• 한국지능시스템학회논문지
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• 제26권4호
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• pp.316-320
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• 2016

본 논문에서는 질감영상의 마이크로패턴 간 공간적인 동시발생 빈도를 고려한 패턴인식을 제안한다. 여기서 마이크로패턴은 블록영상의 중간값에 기반한 국소이진패턴(local binary pattern : LBP)으로 추출되고, 추출된 국소이진패턴들 사이의 동시발생빈도를 고려하여 패턴인식을 수행한다. 중간값 이진패턴은 영상의 국소속성을 고려할 뿐만 아니라 잡음에 강건한 패턴분석을 위함이고, 동시발생빈도는 영상의 전역속성을 고려하여 인식성능을 좀 더 향상시키기 위함이다. 제안된 기법을 120*120 픽셀의 17개 RGB 질감 패턴영상을 대상으로 유클리디언(Euclidean) 거리에 기반한 실험결과, 우수한 인식성능이 있음을 확인하였다.

• 한국산업정보학회논문지
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• 제22권6호
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• pp.39-45
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• 2017

기존의 색상기반 얼굴인식 방법은 조명변화에 민감하며, 위변조의 가능성이 있기 때문에 다양한 산업분야에 적용되기 어려운 문제가 있었다. 본 논문에서는 이러한 문제를 해결하기 위해 깊이 영상을 이용한 지역 이진 패턴(LBP) 기반의 얼굴인식 방법을 제안한다. 깊이 정보를 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하고, 모의실험 결과를 바탕으로 제안된 방식의 인식 성능을 나타낸다.

• 대한전기학회논문지:전력기술부문A
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• 제55권10호
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• pp.410-417
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• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• Journal of Multimedia Information System
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• 제5권4호
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• pp.221-228
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• 2018

Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.

• 한국통신학회논문지
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• 제38B권12호
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• pp.954-961
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• 2013

본 논문은 퍼지ART의 학습 방법의 하나인 FCSR(Fast Commit Slow Recode)에서 패턴 인식을 향상시키기 위해 가변 학습을 이용하는 새로운 학습방법을 제안하였다. 기존의 학습 방법은 연결 강도(대표패턴)의 갱신에 고정된 학습률이 사용된다. 이 방법은 같은 카테고리 내의 입력패턴과 대표패턴의 유사성의 정도와 관계없이 고정된 학습률로 연결 강도를 갱신한다. 이 경우 카테고리 경계에 있는 유사성이 낮은 입력패턴이 연결강도의 갱신에 크게 영향을 주게 된다. 따라서 잡음 환경에서 이것은 불필요한 카테고리 증식의 원인이 되고, 패턴 인식 능력을 낮추는 문제가 된다. 제안된 방법에서는 대표 패턴과 입력 패턴 사이에 유사성이 적을수록 연결강도의 갱신에 입력패턴의 기여를 낮추어간다. 그 결과 잡음환경에서 퍼지 ART의 불필요한 카테고리 증식을 억제하였고, 패턴 인식 능력을 향상시켰다.

• 대한전기학회논문지:전력기술부문A
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• 제48권10호
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• pp.1335-1343
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• 1999

This paper presents concrete pattern recognition method to identify the strength of concrete by evidence accumulation with multiple parameters based on artificial intelligence techniques. At first, zero-crossing(ZCR), mean frequency(MEANF), median frequency(MEDF) and autoregressive model coefficient(ARC) are extracted as feature parameters from ultrasonic signal of concrete. Pattern recognition is carried out through the evidence accumulation procedure using distance measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for concrete pattern recognition.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제20권4호
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• pp.551-559
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• 2006

Pattern Recognition measures the ability of learners to distinguish between two sets of shapes or figures. Locating similar patterns on either side of the presented problem determines a learner's capacity or aptitude for science over general studies. At Ajou University's Institute for Scientifically Enabled Youth, we conducted research using a sample composed of middle school students with general and scientific backgrounds. The result proved that Pattern Recognition measures a different creative talent other than problem solving. In our opinion, Pattern Recognition would be a method better suited to elementary learners over those in middle or high school.

• International Journal of Precision Engineering and Manufacturing
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• 제5권3호
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• pp.11-18
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• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

• Journal of Power Electronics
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• 제16권1호
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• pp.217-226
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• 2016

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5605-5623
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• 2016

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.