• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.118-121
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• 1996

This paper describes ECG signal labeling based on Fuzzy clustering, which is necessary at automated ECG diagnosis. The NPPA(Non parametric partitioning algorithm) compares the correlations of wave forms, which tends to recognize the same wave forms as different when the wave forms have a little morphological variation. We propose to apply Fuzzy clustering to ECG QRS Complex labeling, which prevents the errors to mistake by using If-then comparision. The process is divided into two parts. The first part is a parameters extraction process from ECG signal, which is composed of filtering, QRS detection by mapping to a phase space by time delay coordinates and generation of characteristic vectors. The second is fuzzy clustering by FCM(Fuzzy c-means), which is composed of a clustering, an assessment of cluster validity and labeling.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.479-484
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• 1992

The resolved motion rate control (RMRC) is converting to Joint space trajectory from given Cartesian space trajectory. The RMRC requires the inverse of Jacobian matrix. Since the Jacobian matrix of the redundant robot is generally not square, the pseudo-inverse must be introduced. However the pseudo-inverse is not easy to be implemented on a digital computer in real time as well as mathematically complex. In this paper, a simple fuzzy resolved motion rate control (FRMRC) that can replace the RMRC using pseudo-inverse of Jacobian is proposed. The proposed FRMRC with appropriate fuzzy rules, membership functions and reasoning method can solve the mapping problem between the spaces without complexity. The mapped Joint space trajectory is sufficiently accurate so that it can be directly used to control redundant manipulators. Simulation results verify the efficiency of the proposed idea.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• 한국지능시스템학회논문지
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• 제17권3호
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• pp.390-396
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• 2007

Fuzzy-CBR Collision Avoidance Support System is a system that finds a solution from past knowledge retrieved from the database and adapted to a new situation. Its algorithm has resulted to an adapting a solution for a new situation. However, ontology is needed in identifying concepts, relations and instances that are involved in a situation in order to improve and facilitate the efficient retrieval of similar cases from the CBR database. This paper proposes the way to apply ontology for identifying the concepts involved in a new environment and use them as inputs, for a ship collision avoidance support system., Similarity will be obtained through document articulation and using abstraction levels. A conceptual model of a maneuvering situation will be built using these ontologies.

• 한국지능시스템학회논문지
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• 제2권1호
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• pp.3-16
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• 1992

In this paper, we establish the conceptes of compactifications of a L-fuzzy topological space and a order relation in these compactifications. This order is a preorder. The existemce problem and the uniqueness problem of the largest compactifications are closely related to the mapping extension problem. We give out the largest compactifications and show the non-uniqueness of the largest compactifications in the preorder for a kind of spaces. Moreover, under some natural assumptions of separation axioms, we prove that the preorder is just a partial order, thus it ensures the uniqueness of the largest compactification. In addition. the related discussion involves the special properties of fuzzy product space, the latter seems to be independent interesting.

• 디지털융복합연구
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• 제11권5호
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• pp.313-318
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• 2013

무선 멀티미디어 센서 네트워크에서 QoS는 스트리밍 미디어 서비스 향상을 위한 중요한 이슈이다. 본 논문에서는 무선 멀티미디어 센서 네트워크의 스트리밍 미디어 QoS 향상을 위한 2-layer 사상 기반의 새로운 QoS 평가 기법을 제안한다. 제안된 기법은 응용 레이어와 네트워크 레이어 간의 스트리밍 제어를 위해 퍼지 적합도를 적용하며, 전송 신뢰도와 처리율을 향상시키기 위하여 2-layer 사상을 수행한다. 시뮬레이션 결과 제안된 기법이 다른 비교 기법들에 비해서 패킷 제어율, 전송 신뢰도, 그리고 지연 오버헤드율의 성능이 효율적임을 보인다.

• 제어로봇시스템학회논문지
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• 제8권2호
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• 지능정보연구
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• 제13권2호
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• pp.55-68
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• 2007

분자 생물학(computational molecular biology) 분야에서 DNA 염기 서열 배치 알고리즘은 다양한 방법으로 개선되어 왔다. 본 논문에서는 기존의 DNA 염기의 품질 정보(quality information)를 이용한 DNA 염기 서열 배치 방법을 개선하기 위하여 퍼지 논리 시스템(fuzzy logic system)과 DNA 염기 서열 단편의 특징을 적용한 품질 정보와 퍼지 추론 기법을 이용한 DNA 염기 서열 배치 알고리즘을 제안한다. 기존의 알고리즘은 Needleman-Wunsch가 제안한 전역 배치 알고리즘에 각 DNA 염기의 품질 정보를 적용하여 DNA 염기 서열 배치 점수를 계산하였다. 그러나 전체 DNA 염기의 품질 정보를 이용하여 계산하기 때문에 DNA 염기 말단 부분의 품질이 낮은 경우에는 DNA 염기 서열 배치 점수를 계산하는 과정에서 오차가 발생한다. 본 논문에서는 기존의 품질 정보를 이용한 알고리즘을 개선하여 DNA 염기 서열의 말단 부위의 품질이 낮은 경우에도 정확히 서열을 배치할 수 있도록 한다. 또한 DNA 염기 서열 단편의 길이와 낮은 품질의 DNA 염기 빈도를 퍼지 논리 시스템에 적용하여 DNA 염기 서열 배치 점수를 계산하는데 적용되는 매핑 점수 인자(parameter)를 동적으로 조정한다. 제안된 알고리즘의 성능 평가를 위해 NCBI(National Center for Biotechnology Information)의 실체 유전체 데이터를 받아 성능을 분석한 결과, 제안된 알고리즘이 기존의 품질 정보만을 이용한 알고리즘 보다 DNA 염기 서열 배치에 있어서 효율적임을 확인하였다.

• 농촌계획
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• 제19권3호
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• pp.51-59
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• 2013

The objective of this study was to make a map of farmland vulnerability to flood inundation based on morphologic characteristics from the flood-damaged areas. Vulnerability mapping based on the records of flood damages has been conducted in four successive steps; data preparation and preprocessing, identification of morphologic criteria, calculation of inundation vulnerability index using a fuzzy membership function, and evaluation of inundation vulnerability. At the first step, three primary digital data at 30-m resolution were produced as follows: digital elevation model, hill slopes map, and distance from water body map. Secondly zonal statistics were conducted from such three raster data to identify geomorphic features in common. Thirdly inundation vulnerability index was defined as the value of 0 to 1 by applying a fuzzy linear membership function to the accumulation of raster data reclassified as 1 for cells satisfying each geomorphic condition. Lastly inundation vulnerability was suggested to be divided into five stages by 0.25 interval i.e. extremely vulnerable, highly vulnerable, normally vulnerable, less vulnerable, and resilient. For a case study of the Jinju, farmlands of $138.6km^2$, about 18% of the whole area of Jinju, were classified as vulnerable to inundation, and about $6.6km^2$ of farmlands with elevation of below 19 m at sea water level, slope of below 3.5 degrees, and within 115 m distance from water body were exposed to extremely vulnerable to inundation. Comparatively Geumsan-myeon and Sabong-myeon were revealed as the most vulnerable to farmland inundation in the Jinju.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.