• 전기학회논문지
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• 제59권4호
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• pp.779-787
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• 2010

In this paper, We introduce electrical tracking generated from surface activity associated with flow of leakage current on insulator under wet and contaminated conditions and design electrical tracking pattern recognition system by using LabVIEW. We measure the leaking current of contaminated wire by using LabVIEW software and the NI-c-DAQ 9172 and NI-9239 hardware. As pattern recognition algorithm and optimization algorithm for electrical tracking system, neural networks, Radial Basis Function Neural Networks(RBFNNs) and particle swarm optimization are exploited. The designed electrical tracking recognition system consists of two parts such as the hardware part of electrical tracking generator, the NI-c-DAQ 9172 and NI-9239 hardware and the software part of LabVIEW block diagram, LabVIEW front panel and pattern recognition-related application software. The electrical tracking system decides whether electrical tracking generate or not on electrical wire.

• Journal of information and communication convergence engineering
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• 제9권5호
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• pp.607-609
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• 2011

In this work, we present solutions of two problems. First, the representation of pattern recognition problem in the standard $T_{nml}$ table of the algorithm estimate calculation was considered. Second, the problem of finding the model, consisting of the optimal parameters of an algorithm was considered. Such procedure is carried out by the selection optimal values of the parameters of extreme algorithms. This serves to reduce the number of calculations in the algorithms of estimate calculation and to increase the quality of recognition process. The algorithmic data base of the developed system was based on mathematical apparatus of pattern recognition.

• 대한전자공학회논문지
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• 제14권5호
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• pp.15-21
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• 1977

본 논문은 Syntactic법에 의한 한글의 인식 algorithm에 대하여 논한 것으로 인식절차는 크게 나누어 전처리, graph화, 분할의 3단계로 구성되어 있다. 전처리과정에서는 Hilditch의 thinning algorithm을 이용하여 입력문자의 골격패턴을 얻었다. graph화 단계에서는 세선화패턴으로 부터 4종류의 특징점을 추출하여 입력패턴을 기본패턴의 구성관계로 나타냈다. 분할단에서는 tree문법에 의한 패턴해석을 수행, 입력패턴을 구성하는 각 자모를 순차적으로 추출하였다. 본 algorithm의 효용성을 검토하기 위하여 전자계산기를 이용, 511자의 인쇄체 한글에 대하여 인식실험을 행하였다. 그 결과 약 90%의 정인식율을 얻었다.

• 한국음향학회지
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• 제23권3호
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• pp.235-241
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• 2004

본 논문은 화자 및 문장 독립적 감정 인식을 위한 특징 파라메터와 패턴인식 알고리즘에 관하여 연구하였다. 본 논문에서는 기존 감정 인식 방법과의 비교를 위하여 KNN을 이용한 알고리즘을 사용하였고, 화자 및 문장 독립적 감정 인식을 위하여 VQ와 GMM을 이용한 알고리즘을 사용하였다. 그리고 특징으로 사용한 음성 파라메터로 피치, 에너지, MFCC, 그리고 그것들의 1, 2차 미분을 사용하였다. 실험을 통해 피치와 에너지 파라메터를 사용하였을 때보다 MFCC와 그 미분들을 특징 파라메터로 사용하였을 때 더 좋은 감정 인식 성능을 보였으며, KNN과 VQ보다 GMM을 기반으로 한 인식 알고리즘이 화자 및 문장 독립적 감정 인식 시스템에서 보다 적합하였다.

• 한국통신학회논문지
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• 제37C권10호
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• pp.925-932
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• 2012

본 논문에서는 패턴 인식 알고리즘을 기반으로 인간 형태를 가진 휴머노이드 로봇의 보행 동작을 제어하는 경로 인식 시스템을 개발하였다. 휴머노이드 로봇이 효과적인 작업 수행을 할 수 있도록 행동 프리미티브를 정의 하였으며, Canny 에지 검출 알고리즘을 적용한 보도 블록의 패턴 및 색상 추출, 이를 기반으로 한 이동 방향을 인식하는 알고리즘 제안하고, 리눅스 운영체제와 영상 카메라가 장착된 소형 휴머노이드 임베디드 시스템에 구현하였다. 제안 알고리즘의 성능 실험을 휴머노이드 로봇의 동작 속도 및 인식율에 관점에서 수행하였으며, 다양한 현실 환경을 반영하기 위해 경사도 및 조도 변화를 적용하였다. 실험 결과 제안 알고리즘은 다양한 환경에서 시각 장애인의 길안내 도우미 로봇으로서 적절한 수준에서 반응함을 확인하였다.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.55-63
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• 2013

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.887-890
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• 2012

Image recognition is one of the most important research directions of pattern recognition. Image based road automatic identification technology is widely used in current society, the intelligence has become the trend of the times. This paper studied the image segmentation algorithm theory and its application in road signs recognition system. With the help of image processing technique, respectively, on road signs automatic recognition algorithm of three main parts, namely, image segmentation, character segmentation, image and character recognition, made a systematic study and algorithm. The experimental results show that: the image segmentation algorithm to establish road signs recognition model, can make effective use of smart phone system and application.

• Smart Structures and Systems
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• 제6권1호
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• pp.39-56
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• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.