• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1998.10a
• /
• pp.923-926
• /
• 1998

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.1
• /
• pp.112-120
• /
• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

• Journal of Global Scholars of Marketing Science
• /
• v.10
• /
• pp.111-137
• /
• 2002

The purpose of the current study was to examined the effect of the perceived membership and social support on the members' identification in online community. The sample consisted of 189 college students in pusan, korea. The results of the study were as follows. First, the effects of perceived memberships on the online community members' identification was influenced significantly. Second, perceived social support was found to be significantly influenced on the online community members' identification. Third, community members' identification in the online was found to be significantly influenced on relationship-oriented behavior. Finally, perceived membership and social support was found to be significantly influenced on relationship-oriented behavior mediating community identification.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.8C
• /
• pp.772-781
• /
• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• Communications for Statistical Applications and Methods
• /
• v.14 no.1
• /
• pp.133-140
• /
• 2007

The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.

• Journal of Computing Science and Engineering
• /
• v.2 no.3
• /
• pp.249-273
• /
• 2008

Epidemic protocols have two fundamental assumptions. One is the availability of a mechanism that provides each node with a set of log(N) (fanout) nodes to gossip with at each cycle. The other is that the network size N is known to all member nodes. While it may be trivial to support these assumptions in small systems, it is a challenge to realize them in large open dynamic systems, such as peer-to-peer (P2P) systems. Technically, since the most fundamental parameter of epidemic protocols is log(N), without knowing the system size, the protocols will be limited. Further, since the network churn, frequently observed in P2P systems, causes rapid membership changes, providing a different set of log(N) at each cycle is a difficult problem. In order to support the assumptions, the fanout nodes should be selected randomly and uniformly from the entire membership. This paper investigates one possible solution which addresses both problems; providing at each cycle a different set of log(N) nodes selected randomly and uniformly from the entire network under churn, and estimating the dynamic network size in the number of nodes. This solution improves the previously developed distributed algorithm called Shuffle to deal with churn, and utilizes the Shuffle infrastructure to estimate the dynamic network size. The effectiveness of the proposed solution is evaluated by simulation. According to the simulation results, the proposed algorithms successfully handle network churn in providing random log(N0 fanout nodes, and practically and accurately estimate the network size. Overall, this work provides insights in designing epidemic protocols for large scale open dynamic systems, where the protocols behave autonomically.

• Culinary science and hospitality research
• /
• v.16 no.4
• /
• pp.53-63
• /
• 2010

This study intends to examine the effect of hotel culinarian's psychological ownership based upon social exchange relations on his job satisfaction and organizational commitment. There are main results in this research focusing on deluxe hotel restaurants culinarian's in Seoul. The results of analyzing the data obtained from an empirical analysis were as follows: First, the more they perceive team-membership exchange relations and organizational support, the higher rises their psychological ownership. Second, the mire they perceive organizational support, the higher goes their job satisfaction. Third, the more they perceive team-membership exchange relations, leader-membership exchange relations and organizational support, the more they commitment themselves to their organizations, Fourth, the mopre they have psychological ownership, the higher increases their job satisfaction. Fifth, the mire they have psychological ownership, the higher rises their organizational commitment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.279-282
• /
• 2007

Support Vector Machine(SVM)은 통계적 학습 이론에 기반을 둔 분류기이다. 또한 Twin Support Vector Machine(TWSVM)은 이진 SVM 분류기의 한 종류로써, 서로 관련된 두 개의 SVM 유형 문제를 통해 평행하지 않은 두 개의 평면을 결정하고 이 두 평면을 통해 분류기를 완성하는 방식이다. 이러한 방식은 TWSVM은 학습 시간이 SVM에 비해 훨씬 짧으며, SVM과 비교하여 떨어지지 않는 성능을 보여준다. 본 논문은 분류기 입력에 Fuzzy Memvership을 적용하는 방식의 TWSVM을 제안하고, 2차원 벡터 입력에 대한 실험을 통하여 기존에 제시 되었던 TWSVM과 비교한다.

• Asia-Pacific Journal of Business
• /
• v.13 no.3
• /
• pp.135-155
• /
• 2022

Purpose - The purpose of this study is to examine how a broad palette of government support measures and firms' membership in government-developed clusters are related to firms' openness in innovation processes. Design/methodology/approach - Empirically, this study analyzes the Korea Innovation Survey 2018 data on the innovation activities of 1,450 Korean R&D-active manufacturing firms in a three-year period from 2015 through 2017. Findings - The results suggest that firms engage in open innovation to a greater extent--as measured by the breadth of external collaborating partners and of the utilized external sources of knowledge--when they are provided with a broader palette of government support measures and are located in government-developed clusters. However, the effect of diverse government support measures is attenuated for firms located in these clusters. Research implications or Originality - This study contributes to the innovation literature by illuminating how firms' open innovation can be understood in a national innovation system. Moreover, it provides valuable implications for firms seeking to obtain government support and collaborate with others.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.3
• /
• pp.165-170
• /
• 2011

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.