• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.2
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• pp.63-69
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• 2012

The analysis was conducted for studying the degree of internet addiction and the difference between addicted group and non-addicted group based on usage time. The target group is consisted of 30 people who receive the training at home. The purpose of this training is to reduce digital divide for the disabled with reduced mobility. As a result, the typical user group, less than the 31-point appeared as 4 cases, the potentially dangerous user I group, from 31 to 54 points appeared as 25 cases, the potentially dangerous user II group, from the 54-67 points appeared as only one person. However, there was no game addiction case, more than 67 points. In addition, there was no significant difference in impulsivity and aggression between the high-risk group of 9 persons using internet more than 2.7 hours and the typical user group of 21 persons with less risk. From the result, there are two possibilities. On the one hand, an increase of the usage time for the disabled might not lead to addiction. Or, on the other hand, a measure of addiction for non-disabled might not suitable for the disabled addiction examination.

• Informatization Policy
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• v.24 no.2
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• pp.87-104
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• 2017

The Fourth Industrial Revolution, which is based on the development of information and communication technology (ICT), is expected to replace human knowledge work, which will cause problems of mass unemployment and wide income gap from job polarization. Furthermore, the change is expected to be rapid and wide, demanding proactive measures to respond to such abrupt social changes. However, previous literatures assume that the traditional form of employment will continue and provide limited solutions only. On the other hand, the Fourth Industrial Revolution will enable transition to the Prosumer Economy, which will ultimately lead consumers to become producers through increased job flexibility. If the prosumer economy arrives and the consumers become producers, it will no longer be the matter of finding workplace but rather, the matter of finding the work itself. In this regard, the new technologies of the Fourth Industrial Revolution can be the fundamental solution to such job issues. This paper suggests stable transition to the Prosumer Economy in order to solve the job issues in the age of the Fourth Industrial Revolution. In order to effectively support the process, this paper suggests first, ensuring the amount of education by shortening labor time; second, facilitating life-time education through free online education service; and third, closing the digital divide through mandatory use of the e-government system.

• The Journal of Fisheries Business Administration
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• v.42 no.1
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• pp.97-112
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• 2011

Today, the consumer is more careful in buying goods, invests more time in collecting relevant information to avoid any potential danger, and restricts from potential impulse buying. To react this consumer's carefulness, the seafood brands provide much information including the origin labeling system, the traceability, the food's safety & hygiene. Also the branding by region or company is pursued. Like that, a seafood brand's importance is increased, but there lack few researches dealing how current consumer's attitude influences on real purchase behavior, and how the attitude works consumer purchase decision. Therefore, this study researched the brand's influence on the consumer's attitude and purchase intention. For this purpose, this study targeted the salty mackerel and the dried yellow corvina because they are already branded and sold in some popularity, and researched how a brand's popularity, its image, and its recognized quality could effect on the consumer's attitude and purchase intention. As the result, it was appeared that a seafood brand's popularity didn't directly effect on the consumer's purchase intention, but indirectly influenced through the consumer's attitude as a parameter. From this result, improving a seafood brand's popularity needs some time to form the consumer's positive attitude and to lead to consumer purchase intention of seafood brand. So, it is thought that various promotion activities for seafood consumption must be continually performed rather than some temporary special events. Consumers showed more positive attitude on familiar seafood based on a product's original place and the freshness. Also they had better feeling about some seafood with their speciality images rather than the same kinds of products produced in other regions. This attitude temporarily led to purchase intention. Therefore, it is important that the branding strategy development should start from some seafood familiar to us in traditional food culture and food habit, but should delivery the reliance and the freshness in accurately indicating their origins, and should emphasize their differences as specialities. Consumers showed some positive attitudes on the seafood featuring the hygiene, the safety, continual good quality, and their attitudes led to their purchase intentions in temporary. The seafood product reflecting these results the best is the marketing activities on some Andong salty mackerel products acquired HACCP certification. it is thought that a seafood's branding strategy should be established on distinctive branding strategies using reliable certification mark like HACCP based on the hygiene, the safety, and the quality.

