• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.9-16
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• 2015

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

• Journal of Korean Society for Quality Management
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• v.8 no.1
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• pp.8-15
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• 1980

There are many methods of pattern recognition. In this paper we assume that the responses of independent m groups are described by p-variate normal random variables with distinct mean vectors and a common covariance matrix. Under the assumption we give pattern recognition of m groups by means of canonical discrininant analysis and it's computer program. An example is presented.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.118-125
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• 2018

An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lavender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reacted remarkably to the aroma fragrances and the good correlation of r=0.58-0.88 with the aromatic odor intensities by olfaction was confirmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aroma oils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognition rate was 57.1%-71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all confusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.

• Journal of Biosystems Engineering
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• v.38 no.1
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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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.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.2
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• pp.63-66
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• 2005

This paper describes analysis and pattern recognition techniques for Partial Discharge(PD) signals in Gas Insulated Switchgears (GIS). Detection of PD signals is one of the most important factors in the predictive maintenance of GIS. One of the methods of detection is electro magnetic wave detection within the Ultra High Frequency (UHF) band (300MHz $\~$ 3GHz). In this paper, PD activity simulation is generated using three types of artificial defects, which were detected by a UHF PD sensor installed in the GIS. The detected PD signals were performed on three-dimensional phi-q-n analysis. Finally, parameters were calculated and an Artificial Neural Network (ANN) was applied for PD pattern recognition. As a result, it was possible to discriminate and classify the defects.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.2
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.150-154
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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 are 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.