• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.01a
• /
• pp.69-72
• /
• 2018

최근 증가하고 있는 자유개별여행자들의 경우 항공권, 숙박 등의 여행상품을 개별로 구매하고 여행지에서 지역 여행정보를 통해 여행상품을 구매한다. 일반적인 여행 앱에서는 여행자에게 일괄적으로 관광 상품 메시지를 전송한다. 하지만 해당 여행지에 대한 정보 메시지를 여행 시간대별로 받는다면 여행자는 그 정보를 토대로 좀 더 다양한 여행을 즐길 수 있으며, 해당 지역의 여행 상품을 판매하는 판매자의 경우 효과적인 홍보 마케팅이 이루어질 것으로 사료된다. 따라서 본 연구에서는 해당 여행지를 여행하고 있는 여행자에게 스케줄링된 여행정보를 주기적으로 전송할 수 있는 FCM기반의 푸시서비스 개발을 목적으로 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.353-358
• /
• 2008

청각장애인들은 건청인에 비해 의사소통의 기회가 적어 원만한 상호관계를 유지하는데 어려움이 있다. 이러한 문제는 청각장애인들이 구화를 대신해 몸짓이나 손짓을 이용하여 의사를 전달하는 수화를 건청인들이 대부분 습득하고 있지 않아 청각장애인들과 의사소통이 거의 불가능 한 것이 현실이다. 따라서 본 논문에서는 건청인과 청각장애인들 간의 의사소통을 가능하게 하기 위한 전단계로 FCM 알고리즘을 이용한 지화 인식 방법을 제안한다. 제안된 방법은 화상 카메라를 통해 얻어진 영상에서 YCbCr 컬러 공간과 HSI 컬러 공간을 이용하여 피부영역을 검출한 후 추출된 피부영역을 4 방향 윤곽선 추적 알고리즘을 적용하여 두 손의 위치를 추적한다. 그리고 추적한 두 손의 영역에 대해 형태학적 정보를 이용하여 잡음을 제거한 후, 최종적으로 두 손의 영역을 추출한다. 추출된 손의 영역은 FCM 알고리즘을 적용하여 지화의 특징들을 분류하고 인식한다. 제안된 방법의 성능을 평가하기 위해 화상카메라에서 획득한 지화 영상을 대상으로 실험한 결과, 두 손 영역의 추출과 지화 인식에 있어서 효과적인 것을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.7
• /
• pp.638-643
• /
• 2012

This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2003.11a
• /
• pp.239-250
• /
• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.2
• /
• pp.49-57
• /
• 2012

Conventional fuzzy c-means (FCM) algorithms have achieved a good clustering performance. However, they do not fully utilize the spatial information in the image and this results in lower clustering performance for images that have low contrast, vague boundaries, and noises. To overcome this issue, we propose an enhanced spatial fuzzy c-means (ESFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors in a $3{\times}3$ square window. To evaluate between the proposed ESFCM and various FCM based segmentation algorithms, we utilized clustering validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), and Xie-Bdni function ($V_{xb}$). Experimental results show that the proposed ESFCM outperforms other FCM based algorithms in terms of clustering validity functions.

• Nuclear Engineering and Technology
• /
• v.52 no.4
• /
• pp.689-699
• /
• 2020

In this work, new multi-recycling options of TRU nuclides using PWR fuel assemblies comprised of MOX and FCM (Fully Ceramic Micro Encapsulated) fuels are suggested and neutronically analyzed. These options do not use a fully recycling of TRU but a partial recycling where TRUs from MOX fuels are recycled while the ones from FCM fuels are not recycled due to their high consumption rate resulted from high burnup. In particular, additional external TRU feed in MOX fuels for each cycle was considered to significantly increase the TRU consumption rate and the finally selected option is to use external TRU and enriched uranium feed as a makeup for the heavy metal consumption in MOX fuels. This hybrid external feeding of TRU and enriched uranium in MOX fuel was shown to be very effective in significantly increasing TRU consumption rate, maintaining long cycle length, and achieving negative void reactivity worth during recycling.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.461-467
• /
• 1998

Although many methods of knowledge acquisition has been developed in the expert systems field, such a need for causal knowledge acquisition has not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approcach, we prototyped a causal knowledge-driven inference engine named CAKES and then experime ted with some illustrative examples.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.8
• /
• pp.3689-3696
• /
• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.1
• /
• pp.378-384
• /
• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.12
• /
• pp.1548-1558
• /
• 1988

In this paper, a segmentation algorithm for color images based on the scale-space filter and the Fuzzy c-means (FCM) techniques is proposed. The methodology uses a coarse-fine concept to reduce the computational burden required for the FCM. The coarse segmentation attempts to segment coarsely using a thresholding technique, while a fine segmentation assigns the unclassified pixels by a coarse segmentation to the closest class using the FCM. Attempts also have been made to compare the performance of the proposed algorithm with other algorithms such as Ohlander's, Rosenfeld's, and Bezdek's. Intensive computer simulations has been done and the results are discussed in the paper. The simulation results indicate that the proposed algorithm produces the most accurate segmentation on the O-K-S color coordinate while requiring a reasonable amount of computational effort.