• Journal of The Korean Association of Information Education
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• v.8 no.3
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• pp.293-303
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• 2004

To follow in change and the development which circumference environment of education are quick even from scene of education students form the structure of knowledge themselves, the place where own lead studying of personal small group studying is emphasized, here upon specialist learning activity there is a wild possibility in the model which is suitable. But, studying of the learning paper was most center mainly the specialist learning activity of existing, it solves a learning problem at unit hour to, the hour was too insufficient to solve and it became plentifully at block time. But, this is to the curriculum operation and or the schedule operation it is when trying to consider the intensive degree of learning the elementary student, a problem point there is. It grasps the strong point and a weak point of specialist learning activity model of existing from the research which consequently, it sees and it applies more efficiently from web base study to establish the instructional strategy for, it composed the modules which strengthen the interaction of learning subject for. Also, unit macro learning and block time learning in order to do to become accomplished at web with studying problem, it will be able to solve inside unit hour in order, specialist teaching-learning system based on the web. It developed, after applied in the electrification S elementary school 5 grades which will reach the result, it analyzed.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.191-196
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• 2019

The development of open-pit mines leads to large-area topographical changes in highland forests and can lead to severe deterioration of forests, requiring continuous monitoring. The drone photogrammetry is performed at a lower altitude than the existing manned aerial photogrammetry, and thus has a relatively high accuracy. The purpose of this study is to construct spatial information of large open pit mine using drone photogrammetry and to evaluate the accuracy and utilization of the results. The accuracy of the drone photogrammetric results was 0.018 ~ 0.063m in the horizontal direction and 0.027m ~ 0.088m in the vertical direction. These results satisfy the permissible accuracy of 1: 1,000 digital topographic map and it can be used for open mine monitoring. The geospatial information of the open pit mine can be used in various ways, and it can be used to monitor the quantitative change of a specific area for time series change through data management by periodic data acquisition. If drone photogrammetry is applied to open-pit mine monitoring in the future, work time and cost can be greatly reduced compared to the conventional GNSS or total station method, and the work efficiency can be greatly improved because more visible data can be generated.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.879-891
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• 2007

Forecasting is a critical task for fashion companies because of continuous change in fashion and long process lead-time. Therefore, it is of great importance for both scholars and companies to understand how forecasted fashion styles are accepted by consumers. This research aimed to investigate consumer acceptance of fashion styles in Korea. The study examined and compared oversea collections of women's wear to the street fashion in Korea for seven seasons from 02 s/s to 05 s/s. Information on oversea collections were obtained from the magazine, Fashion Show, and the street fashion information from Seoul Fashion Design Center. The results showed that overall trends presented in oversea collections have been well accepted, while acceptance of specific styles or items varied. During the period of this research, sporty style and feminine style were very strong in the street fashion. Many styles and items were modified and selectively accepted probably because of cultural differences and limitations of mass production. Some styles which were presented in oversea collections were not accepted in Korea, and at the same time some cases were observed only in the street fashion in Korea. The results of this study provide guidelines for Korean apparel companies in merchandise planning and empirical findings to deepen the understanding on Korean society with respect to fashion.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.91-101
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• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Information Systems Review
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• v.14 no.1
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• pp.1-19
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• 2012

A large-scale IS development project takes a long time, thus its project manager needs to be more careful on risk management. In particular, appropriate cutover decision making is critical in large-scale IS development projects because the opening of the large-scale IS significantly impacts the organization. Regardless of its importance, cutover decision making in conventional IS development projects has been done in a quite simple way. Conventional cutover decisions have been made by considering just whether the new IS operates or not from the system, application, and data implementation perspectives. However, this approach may lead to unsatisfactory performance or system failure in complex large-scale IS development. Under this background, we propose a new framework for cutover decision making on large-scale IS projects. To validate the applicability, we applied the framework to a core banking system development case. The case study shows that our framework is effective in proper cutover decision making.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.135-146
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• 2004

For improving the effectiveness of travel information, some rational paths are needed to provide them to users driving in real road network. To meet it, k-shortest path algorithms have been used in general. Although the k-shortest path algorithm can provide several alternative paths, it has inherent limit of heavy overlapping among derived paths, which nay lead to incorrect travel information to the users. In case of considering the network consisting of several turn prohibitions popularly adopted in real world network, it makes difficult for the traditional network optimization technique to deal with. Banned and penalized turns are not described appropriately for in the standard node/link method of network definition with intersections represented by nodes only. Such problem could be solved by expansion technique adding extra links and nodes to the network for describing turn penalties, but this method could not apply to large networks as well as dynamic case due to its overwhelming additional works. This paper proposes a link-based shortest path algorithm for the travel information in real road network where exists turn prohibitions. It enables to provide efficient alternative paths under consideration of overlaps among paths. The algorithm builds each path based on the degree of overlapping between each path and stops building new path when the degree of overlapping ratio exceeds its criterion. Because proposed algorithm builds the shortest path based on the link-end cost instead or node cost and constructs path between origin and destination by link connection, the network expansion does not require. Thus it is possible to save the time or network modification and of computer running. Some numerical examples are used for test of the model proposed in the paper.