• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.17-23
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• 2011

Proportional control solenoid is a type of modulating valve that can continuously control the valve position with magnetic force of solenoid. Recent microcontroller based digital servocontroller for proportional valve is being developed toward the smart valve with additional features such as enhanced control algorithm for finer process and intelligent on-board diagnosis for maintenance. In this paper, development of servocontroller network control with CAN bus which is free from problems of security and network traffic jam is presented. Design of network control system includes modes of communication between master and slave, assignment of 29bit message identifier and message objects, transaction of communication sequence, etc. Monitoring function and control experiments for remote valve through CAN network prove the extended function of smart valve control system.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.1765-1768
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• 2002

최근 들어 웹사이트 개인화(Web Personalization)에 관한 연구가 활발히 진행되고 있다. 웹 개인화는 클러스터링과 같은 데이터 마이닝 기법을 이용하여 개개의 사용자에게 가장 흥미를 갖을만한 URLs의 집합을 예측하는 것이라 할 수 있다. 기존에는 웹 트랜잭션을 클러스터링 하기 위해서 사용자의 방문여부에 따라 트랜잭션을 비트벡터(bit vector)로 표현하였다. 하지만 이것은 웹 트랜잭션의 클러스터링에 있어서 사용자의 흥미를 배제하고 단순히 방문여부만을 반영하게 된다. 이에 본 논문에서는 사용자의 흥미도(Interestingness)를 반영할 수 있도록 보완된 웹 트랜잭션 모델을 제시하고 제안된 트랜잭션 모델을 적용한 유사도 비교방법을 제안한다. 그리고 성능평가를 통하여 제안한 방법이 기존 방법에 비해 클러스터링의 정확성을 높임을 보인다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.81-90
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• 2012

For digital TV, the recommendation of u-health personalized service of semantic environment should be done after evaluating individual physical condition, illness and health condition. The existing recommendation method of u-health personalized service of semantic environment had low user satisfaction because its recommendation was dependent on ontology for analyzing significance. We propose the personalized service recommendation method based on Naive Bayesian Classifier for u-health service of semantic environment in digital TV. In accordance with the proposed method, the condition data is inferred by using ontology, and the transaction is saved. By applying naive bayesian classifier that uses preference information, the service is provided after inferring based on user preference information and transaction formed from ontology. The service inferred based on naive bayesian classifier shows higher precision and recall ratio of the contents recommendation rather than the existing method.

• Asia pacific journal of information systems
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• v.9 no.2
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• pp.77-97
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• 1999

industry each which are using a kind of EDI, supplier-oriented Electronic Data Interchange(SOEDI) to procure materials, equipment and supplies more effectively. The major objective of this study are to determine the basic nature of the impact of SOEDI on the structure and procedure in purchasing. The other is to identify the variables or determinants which influence the benefits which can be experienced in purchasing organizations. This study was conducted through interviewing of organizations which are currently active in the use of EDI. Samples of 107 purchasing organizations were collected. Some key findings of this study are ; 1) There were remarkable differences on influencing level and relationship of determinants which influence the benefits of purchasing organizations. There was the most remarkable one in trading industry. 2) The more suppliers, the more benefits. The transaction rate with suppliers who are affiliated with EDI is low as a whole but suppliers who are affiliated with EDI can be a critical factor on selection of buyers. 3) Procedure related to purchasing order was mainly changed. There is a development on signature but still exists. 4) The number of employees were reduced a little bit.

• Journal of Distribution Research
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• v.1 no.1
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• pp.83-107
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• 1996

The concern of external environment is growing up in the field of channel distribution. In the former channel distribution, the channel distribution environment is little bit unmoveable owing to a powerful manufacture control against poor distributor. Therefore intra-efficiency is channel member's core interest. The structure of channel governance, however, come to be changable because of the mature distributor power against manufacturer such as a Price Break, JIT of channel governance, and a serial of change. Accordingly, it is acceptable that the interest of external-environment of channel members' is more and more enlarged, and external-environment change in the channel distribution make the serious problems in intraorganizational system. Thus, it is meaningful that this study try to discover the consumer power as external environment factor and to find the best strategy to overcome this consumer power. Resource dependence theory, Transaction cost theory, Political Economic Approach, and Working partnership Approach are the theory foundation of the reasearch. Apparel franchise is a sample to analyse the hypothesis and correlation and multi-regression are a chief tools to estimate the hypothesis. Thus, the above results imply that a flexible governance is appropriate to consumer power, conflict is not intervening value between consumer power and channel governance, and the channel member's satisfaction can be confirmed in the flexible governance better than control governance.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.157-180
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• 2010

Rapid development of information technology has generated a new industry and market. In particular, network technology such as the Internet and other computer networks made transaction activities switched from traditional offline commerce to e-commerce. Among them, digital content is bit-based object which is created and distributed through electronic environment. In particular, many entertainment contents such as the music, the movies, and the computer game softwares are main products. Although digital content commerce has high potential demand, it lacks the consideration about the factor related to maintaining existing customer such as customer loyalty and switching costs. There has been a number of research on customer loyalty and other factors affecting it in the traditional electronic commerce environment, but there is a lack of research which examines the characteristics of digital content. The study about the effect of switching costs on customer loyalty in digital content commerce is necessary because the customers of digital content commerce market are from those of other e-commerce market or traditional offline commerce market. In addition, customer loyalty and switching costs are important factors because they may build up greater customer retention. For that reason, this study focused on examining the relationships among switching costs, antecedents for switching costs, and customer loyalty in online digital music service industry. The study has three major purposes: (1) to find antecedents of switching costs on digital content commerce and examine effect of antecedents for switching costs; (2) to identify effect of switching costs on customer loyalty in digital content commerce and examine moderating effects of alternative attractiveness; (3) to identify the differences of antecedents for switching costs by contents transmission type(streaming service and downloading service). And, the online digital music service industry is selected in this study since there are many users and transactions incurring. To accomplish these purposes, a survey questionnaire was developed and distributed to 256 informants. Survey instrument was developed based on previous research and pre-established survey items. Total of 206 surveys are collected and used in the data analysis. Among the respondents, 56.8% is male and 43.2% is female. Also, 86 responses were streaming service user group and 120 responses were download service user group. These data was analyzed using regression analysis. Major findings of empirical analysis can be summarized as follows. First, switching costs have positive effect on customer loyalty in digital content commerce environment. Second, the influence of switching costs on customer loyalty increases under conditions of high alternative attractiveness. Third, DRM convenience and breadth of use have positive effect on switching costs. The findings imply that the digital content provider should pay more attention to switching costs in addition to customer satisfaction in order to attract customers. Also, increasing the convenience of DRM use by securing the convenience of user interface and expanding the support device and increasing the service use scope by providing diverse value-added service helps to create a switching barrier. The result of the study can become a practical use in marketing strategy for maintaining existing customer. In particular, switching barrier is very important under conditions of high competition in the online music service market. This study can be used as a basis for further studies about customer retention in digital content commerce.