• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• 대한전자공학회논문지SD
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• 제44권3호
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• pp.12-19
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• 2007

본 논문에서는 피사체 인식 및 자동 선택이 가능한 모바일 폰 용 자동초점 알고리즘을 제안하였다. 제안된 알고리즘은 피사체 인식 단계와 목적물 자동 선택 단계로 구성된다. 피사체 인식 단계에서는 화면 전체에 배치된 다중 소형 측거점과 초점값 연산자를 사용하여 복수개의 피사체를 모바일 폰에 내장된 이미지 센서만을 사용하여 인식함으로써 기존의 적외선이나 초음파와 같은 외부 장치를 사용하는 방식과 달리 모바일 폰에서의 피사체 인식을 가능케 하고자 하였다 목적물 자동 선택 단계에서는 퍼지 멤버십 변수와 퍼지 추론을 통해 사용자가 촬영하고자 하는 목적물을 자동 선택하는데 이는 사진 기술이 없는 사용자라도 선명한 화질의 디지털 이미지를 획득할 수 있도록 하기 위함이다. 제안된 알고리즘은 프로그램 언어로 구현되었으며, 초점 거리 제어가 가능한 CCD 카메라와 PC를 사용하여 실시간으로 이미지를 분석, 검증하였다.

• 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.

• 전자공학회논문지B
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• 제33B권10호
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• pp.80-89
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• 1996

This paper proposes a new COA (center of area) defuzzifier that is working in the accurate and fast manner. The proposed COA defuzzifier involves both membership values and the spans of membership functions in clauclating a crisp value. In additon, it avoid division by replacing the COA calculation with the searching of the momentum equilibrium point. The moment equilibrium point is searched in the coarse-to-fine manner such that the moment computing points during the coarse searching are moved in the interval of fuzzy terms until they are reached at two adjacent fuzzy terms searching method accerlates the finding of the moment equilibrium point by O(M) mazimally when compared iwth the equal interval searching method of ruitz. In order to verify the accuracy of the proposed COA defuzzifier, the crisp values obtained form the proposed coarse-to-fine searching are compared with the precise crisp values from the arithmetic calculation. Application to the truck backer-upper control problem of the proposed COA defuzzifier is presented. The control performance is compared with that of the conventional COA defuzzifier in tems of the average tracing distance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.127-130
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• 1995

Fuzzy controllers have proven to be powerful in controlling dynamic processes where mathematical models are unknown or intractable and ill-defined. The way of improving the performance of a fuzzy controller is based on making up rules, constructing membership functions, selecting a defuzzification method and adjusting input-output scaling factors. But there are many difficulties in tuning those to optimize a fuzzy controller. So, in this paper, we propose the look-up table based self-orgenizing fuzzy controller (LSOFC) which optimizes look-up values resulting from the above fuzzy processes. We use the plus-minus tuning method(PMTM), scanning the value through the processes of addition and subtraction. Simulation results demonstrate that the performance of LSOFC is far better than that of a non-tuning fuzzy controller.

• 공학논문집
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• 제2권1호
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• pp.13-21
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• 1997

본 연구는 연속음성을 인식하기 위하여 특징 Parameter의 변동성을 Fuzzy 변수로 취하여 Membership 함수로 표현한 후, Fuzzy 추론으로 연속음성을 인식하는 연구이다. 특징 Parameter로는 Formant 주파수, Pitch, Log Energy, Zero Crossing Rate등을 사용한다. 연속음성의 Data로서는 한국어의 연속음성을 대상으로 하여 음성인식 system을 구현한다음, 인식실험을 통하여 본 연구의 유교성을 확인한다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1997년도 추계학술대회 발표논문집:21세기를 향한 정보통신 기술의 전망
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• pp.105-116
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• 1997

The fuzzy logic controller was used effectively for the controlling of unmeasured parameter and inaccurate mathematical modeling was designed. In this paper, we proposed that the method of reducing the vibration at the target point on calculating the membership degree about input value without a look-up table was proposed. And its effectiveness in the inverted pendulum was acquired.

• 한국컴퓨터정보학회논문지
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• 제10권1호
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• pp.67-72
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• 2005

대부분의 이진화 알고리즘은 임계치를 결정하기 위하여 히스토그램을 사용하여 밝기 분포를 분석한다. 배경과 물체의 명도차이가 큰 경우에는 양봉 형태의 히스토그램이 나타나며 최적의 임계치를 찾기 위해 히스토그램 골짜기를 선택하는 것만으로도 양호한 임계치 결과를 얻을 수 있다. 반면에 배경과 물체의 밝기 차이가 크지 않거나 밝기 분포가 양봉 특성을 보이지 않을 때는 히스토그램 분석만으로 적절한 임계치를 얻기 어렵다 본 논문에서는 RGB 컬러 모형의 각 색상에 대하여 퍼지 소속 함수를 적용하고, 그 결과를 이용해 배경에 비해 가독성이 높은 특징들을 배경과 분리하는 방법을 제안한다. 제안된 이진화 방법은 RGB의 각 색상에 퍼지 소속 함수를 적용하여 얻은 값들을 이용해 이진화한다. 기존의 임계치를 이용한 이진화 방법에 비해 잡음 영역을 상당히 제거 할 수 있으며, 운송 컨테이너 영상에 적용한 결과, 기존의 방법에 비해 효율적인 것을 확인하였다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 춘계학술대회
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• pp.111-123
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• 2000

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a novel approach to the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy clustering algorithm and Fuzzy-ART neural network are applied to describe the states of disposal product with the membership functions and to make recycling cell formation. This approach leads to recycling and reuse of the materials, components, and subassemblies and can evaluate the value at each cell of disposal products. Application examples are illustrated by disposal refrigerators, compared fuzzy clustering with Fuzzy-ART neural network performance in cell formation.