• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.235-242
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• 2020

This paper proposes a system that can manage and verify personal career information through a block chain to solve the problem of getting a job by forging an individual's career when hiring employees. Blockchain network uses private network, and inside the block, the user's academic and career information is kept. The functions of the block chain perform the functions of block creation, block internal data retrieval, career and academic verification, which works through chain code. As a result of the performance evaluation of the proposed system, the processing time per transaction was measured at approximately 110 ms and the search time was measured at 10 ms, and it was applied to the actual system to confirm that it was available.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.11B no.2
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• pp.51-58
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• 2001

This paper proposes a new strategy to estimate the rotor flux of an induction machine for the direct field oriented control. Electrical model of the induction machine presents the basic idea based on observer structure, which is composed of voltage model and current model. But the former has the defects in low speed range, the latter has the defects of sensitivity to machine parameters. In spite of these shortcomings, the closed loop flux observer based on two models has been prevalent estimation method for the direct field oriented control. In this paper, generalized analysis method named "observer characteristic function method"is proposed to analyze the kinds of the linear flux observers in unified form. With the observer characteristic function, the estimated rotor flux error involved in the classical methods can be easily clarified. Moreover, the novel rotor flux observer based on this analysis is also presented and the effectiveness of the observer has been verified by the simulation and experimental results.

• The Journal of Information Technology
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• v.3 no.3
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• pp.39-55
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• 2000

Internet Trade is the new type of business transaction, which sells and advertises the product and services by using the Internet, which is spreaded rapidly to the world. Under these circumstance, our country should discuss the proper position and role as the center in the trade amount in the world trade market, and face the irresistible changes of trade environment. First, in the institution and legal aspect. Second, the price payment system of the internet trade. Third, the so-called infra construction, the physical sector for activating the internet trade, the comprehensive plan of super-highway information communication net work by the government authorities is expected to be promoted. There is no perfect solution that internet trade could be completely done in front of monitor and will be solved one by one along with the development of electronic commerce. At last, the government's and industry's joint positive concern and participation in the rapidly changing new world trade trends of internet trade is expected fit for our position in the world.

• Asia pacific journal of information systems
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• v.10 no.4
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• pp.1-20
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• 2000

Electronic Data Interchange(EDI) has the potential to improve business operations by expediting the exchange of business documents. It will also provide substantive operational and strategic benefits to the trading firms. However, the successful implementation of EDI systems requires the mutual trust and cooperation between the trading firms. The extent of EDI diffusion and performance depends on inter-organizational, intra-organizational, as well as innovation factors. Researches based on the sociopolitical process framework in the use of IT, organizational theory, resource dependence theory, and innovation diffusion theory have identified 3 inter-organizational variables(transaction climate, dependence, external IS expert support) and 4 intra-organizational variables(strategic IS planning, infrastructure, top management support, education/training,), and 3 innovation variables(compatibility, relative advantage, cost) that affect EDI diffusion. In this study, a multi-dimensional measure on EDI diffusion has been developed to capture the external and internal integration. Then, the influence of these 10 variables on the extent to which the EDI adopting firms pursue diffusion has been examined. Whether more diffusion leads to superior performance has also been studied. International trade managers from 107 firms in the trade industry participated in a field survey. The results based on a structural equation model(SEM), developed using AMOS, provide quite a strong support for the hypothesized relations. Both education/training and IT infrastructure influenced external and internal diffusion of EDI systems. Internal diffusion of EDI enables the adopting firms to improve operational and strategic performance, whereas external diffusion contributes only to operational performance.

• The Korean Journal of Archival Studies
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• no.6
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• pp.3-40
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• 2002

Appraisal is a basic archival function that analyzes values of records and determines the eventual disposal of records based upon their archival values. In Korea, this appraisal concept introduces in earnest through Public Records and Archives Act(PRA, 공공기관의 기록물관리에관한법률) with which Korean record management systems settle inflexibly. In theoreical and methodological area, therefore, it is necessary to analyze appraisal system in this Act with it in archival science. In PRA Act, appraisal system is founded on the Tables of Transaction for Records Scheduling(TRS, 기록물분류기준표) through which disposal activities of all records are definited in a concrete form. In this system, selection of archival materials which has been recognized as a important function of record center is carried out by record creators and archival institutions; Primary value between semi-currenty and non-currenty are reflected at the same time. In view of values, this appraisal system intends to separate reasonably consideration for continuing utility of achives from current use of records throughout agencies duration. Ultimately, appraisal based upon TRS makes up not separated management course but organic courses reflecting the Continuum of Care. Of course, this appraisal system makes up the deficiency partially. TRS regarded as 'mainboard' of current appraisal system will have to be enacted elaborately. And appraisal strategies of electronic records must set up in detail in PRA Act and TRS. Lastly, arrangement and description concepts immanent in TRS will have to supplement in archival institutions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.718-723
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• 2018

In a positive Internet of Medical Things (IoMT) environment, by combining the latest computer network technology with IoT technology, remote health care such as health care and monitoring is improved through the provision of quality medical information services. In this paper, we identified and compared the platforms applied with blockchain and presented the results of developing the product distribution de-centralized DApp. In the process, we developed a distribution platform that can use blockchain technology to identify product fraud, manage data, manage customers' information, prevent forgery, track transaction history, and facilitate product transactions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.127-132
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• 2021

This paper applies a machine learning model to forecasting freight rates in dry bulk and tanker markets with wavelet decomposition and empirical mode decomposition because they can refect both information scattered in the time and frequency domain. The decomposition with wavelet is outperformed for the dry bulk market, and EMD is the more proper model in the tanker market. This result provides market players with a practical short-term forecasting method. This study contributes to expanding a variety of predictive methodologies for one of the highly volatile markets. Furthermore, the proposed model is expected to improve the quality of decision-making in spot freight trading, which is the most frequent transaction in the shipping industry.

• SUVANNABHUMI
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• v.13 no.2
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• pp.7-35
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• 2021

This research is an attempt to understand the economic and social consequences that are occurring in Indonesia due to the spread of COVID-19. Indonesia, which has maintained solid economic growth since the inauguration of President Jokowi's government, is also experiencing difficulties to deal with unexpected COVID-19 pandemic as the global economic turmoil has had a very significant impact on its economy. The economic impact of COVID-19 can be felt, starting from the phenomenon of panic buying, the free fall of the stock price index, the depreciation of the Rupiah against the Dollar, sluggish activities in the processing industry, and ultimately it has an impact on slowing economic growth. Various policies and measures have been taken by the Indonesian government to minimize the negative impact caused by the COVID-19 pandemic on the economy. One such area is electronic commerce business or e-commerce that witnessed a vast increase of online and non-cash transaction amid rising voices that the country needs to prepare for the advent of a new economic system, the so-called New Normal era. The Covid-19 pandemic will temporarily slow economic growth and delay some development projects and policy initiatives as the Indonesian government diverts capital from infrastructure development to help respond to the crisis. However, the Jokowi administration's efforts for continuous reform are expected to accelerate the transition to the digital economy.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.