• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.640-643
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• 2003

A Walsh function based associative memory is capable of storing m patterns in a single pattern storage space with Walsh encoding of each pattern. Furthermore, each stored pattern can be matched against the stored patterns extremely fast using algorithmic parallel processing. As such, this special type of memory is ideal for real-time processing of large scale information. However this incredible efficiency generates large amount of crosstalk between stored patterns that incurs mis-recognition. This crosstalk is a function of the set of different sequencies [number of zero crossings] of the Walsh function associated with each pattern to be stored. This sequency set is thus optimized in this paper to minimize mis-recognition, as well as to maximize memory saying. In this paper, this Walsh memory has been applied to the problem of face recognition, where PCA is applied to dimensionality reduction. The maximum Walsh spectral component and genetic algorithm (GA) are applied to determine the optimal Walsh function set to be associated with the data to be stored. The experimental results indicate that the proposed methods provide a novel and robust technology to achieve an error-free, real-time, and memory-saving recognition of large scale patterns.

• 한국전문물리치료학회지
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• 제5권2호
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• pp.39-46
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• 1998

Thumb adduction is an abnormal pattern typically noted in children with spastic cerebral palsy. This abnormal pattern can limit hand function, specifically in the type and quality of prehension pattern used and in the coordination of release. This ABAB single-subject research was designed to examine the effects of short thumb opponens splint on hand function in cerebral palsy. The subject was a 4 years and 8 months old boy with right upper extremity spasticity. The child was fitted with a short thumb opponens splint, which was worn for 8 hours per day during the daytime. Two different measures were used: (a) prehension component scores; (b) Bruininks-Oseretsky test of motor proficiency. Data was collected three times a week for 10 weeks. Visual analysis of data indicate that after the application of a short thumb opponens splint, improvements were noted in the prehension pattern and fine motor functional task. The results of this study suggest that short thumb opponens splint may prove efficaciousness in the treatment of the child with cerebral palsy.

• 한국콘텐츠학회논문지
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• 제10권4호
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• pp.10-18
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• 2010

본 논문에서는 유전자알고리즘을 이용한 시그모이드 활성화 함수 파라미터의 최적화와 이중나선기준문제(two spirals benchmark problem)의 입력공간 패턴인식 상태를 분석 한다. 실험을 위하여 캐스케이드 코릴레이션 학습 알고리즘(Cascade Correlation learning algorithm)을 이용한다. 첫 번째 실험에서는 기본적인 시그모이드 활성화 함수를 사용하여 이중나선 문제를 분석하고, 두 번째 실험에서는 시그모이드 활성화 함수(sigmoidal activation function)의 파라미터 값이 서로 다른 함수를 사용하여 8개의 풀을 구성한다. 세 번째 실험에서는 시그모이드 함수의 변위를 결정하는 세 개의 파라미터 값을 유전자 알고리즘을 이용하여 얻고 이 파라미터 값들이 적용된 시그모이드 함수들은 후보뉴런의 활성화를 위해서 사용된다. 이러한 알고리즘의 성능평가를 위하여 각 학습단계 마다 입력패턴공간에서 인식된 이중나선의 형태를 보여준다.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.703-710
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• 2010

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.

• 한국생산제조학회지
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• 제9권1호
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• 한국의류학회지
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• 제22권3호
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• pp.303-311
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• 1998

We developed a pattern drafting computer program using a popular CAD program for the purpose of the automatic manufacturing and education based on the concept of the easy -order-system which can easily reflect body characteristics and design favors of individuals to the ready-made clothing. The programs were written in Auto LISP which can utilise most of frictions of the Auto CAD, and the result of the design for an full-pleated skirt was shown. Also, we devised an easy way to set the location of reference points by inputting of tangential angle only for the use of the cubic spline curve which can represent body shape very well. Finally, by marking down all of symbols needed in manufacturing process, we completed the automatic pattern drafting program to make it useful as an industrial pattern without any amendment or correction in cutting and sewing operation.

