• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.61-74
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• 2010

The knowledge sharing in a knowledge management process is much affecting generation and distribution of knowledge. Especially, the knowledge distribution is being revitalized with the center of social media service like twitter and library service 2.0 in the knowledge-based IT (Information Technology) environment. The present research analyzed the structure and characteristics of a social network inside an organization that is growing like an organism through self-organization through tools for SNA (Social Network Analysis) and multiple regression analysis of independent variables such as 1) a relationship between social network's structure and knowledge sharing, 2) a relationship between structural holes and knowledge sharing influence of centrality, 3) a relationship between individual ability and knowledge sharing of information technology and work recognition.

• Korean Journal of Digital Imaging in Medicine
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• v.10 no.1
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• pp.29-34
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• 2008

Purpose To analyze the input and output error of data CD which records the image information and the problems of the server of the compatibility. And to report a compatibility assessment and verification of suitable to DICOM of PACS data CD with investigation of current situation of Korea METHOD AND MATERIALS Date CD of each 8 vendors in 30 hospitals was analyzed. We grasped a main verification element existence of a generation compatibility of data CD. The items of element are media identification, DICOM compression, DICOM viewer send, specified object information modify, auto-run, DICOM content type, etc, and give 1 point for each item. We divided the assessment about an each item into 5 levels. Verification about. DICOM conformance by using DICOM validation tool kit is shown to be classified pass or fail according to error occurrence of tag valus. Classify the prequency of tag occurrence as the item. RESULTS The average point of date CD compatibility is 8 point (very good), lowest is 5 point (6.6%), and highest is 10 point (23%_. Most high occurrence frequency's distribution is 7 point (36.6%). As a result of verification about DICOM conformance, PASS in 8 occurrence frequency's distribution is 7 point (36.6%). As a result of verification about DICOM maximum length numbers (14 items), DICOM error of modality (10 items), discord of pixel data length (6 items). etc.

• Composites Research
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• v.34 no.4
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• pp.257-263
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• 2021

Composite materials have been widely applied for fabricating pressure vessels used for storing gaseous and liquid fuel because of their high specific stiffness and specific strength. Accordingly, the accurate measurement of their mechanical property, particularly the burst pressure or fracture strain, is essential prior to the commercial release. However, verification of the safety of composite pressure vessels using conventional test methods poses some limitations because it may lead to the deformation of the load transferring media or provoke an additional energy loss that cannot be ignored. Therefore, in this study, the segment-type ring burst test device was designed considering the theoretical load transferring ratio and applicable displacement of the vertical column. Moreover, to verifying the uniform distribution of pressure of the segment type ring burst test device, the hoop stress and strain distribution of ring specimens were compared with that of the hydraulic pressure test method via FEM. To conduct a simulation of the fracture behavior of the composite pressure vessel, a Hashin failure criterion was applied to the ring specimen. Furthermore, the fracture strain was also measured from the experiment and compared with that of the result from the FEM.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.511-518
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• 2022

Supervised learning based on deep learning has made a leap forward in various application fields. However, many supervised learning methods work under the common assumption that training and test data are extracted from the same distribution. If it deviates from this constraint, the deep learning network trained in the training domain is highly likely to deteriorate rapidly in the test domain due to the distribution difference between domains. Domain adaptation is a methodology of transfer learning that trains a deep learning network to make successful inferences in a label-poor test domain (i.e., target domain) based on learned knowledge of a labeled-rich training domain (i.e., source domain). In particular, the unsupervised domain adaptation technique deals with the domain adaptation problem by assuming that only image data without labels in the target domain can be accessed. In this paper, we explore the unsupervised domain adaptation techniques.

• Journal of Korea Port Economic Association
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• v.40 no.3
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• pp.131-141
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• 2024

This study used representative variables mentioned in the media after COVID-19 to identify the relationship between container ocean freight rates. The research results are as follows: Container utilization index, US distribution industry inventory ratio, and number of new US unemployment benefit claims emerged as variables that influenced freight rates. It was shown that a 1% increase in the container utilization index would increase freight rates by 9.4%, and a 1% increase in the US distribution industry inventory ratio would increase freight rates by 0.5%. A 1% increase in the number of new US unemployment benefit claims would increase freight rates by 0.26%. A 1% increase in the port congestion index would decrease freight rates very slightly. This study is significant in that it collected and presented meaningful variables after COVID-19, applied an econometric model to analyze the relationship between independent variables and freight rates, and explored significant variables in the relationship between independent variables that are intricately intertwined.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.105-115
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• 2021

