• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1899_1900
• /
• 2009

본 논문에서는 주어진 데이터의 입자화 특성을 효과적으로 모델 구축에 반영하고자 재구성 평가 기준을 통한 새로운 형태의 입자화 기반 RBF 뉴럴 네트워크를 개발한다. 주어진 데이터들의 입자화 특성을 파악하기 위해서 새로운 형태의 FCM 클러스터링(-Context-based fuzzy clustering)을 이용한다. 즉, 출력 공간의 입자화 특성은 K-means clustering 방법을 사용한 것에 반해, 입력 공간에서의 정보들은 Context-based fuzzy clustering 방법을 이용하여 효율적으로 데이터의 특성을 파악하여 모델의 구축에 반영하였으며, 또한 모델의 최적화를 위하여 RBF 뉴럴 네트워크의 은닉층의 수를 재구성 평가 기준을 통하여 모델의 최적화를 꾀하였다. 제안된 모델의 효율적인 특성을 보여주기 위해 저차원 합성 데이터를 이용하여 모델을 평가한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.1
• /
• pp.125-132
• /
• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• Journal of Korea Multimedia Society
• /
• v.13 no.1
• /
• pp.133-142
• /
• 2010

As the ubiquitous technology has penetrated into almost every aspect of modern life, the research of the security technology to solve the weakness of security in the ubiquitous environment is received much attention. Because, however, today's security systems are usually based on the fixed rules, many security systems can not handle diverse situations in the ubiquitous environment appropriately. Although many existing researches on context aware security service are based on ACL (Access Control List) or RBAC (Role Based Access Control), they have an overhead in the management of security policy and can not manipulate unexpected situations. Therefore, in this paper, we propose a context-aware security service providing multiple authentications and authorization from a security level which is decided dynamically in a context-aware environment using FCM (Fuzzy C-Means) clustering algorithm and Fuzzy Decision Tree. We show proposed model can solve typical conflict problems of RBAC system due to the fixed rules and improve overhead problem in the security policy management. We expect to apply the proposed model to the various applications using contextual information of the user such as healthcare system, rescue systems, and so on.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.147-157
• /
• 2017

In recent years, Super-pixels have become very popular for use in computer vision applications. Super-pixel algorithm transforms pixels into perceptually feasible regions to reduce stiff features of grid pixel. In particular, super-pixels are useful to depth estimation, skeleton works, body labeling, and feature localization, etc. But, it is not easy to generate a good super-pixel partition for doing these tasks. Especially, super-pixels do not satisfy more meaningful features in view of the gestalt aspects such as non-sum, continuation, closure, perceptual constancy. In this paper, we suggest an advanced algorithm which combines simple linear iterative clustering with fuzzy clustering concepts. Simple linear iterative clustering technique has high adherence to image boundaries, speed, memory efficient than conventional methods. But, it does not suggest good compact and regular property to the super-pixel shapes in context of gestalt aspects. Fuzzy similarity measures provide a reasonable graph in view of bounded size and few neighbors. Thus, more compact and regular pixels are obtained, and can extract locally relevant features. Simulation shows that fuzzy similarity based super-pixel building represents natural features as the manner in which humans decompose images.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.2
• /
• pp.110-115
• /
• 2006

In pure P2P environment, it is possible to provide service by using a little real-time information without using accumulated information. But in case of using only a little information that was locally collected, quality of recommendation service can be fallen-off. Therefore, it is necessary to study a method to improve qualify of recommendation service by using users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information per each service field and classifying it per each user, using SOM. In addition, we could recommend proper services for users by quantifying the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.2
• /
• pp.436-444
• /
• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.340-343
• /
• 2006

본 논문은 스마트 홈에서 베이지안 네트워크에 기반을 둔 보편성을 가지는 상황인식 시스템의 구현방법을 제안한다. 상호정보를 사용하여 베이지안 네트워크의 구조 학습을 하고, 보다 효율적인 데이터 처리를 위해서 퍼지 클러스터링을 사용하는 방법을 도입한다. 마지막으로 시뮬레이터를 통하여 자료 취득 및 상황인식의 결과를 보인다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.1527-1530
• /
• 2009

유비쿼터스 환경의 확산에 따른 다양한 보안문제의 발생은 센서의 정보를 이용한 상황인식 보안 서비스의 필요성을 증대시키고 있다. 본 논문에서는 FCM (Fuzzy C-Means) 클러스터링과 다변량 퍼지 결정트리 (Multivariate Fuzzy Decision Tree)를 이용하여 센서의 정보를 분류함으로써 사용자의 상황을 인식하고, 사용자가 처한 상황에 따라 다양한 수준의 보안기술을 유연하게 적용할 수 있는 상황인식 보안 서비스를 제안한다. 제안 모델은 기존에 많이 연구되어 오던 고정된 규칙을 기반으로 하는 RBAC(Role-Based Access Control)계열의 모델보다 더욱 유연하고 적합한 결과를 보여주고 있다.

• Journal of Korea Port Economic Association
• /
• v.33 no.4
• /
• pp.17-36
• /
• 2017

In the modern economic context, it is apparent that there is a strong focus on the importance of global shipping industry. Recently, the world economic crisis has negatively influenced the industry with regard to both supply and demand, which has seen almost no sign of recovery. The fact that the entire industry is operating with low efficiency and at a low profit state has made all stakeholders anxious. This research examines the financial performance of the world's major shipping lines in order to give maritime stakeholders a closer look into the industry behind the ranking. Besides, the research evaluates the competitiveness of shipping companies in terms of financial ability and suggestions for strategic actions to stakeholders are provided. For these purposes, Fuzzy-C Means is used to cluster the selected lines into different groups based on their financial indices, namely liquidity, asset management, debt management and profitability. Levene's tests which are then followed by ANOVA tests are also utilized to assess the robustness of the clustering outcomes. The results indicate that liquidity, solvency and profitability act as the main criteria in the classification problem.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.471-473
• /
• 2010

본 논문은 선형회귀(LR: Linear Regression)와 국부적인 방사기저함수 네트워크(RBFN: Radial Basis Function Networks)를 결합한 점진적인 모델(incremental model)의 설계와 관련되어진다. 전형적인 RBFN에 의한 모델링과는 달리, 제안된 방법의 근본적인 원리는 두 단계에 의해 고려되어진다. 첫째, 전체 모델의 설계과정에서 전역적인 모델로써 선형회귀에 의해 데이터의 선형부분을 구축한다. 다음으로, 모델링 오차는 오차가 존재하는 국부적인 공간에서 RBFN에 의해 보상되어진다. 여기서, 오차의 분포로부터 RBFN을 설계하기 위해 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)를 통해 정보입자의 형태로 구축되어진다. 실험은 자동차 mpg 연료소비량 예측과 부동산 가격예측문제를 통해 제안된 방법의 우수성을 증명한다.