• 대한기계학회논문집A
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• 제20권4호
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• 재활복지공학회논문지
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• 제8권4호
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• pp.305-312
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• 2014

본 연구에서는 재활정도를 정량적으로 표현하기 위하여 정상 보행과 모의 비정상 보행 시 보행주기에 따른 근전도 패턴에 고차 곡선 맞춤을 적용하여 분석하였다. 보행 근전도 패턴에 적합한 다항식을 생성하였으며, 그 변수를 군집분석 하여 5개의 그룹으로 분류하였다. 정상패턴을 포함한 그룹을 기준으로 거리가 가까운 그룹을 나열하여 각 변수의 분포를 확인하였다. 진폭감소 패턴, 불규칙 패턴, 역상 패턴 순으로 정상 패턴에 유사하였으며, 분류된 그룹의 분포는 중첩되는 범위가 작아 변수 값을 이용한 그룹 분류가 가능하였다. 분류된 재활정도를 정량적으로 나타내기 위하여 각 계수항의 표준편차를 패턴별로 비교하였고, 정상에 가까울수록 큰 값을 가지는 것을 확인하였다. 역상 패턴의 경우는 편차 값은 크지만 부호가 음의 값을 가지므로 가장 작았다. 본 연구의 결과인 정량적인 재활정도의 표현은 보다 효율적인 재활방법 연구에 기여할 것으로 예상된다.

• 한국산학기술학회논문지
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• 제15권3호
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• pp.1724-1733
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• 2014

본 논문에서는 비모노톤함수(non-monotone function)인 CosExp(cosine-modulated symmetric Exponential function) 함수와 모노톤함수(monotone function)인 시그모이드 함수를 캐스케이드 코릴레이션 알고리즘(Cascade Correlation algorithm)의 학습에 병행해서 사용하여 이중나선문제(two spirals problem)의 패턴인식에 어떠한 영향이 있는지 분석하고 이어서 알고리즘의 최적화를 시도한다. 첫 번째 실험에서는 알고리즘의 후보뉴런에 CosExp 함수를 그리고 출력뉴런에는 시그모이드 함수를 사용하여 나온 인식된 패턴을 분석한다. 두 번째 실험에서는 반대로 CosExp 함수를 출력뉴런에서 사용하고 시그모이드 함수를 후보뉴런에 사용하여 실험하고 결과를 분석한다. 세 번째 실험에서는 후보뉴런을 위한 8개의 풀을 구성하여 변형된 다양한 시그모이드 활성화 함수(sigmoidal activation function)를 사용하고 출력뉴런에는 CosExp함수를 사용하여 얻게 된 입력공간의 인식된 패턴을 분석한다. 네 번째 실험에서는 시그모이드 함수의 변위를 결정하는 세 개의 파라미터 값을 유전자 알고리즘을 이용하여 얻는다. 이 파라미터 값들이 적용된 시그모이드 함수들은 후보뉴런의 활성화를 위해서 사용되고 출력뉴런에는 CosExp 함수를 사용하여 실험한 최적화 된 결과를 분석한다. 이러한 알고리즘의 성능평가를 위하여 각 학습단계 마다 입력패턴공간에서 인식된 이중나선의 형태를 그래픽으로 보여준다. 최적화 과정에서 은닉뉴런(hidden neuron)의 숫자가 28에서 15로 그리고 최종적으로 12개로 줄어서 학습 알고리즘이 최적화되었음을 확인하였다.

• 전자공학회논문지S
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• 제34S권2호
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• pp.94-103
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• 1997

The WCHF-FSJTC (wavelet circular harmonic function frequency selective joint transform correlator) using th wavelet transformed CHF as the reference image in FSJTC is proposed for rotation invariant pattern recognition. Since the wavelet transform has the property of feature extraction, the proposed system can have the better DC (discrimination cpability) and the higher SNR(signal to noise ratio) compared with the conventional CHF-CJTC(circular harmonic function conventional joint transform correlator). And since the structure of the proposed system is FSJTC which can eliminate auto-correlation and cross-correlation between input images, it can eliminate false alarm caused by the overlapping among correlation peaks. The used wavelet functio is the morlet function, which is proper for the reference image used in this paper. the optimal dialation parameter and oscillation frequency of the wavelet function are also achieved with varying the parameters of the wavelet function. The computer simulation shows that the proposed system has the best performance when the dilation parameter is 0.8 and the oscillation frequency is 0.48.