• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.977-984
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• 2006

This paper is intended to trace and management of drug based on RFID Technology at a circulation market, from manufacturer to end user, of drug. To avoid counterfeit and generic drug and establish of order in the circulation of drug, at the moment of manufacturing, tags for each bottle and each box are tagged. and then from factory to hospital, through whole logistics, e-pedigree for the drug is made and monitored. Using inventory information, it is easy to manage and control stock of drug. In addition to, RFID System enables storing and delivery to be simple, process time to be shortened. As this research is to study of applying RFID to drug, in this paper, standard RFID code for drug is suggested and tried to apply domestic middle win. Finally, the result of tag pattern design and how to tag for the drug based on 90Mhz is proposed

• Journal of the Korean Regional Science Association
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• v.12 no.2
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• pp.99-116
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• 1996

This study investigates the nature and patterns of interrelation between the emerging information and communication technology(ICT) and the organization of corporate space, both theoretically and empirically. In this work, ICT is conceptualized not so much a space-adjusting technology as an organizational technology. ICT is considered as a governance technology which is related to coordination function within a firm. Therefore, it is supposed to have a great relevance to the spatial reorganization of functions within a firm. Both questionnaire and case study method are used to gather necessary data from Korean electronics manufactures. The results of this study can be summarized as follow. First, the spatial structure of a firm, which is operationalised as the number and type of spatially separated establishments, is turned out to have a great explanatory power to its adoption of computer networks. Computer networks in muli-locational companies are introduced to overcome the limits of its spatial structure, such as duplication of functions, such as duplication of functions, loss of time spent in proceeding a job between different functional units, and unresponsiveness to the change of market demand. Second, new spatial division of labor and function could be possible through a series of business process reengineering, not through the mere adoption of ICT. Case studies reveal that computer network could help a firm to realize new forms of spatial division of labor, especially in those functions which is mainly based on the flow of information. Such function as ICT management, sales logistics and after-sales service are major parts where a new operational unit has appeared with the help of ICT. From above results, it can be concluded that the interrelations between ICT and organizational space should be approached intimately integrated with the change of industrial structure and it's organizational implications.

• Journal of Navigation and Port Research
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• v.29 no.5 s.101
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• pp.421-429
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• 2005

Traditionally, determination of the optimal containership size is the most important factor for competitiveness of shipping companies in the shipping market. Under this environment, many shipping companies and researchers have studied about it. The objective of this research is to determine the optimal containership size using a total shipping cost in the main trunk lines. Total shipping cost is calculated at the ground of capital costs, vessel operation costs, voyage costs, port charges and miscellaneous costs for 'Europe-Far East', 'Far East-North America' and 'Europe-Far East-North America' services. Analysis results showed that the 6,500TEU containership is an optimal size on the 'Europe-Far East' and 'Europe-Far East-North America' services. And the 8,200TEU containership is an optimal size on the 'Far East-North America' service. Moreover, if the larger containerships over 8,200TEU class start operation afterward, it would be less competitive in the analyzed 3services.

• Convergence Security Journal
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• v.18 no.5_2
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• pp.3-10
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• 2018

The drone is an unmanned aircraft that can be steered and controlled using radio waves on the ground, and the pilot moves unmanned without boarding. The history of the unmanned airplane began with military use, and the first unmanned aerial flight was the first successful flight of the 'Sperry Aerial Torpedo' drones built in the United States in 1917 with a bomb. With the development of unmanned aerial technology, the use of military drones has expanded to a wider field. Recently, the use of drones has been utilized in various fields such as agriculture, industry, logistics, broadcasting, and safety, and the scale of the market is also expanding. Although the drones are becoming indispensable to penetrate our lives, they can be used for bad purposes depending on the intended use of the user, but the risk factors are overlooked. Therefore, technical defects related to drones and accidents caused by operator's mistakes can not be completely prevented. However, privacy infringement, security leakage, and terrorism, which may be caused by illegal use of drones, It will not be inhibited and will accelerate.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.47-53
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• 2019

The purpose of the study was to utilize VECM(Vector Error Correction Model) and detect causal relationships among shipping freight rates. Shipping freight rates used in this study were BDI(Baltic Dry Index), HRCI(Howe Robinson Containership Index), WS(World Scale rate) and SCFI(Shanghai Containerized Freight Index). Using weekly data published since August 2nd, 2013 to September 6th, 2019, it was discovered that BDI and WS were heavily influenced by past week's BDI and WS respectively. VECM also found that one percent increase in WS resulted in 0.022% increase in following week's HRCI data. One percent increase in HRCI affects SCFI by 0.77% on the following week. This study believes that finding may help each shipping market of shipping freight rates estimates, thereby encouraging decision markers to exercise discretion and establish best interest decision.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

• Journal of Korea Trade
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• v.25 no.5
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• pp.110-128
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• 2021

Purpose - The essential purpose of this study is to analyze the possibility of substitution of an individual job resulting from technological development represented by the 4th Industrial Resolution, considering the different effects of digital transformation on the labor market. Design/methodology - In order to estimate the substitution probability, this study used two data sets which the job characteristics data for individual occupations provided by KEIS and the information on occupational status of substitution provided by Frey and Osborne(2013). In total, 665 occupations were considered in this study. Of these, 80 occupations had data with labels of substitution status. The primary goal of estimation was to predict the degree of substitution for 607 of 665 occupations (excluding 58 with markers). It utilized three methods a principal component analysis, an unsupervised learning methodology of machine learning, and Ridge and Lasso from supervised learning methodology. After extracting significant variables based on the three methods, this study carried out logistics regression to estimate the probability of substitution for each occupation. Findings - The probability of substitution for other occupational groups did not significantly vary across individual models, and the rank order of the probabilities across occupational groups were similar across models. The mean of three methods of substitution probability was analyzed to be 45.3%. The highest value was obtained using the PCA method, and the lowest value was derived from the LASSO method. The average substitution probability of the trading industry was 45.1%, very similar to the overall average. Originality/value - This study has a significance in that it estimates the job substitution probability using various machine learning methods. The results of substitution probability estimation were compared by industry sector. In addition, This study attempts to compare between trade business and industry sector.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.187-197
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• 2020

In this study, how international diversification of domestic companies increases corporate profits and increases the dividend income of paid-in capital investors, who provided the basis for corporate business activities in the process of distributing profits. I tried to find out if it had an effect. An empirical analysis was conducted using a fixed-effect model for companies with settlements at the end of December listed on the domestic securities market from 2011 to 2018. It was confirmed that the higher the level of international diversification of individual companies, the higher the company's dividend payout ratio and dividend yield. This means that companies can steadily expand corporate profits and dividend yield of shareholders by securing new overseas markets through international diversification, it can be seen that a company's international diversification strategy can contribute to the increase of corporate value by increasing the company's dividend payout ratio by increasing dividendable profit.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.