• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.185-191
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• 2003

The aim of this research is to overall analyze/classify characteristics of Asian major ports. To achieve this aim, we firstly pointed out critical problems on research methodology and research scope which most of previous research have, from related literature review. In order to overcome those problems, major ports in A냠 were selected by the objective indicators, and both algorithms of AHP(Analytic Hierarchical Process) and FCM(Fuzzy C-Means) that revise weakness in previous clustering method were used. Through these hybrid approach, it were found that only 10 ports of 16 major Asian ports had their own phases in Asian major ports. Those 10 ports were classified into 6 port groups, and also membership degree of each port within the 4 port groups and ranking of each ports seer analyzed. Finally, based on results of these analysis, present status and future direction of Busan port were discussed.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.587-592
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• 2003

Many real world control problems have continuous states and actions. When the state space is continuous, the reinforcement learning problems involve very large state space and suffer from memory and time for learning all individual state-action values. These problems need function approximators that reason action about new state from previously experienced states. We introduce Fuzzy Q-Map that is a function approximators for 1 - step Q-learning and is based on fuzzy clustering. Fuzzy Q-Map groups similar states and chooses an action and refers Q value according to membership degree. The centroid and Q value of winner cluster is updated using membership degree and TD(Temporal Difference) error. We applied Fuzzy Q-Map to the mountain car problem and acquired accelerated learning speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.637-644
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• 2011

Being digitalized, the music can be easily purchased and delivered to the users. However, there is still some difficulty to find the music which fits to someone's taste using traditional music information search based on musician, genre, tittle, album title and so on. In order to reduce the difficulty, the contents-based or the emotion-based music retrieval has been proposed and developed. In this paper, we propose new method to determine the importance of MPEG-7 low-level audio descriptors which are multi-dimensional vectors for the emotion-based music retrieval. We measured the mutual similarities of musics which represent a pair of emotions expressed by opposite meaning in terms of each multi-dimensional descriptor. Then rough approximation, and inter- and intra similarity ratio from the similarity relation are used for determining the importance of a descriptor, respectively. The set of weights based on the importance decides the aggregated similarity measure, by which emotion-based music retrieval can be achieved. The proposed method shows better result than previous method in terms of the average number of satisfactory musics in the experiment emotion-based retrieval based on content-based search.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.343-345
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• 2016

본 논문에서는 아동이 그린 그림에 대해 ART2 알고리즘을 적용하여 색채 정보를 군집화하고, 군집화 된 색채 정보의 중심 벡터 값들을 퍼지화 한다. 퍼지화 된 색채 정보의 소속도를 퍼지 추론 규칙에 적용한 후에 비퍼지화 한다. 비퍼지화 된 결과를 적용하여 아동의 심리 상태를 분석한다. 제안된 방법을 실험하여 알슐러와 해트윅(Alschuler and Hattwick)의 색채에 따른 심리 상태와 비교한 결과, 제안된 심리 분석 방법이 알슐러와 해트윅의 색채에 따른 심리 상태 분석 결과와 거의 일치하는 것을 확인하였다.

• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1136-1140
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• 2009

지상강우 관측망을 이용한 강우량 측정의 대안으로서 사용되는 기상 레이더를 활용한 강우량 추정의 경우, Z-R 방정식을 이용하여 반사도를 강우량으로 환산하는 방법을 일반적으로 사용한다. 이때 발생하는 각종 오차는 레이더 장비가 가지는 기계적인 오차뿐만 아니라 Z-R 방정식이 가지는 오차 등이 있으며, 이를 보정하기 위해서 레이더를 활용하여 추정된 강우량에 지상강우량계와 레이더강우량과의 비율인 G/R비를 보정하는 방법을 일반적으로 사용한다. 본 연구에서는 이와 같이 레이더 강우량을 보정하기 위해서 사용되는 G/R비를 산정하는데 미치는 지형적인 효과를 고려하기 위해서 광덕산 레이더 유효범위 100km 내(군사분계선 이북 미포함)의 지역에 대하여 군집분석을 실시하여 크게 산악지역과 평야지역으로 구분하고, 각각 구분된 지역에 대하여 G/R 비를 산정하여 초기추정 레이더 강우량에 곱하는 mean-field bias 보정을 실시하였다. 광덕산 레이더 기상관측소의 유효범위 100km 내의 2007년, 2008년 홍수기(6/21${\sim}$9/20)기간 동안 94개 Automatic Weather Station(AWS)지점에 대하여 크게 산악지역과 평야지역으로 지역화 시키는 방법은 비계층적 군집분석 기법 중 fuzzy-c mean 방법을 적용하였다. 또한 광덕산 레이더 반사도 기본 자료는 차폐영역으로 생기는 반사도 데이터 누락을 보완하기 위하여 0도와 1.5도 sweep 합성 10분단위 uf 자료를 사용하였으며, AWS와 보정이 이루어지는 레이더 격자의 크기는 최대 4km${\times}$4km로 선정하였다. 본 연구에 있어서 검증방법은 지역을 구분하기 전과 후를 AWS 실측 관측값과 절대상대오차, 평균제곱근 오차로써 비교하였다.