The geological CO2 sequestration in underground geological formation such as deep saline aquifers and depleted hydrocarbon reservoirs is one of the most promising options for reducing the atmospheric CO2 emissions. The process in geological CO2 sequestration involves injection of supercritical CO2 (scCO2) into porous media saturated with pore water and initiates CO2 flooding with immiscible displacement. The CO2 migration and distribution, and, consequently, the displacement efficiency is governed by the interaction of fluids. Especially, the viscous force and capillary force are controlled by geological formation conditions and injection conditions. This study aimed to estimate the effects of surfactant on interfacial tension between the immiscible fluids, scCO2 and porewater, under high pressure and high temperature conditions by using a pair of proxy fluids under standard conditions through pendant drop method. It also aimed to observe migration and distribution patterns of the immiscible fluids and estimate the effects of surfactant concentrations on the displacement efficiency of scCO2. Micromodel experiments were conducted by applying n-hexane and deionized water as proxy fluids for scCO2 and porewater. In order to quantitatively analyze the immiscible displacement phenomena by n-hexane injection in pore network, the images of migration and distribution pattern of the two fluids are acquired through a imaging system. The experimental results revealed that the addition of surfactants sharply reduces the interfacial tension between hexane and deionized water at low concentrations and approaches a constant value as the concentration increases. Also it was found that, by directly affecting the flow path of the flooding fluid at the pore scale in the porous medium, the surfactant showed the identical effect on the displacement efficiency of n-hexane at equilibrium state. The experimental observation results could provide important fundamental information on immiscible displacement of fluids in porous media and suggest the potential to improve the displacement efficiency of scCO2 by using surfactants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.130-132
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• 2012

The use of visual media has been increased by development of contemporary society. To use these information of image, there are various methods of image processing. Edge detection which is one of those is technique to detect dramatically changing part of image brightness. Existing methods detect edge through mask which is composited by constant values. Because existing methods do not consider factor as location, direction of pixel in image, performance of edge detecting in insufficient. Therefore, an algorithm which is using variable vector for the variation of brightness in mask of $3{\times}3$ pixels is proposed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.711-720
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• 2020

This research aims to analyze the factors affecting the acceptance of social commerce, including performance expectancy, effort expectancy, social support, facilitating conditions, hedonic motivation, habitability, price saving orientation, and privacy concerns using the Unified Theory of Acceptance and Use of Technology (UTAUT2). UTAUT2 has been examined and modified in various contexts. The research model studies the acceptance and use of technology in the context of customers. This study adopts a quantitative method using the partial least squares regression (PLS) approach involving 244 respondents. The respondents are users of social commerce in Indonesia. The result of this research indicates that social influence, facilitating conditions, hedonic motivation, habit, price value orientation, and privacy concerns have a significant effect on behavioral intention. On the other hand, performance expectancy and effort expectancy does not affect behavioral intention. Furthermore, price value has a significant effect on social commerce user behavior. Lastly, facilitating conditions and habits does not affect social commerce user behavior. This research contributes to the development of theory by examining an additional variable, which is privacy concern. This study is significant since social media and social commerce have grown exponentially nowadays. Implications of the results for the development of the theory (UTAUT2) and practice are discussed in the article.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.555-564
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• 2020

The purpose of this study is to analyze the efficiency of research institutes of futures companies, and to promote the development of futures market and real economy. This study employs DEA-solver software to conduct super-efficiency data envelopment analysis (SE-DEA), and also selects 40 representative futures research institutes in China as decision-making units (DMUs). For data of input and output indicators, we collect from the China Futures Association, Futures Daily, Hexun.com and Webstock.com respectively, and the time duration is the 103 trading days between from October 2019 to February 2020. Then the indicator for the strategy accuracy rate is calculated separately by analyzing the strategies published by each DMUs in public media. In conclusions, most institutes have excessive investment in human resources, and also have insufficient strategy accuracy rate and insufficient published research reports. The findings of this study suggest that Chinese futures companies need to improve the efficiency of research institutes, and better meet the demand of the financial market. In fact, the analysis of the efficiency of the futures company research institute has not been found in the literature worldwide, Application of DEA model in efficiency analysis of securities and futures research institutions and establishment of indicators are the innovations of this paper.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.159-168
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• 2020

The cryptocurrency market has received immense consideration in media and academia since the beginning of 2013 because of its huge price fluctuation. This study focuses on Arab investors who invest in the cryptocurrency market by investigating the influence of behavioral finance factors on investment decisions in the cryptocurrency market. A quantitative approach was used by employing a snowball sampling method through 112 questionnaires. The results show that herding theory, prospect theory, and heuristic theory have a significant effect on investors' investment decisions in the cryptocurrency market. This emphasizes the significant role of the proposed behavioral factors as determinants of the investors' investment decisions. This study contributes to the existing research by consolidating the results of different researches in this study. It also contributes to the investors' understanding of the dynamics of the cryptocurrency market and it enhances the ability to make informed decisions based on their understanding. The implication of the findings will prepare hit and run investors to be progressively prepared to stay in the cryptocurrency market and develop their abilities on the most proficient method to settle on sound venture choices. Furthermore, the findings of this study will encourage financial specialists to realize that information on the traditional finance theory is not adequate to excel in the cryptocurrency market.