• 센서학회지
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• 제17권6호
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• 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.

• 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.

• 비파괴검사학회지
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• 제15권4호
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• pp.531-539
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• 1996

본 연구에서는 초음파 신호형상인식법을 이용하여 용접부의 인공 결함을 분류하기 위한 연구를 실시하였다. 이를 위해 신호처리 및 특징 변수를 추출할 때에 많은 사용자 정의 변수를 가지는 신호 형상 인식 패키지를 개발하였으며 디지탈 신호처리, 특징 변수 추출, 특징 변수의 선택, 분류기 선정 등의 과정을 일괄적으로 처리하였다. 특히, 선형 분류기, 경험적 Bayesian 분류기 등의 통계적 분류기와 신경회로망 분류기를 함께 사용하여 비교, 검토하였다. 이에 관한 적용 연구로 노치와 구멍으로 이루어진 인공 결함을 분류하였다. 그 결과 인공결함 분류에서 높은 인식률을 얻었으며, 특히 적절히 학습 시켰을 경우 신경회로망 분류기가 통계적 분류기에 비하여 인식률 면에서 유리하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart 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 classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification 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 results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.231-242
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• 2018

Subway tunnel structure has been rapidly developed in many cities for its strong transport capacity. The model-based damage detection of subway tunnel structure is usually difficult due to the complex modeling of soil-structure interaction, the indetermination of boundary and so on. This paper proposes a new data-based method for the damage detection of subway tunnel structure. The root mean square acceleration and cross correlation function are used to derive a statistical pattern recognition algorithm for damage detection. A damage sensitive feature is proposed based on the root mean square deviations of the cross correlation functions. X-bar control charts are utilized to monitor the variation of the damage sensitive features before and after damage. The proposed algorithm is validated by the experiment of a full-scale two-rings subway tunnel lining, and damages are simulated by loosening the connection bolts of the rings. The results verify that root mean square deviation is sensitive to bolt loosening in the tunnel lining and X-bar control charts are feasible to be used in damage detection. The proposed data-based damage detection method is applicable to the online structural health monitoring system of subway tunnel lining.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.173-190
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• 2019

Steel-confined reinforced concrete (SCRC) columns feature highly complex and invisible mechanisms that make damage evaluation and pattern recognition difficult. In the present article, the prevailing acoustic emission (AE) technique was applied to monitor and evaluate the damage process of steel-confined RC columns in a quasi-static test. AE energy-based indicators, such as index of damage and relax ratio, were proposed to trace the damage progress and quantitatively evaluate the damage state. The fuzzy C-means algorithm successfully discriminated the AE data of different patterns, validity analysis guaranteed cluster accuracy, and principal component analysis simplified the datasets. A detailed statistical investigation on typical AE features was conducted to relate the clustered AE signals to micro mechanisms and the observed damage patterns, and differences between steel-confined and unconfined RC columns were compared and illustrated.

• 한국통신학회논문지
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• 제39C권7호
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• pp.608-614
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• 2014

이 논문에서는 비간섭 전력 부하 감시에 알맞은 패턴 인식 시스템을 다룬다. 전력 신호의 고차 적률 정보를 써서 전기기구를 효과적으로 분별하여 인식할 수 있는 새로운 특징 추출 방법을 제안한다. 동작 특성이 비슷한 두 전기기구를 제안한 고차 적률 특징과 커널 판별 분석을 쓰는 패턴 인식 시스템이 효과적으로 분별하여 인식할 수 있다는 것을 모의실험으로 보인다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• International Journal of Quality Innovation
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• 제2권1호
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• pp.30-49
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• 2001

This paper considers a recent environment in the manufacturing process in which data in large amounts can be obtained on-line in real-time. Under this environment an on-line real-time Statistical Process Control (SPC) scheme equipped with detection of a process change, change-point estimation, and recognition of the change pattern is proposed. The proposed SPC scheme is composed of a Cusum chart, filtering methods and Akaike Information Criterion (AIC). We examine the performance of this scheme by Monte Carlo simulation and show its usefulness.