• Journal of Electrical Engineering and information Science
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• 제2권4호
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• pp.59-65
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• 1997

In this paper, a general fuzzy syntactic method for recognition of flaw patterns and for the measurement of flaw characteristic parameters for a non-destructive inspections signal, called eddy-current, is presented. Solutions are given to the subtasks of primitive pattern selection, signal to symbol transformation, pattern grammar formulation, and event-synchronous flaw pattern extraction based on the grammars. Fuzzy attribute grammars are used as the model for the pattern grammar because of their descriptive power in the face of uncertain constraints caused by nose or distortion in the signal waveform, due to their ability to handle syntactic as well as semantic information. This approach has been implemented and the performance of eh resultant system has been evaluated using a library of law patterns obtained from steam generator tubes in nuclear power plants by an eddy current-based non-destructive inspection method.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.481-483
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• 2015

LCD의 특성을 이해하기 위해 Vertical Alignment Liquid Crystal Cell을 만들어보고, 전류를 흘려 Cell에 나타나는 pattern을 확인한다. 다양한 rubbing 방향을 주어 완성된 Cell에 나타나는 여러 가지 pattern들을 확인, 비교해보고 그 pattern의 발생 원인에 대해 연구한다. Cell에 전류를 가해주면 line이 나타나는데 가해준 전압과 주파수가 높을수록 line이 많이 나타나는 것을 확인할 수 있었다.

• 한국통신학회논문지
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• 제5권1호
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• pp.44-48
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• 1980

본 논문에서는 필기체 한글인식을 위한 한 방법을 제안했다. 기본 자모를 대상으로 하였으며, 임의의 Pattern에 대하여 접합보상및 정형 Algorithm을 제안하므로써 본래의 표준 한글 pattern으로 정형화하였다. 인식에는 Tree grammar를 사용하였으며, 새로운 Parsing 방법을 제안하므로써 종래의 방법보다 처리를 간단화시켰으며 error를 감소시켰다. 제한된 필기체에 대하여는 매우 효과적이었으며 on line 필기체 인식에도 유용성이 있음을 보였다.

• ETRI Journal
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• 제37권5호
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• pp.990-1000
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• 2015

This paper presents an overlapping pattern substitution (PS) method. The original overlapping PS method as a reversible data hiding scheme works well with only four pattern pairs among fifteen possible such pairs. This paper generalizes the original PS method so that it will work well with an optimal pair from among the fifteen possible pattern pairs. To implement such an overlapping PS method, changeable and embeddable patterns are first defined. A class map is virtually constructed to identify the changeable and embeddable pairs. The run-lengths between consecutive least probable patterns are recorded. Experiments show that an implementation of our overlapping PS method works well with any possible type of pairs. Comparison results show that the proposed method achieves more embedding capacity, a higher PSNR value, and less human visual distortion for a given embedding payload.

• 한국통신학회논문지
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• 제22권3호
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• pp.481-492
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• 1997

This paper presents an algorithm for recognizing surface mount device(SMD) IC pattern based on the error back propoagation(EBP) neural network and discrete cosine transform(DCT). In this approach, we chose such parameters as frequency, angle, translation and amplitude for the shape informantion of SMD IC, which are calculated from the coefficient matrix of DCT. These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Learning of EBP neural network is carried out until maximum error of the output layer is less then 0.020 and consequently, after the learning of forty thousand times, the maximum error have got to this value. Experimental results show that the rate of recognition is 100% in case of the random pattern taken at a similar circumstance as well as normalized training pattern. It also show that proposed method is not only relatively relatively simple compare with the traditional space domain method in extracting the feature parameter but also able to re recognize the pattern's class, position, and existence.

• International Journal of Contents
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• 제2권3호
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• pp.11-17
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• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 대한토목학회논문집
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• 제39권6호
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• pp.833-839
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• 2019

BIS (Bus Information System:버스정보시스템, 이하 BIS)는 시내버스 운행과 관련된 각종 정보를 수집하고 예측알고리즘을 통해 이용객에게 정보를 제공하고 있다. 동일 구간의 최근 정보를 통한 예측방법은 해당 구간의 소통상황을 반영하지만 예측 대상노선의 특성을 반영할 수 없다는 한계가 있다. 해당노선의 동시간대 과거이력자료를 통해 예측하는 방법은 소통상황의 변동성이 큰 첨두시 예측에 한계가 있는 실정이다. 따라서 예측대상 시점의 통행패턴을 인식하고 가장 유사한 과거 시점의 통행패턴을 선택할 수 있는 패턴인식형 버스도착시간 예측 알고리즘을 개발하였다. 본연구의 예측 결과를 서울시 BIS 도착예측정보이력과 비교 검증한 결과 각 정류장 간 통행시간의 평균제곱근오차가 비첨두시 약 35초(기존: 40초), 첨두시 약 40초(기존: 60초)로 기존대비 약 10~20 %의 개선을 보였다. 이는 동일 과거 시간대 외의 시간대에 현재 교통상황을 대표할 수 있는 자료가 존재함을 의미한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.23-27
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• 2000

In this paper, we propose an emotional evaluation model based on information fusion. This model can transform the physical features of a color pattern to the emotional features. Our proposed model consists of the fuzzy logic system and neural network model. The evaluation values produced by them were fused. The model shows comparable performances to the neural network and fuzzy logic system for the approximation of the nonlinear transforms. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.1017-1018
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• 2006

We measured EEG, extracted the feature vectors using alpha and beta rhythm from the measured EEG and pattern recognition was simulated by using the feature vector and the algorithms which are conventional LVQ and Forward only Counter Propagation Networks. And then the successful rate of pattern class of EEG data had about 76 %.