, expansive virtual environments, and past virtual experiences on participant satisfaction within virtual reality or metaverse settings, the utilization of the metaverse as a dedicated workspace is still an evolving area. There exists a notable gap in research concerning the factors influencing the performance of the metaverse as a workspace, particularly in non-immersive work-type metaverses. Unlike studies focusing on immersive virtual reality or metaverses emphasizing immersion and presence, the majority of contemporary work-oriented metaverses tend to be non-immersive. As such, understanding the factors that contribute to the success of these existing non-immersive metaverses becomes crucial. Hence, this paper aims to empirically analyze the factors impacting personal outcomes in the non-immersive metaverse workspace and derive implications from the results. To achieve this, the study adopts the Embodied Social Presence (ESP) model as a theoretical foundation, modifying and proposing a research model tailored to the non-immersive metaverse workspace. The findings validate that the impact of presence on task engagement and task involvement exhibits a moderating effect based on the metaverse platform used. Following interviews with participants engaged in non-immersive metaverse workplaces (specifically Gather Town and Ifland), a survey was conducted to gather comprehensive insights.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.34-44
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• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of Korean Society of Archives and Records Management
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• v.6 no.2
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• pp.57-85
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• 2006

There should be an integration between work management and records management in order to document the work processes thoroughly. It's proper to establish a records classification system to have the work classification and record classification table integrated for that purpose. But the conventional procedures and methodology used for records classification system development lack specific features to be used as voluntary guidelines of a common organization or group and to conduct analysis. Recognizing the problems, this study suggested the specific methods of records classification system development to link work management and records management organically. First, the functional classification was chosen as the principle of classification for records classification system development. Then concrete methods of records classification system development were suggested. Analysis and comparison were made for the DIRKS(Designing and Implementing Recordkeeping Systems), which is the standard records management and work analysis of Australia, and AS 5090. The results were used to suggest specific methods of records classification system development in conjunction with the research into the methodology employed for work analysis in information engineering and business administration to compensate for its weakness. The significance of the study can be found in that it suggested the methods of typical records classification system development in connection with records classification, and that it applied them to the Presidential Committee for the Inspection of Collaborations for Japanese Imperialism and tested them.

• Journal of Technology Innovation
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• v.21 no.1
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• pp.85-108
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• 2013

This paper introduces a new qualitative measurement indicator, Relatively Rank-normalized Impact Factor ($R^2nIF$) that can overcome drawbacks of the existing research performance measures. With the help of this innovative indicator, this study analyzes and compares the quality of research papers across countries and National Science Indicators (NSI) standard academic fields. The development of an improved bibliometric indicator for evaluating the quality of research papers and disentangling the "international" dimension of research performance will be of interest to academics and practitioners alike.