• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Proceedings of the KIEE Conference
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• 1994.07b
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• pp.1540-1542
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• 1994

This paper describes an experimental study of pattern recognition of partial discharge for three different discharge sources by using neural network(NN) system. The NN system is three layer feedforward connections and its learning method is a backpropagation algorithm incorporating an external teacher signal. Input information for NN is a statistical parameters of a discharge magnitude and the number of pulse count. After learning three typical input patterns, NN system offers good discrimination between different defects.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.78-80
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• 2005

The pattern recognition of automobile and parking line for the automatic car parking system was processed based on statistical method for reducing the processing time. The command of driving for parking at the vacant parking lot was transmitted from processor to motor driven actuator using direct sequence spread spectrum communication, which enables the multiple transmission in CAN(controller area network). The test-bed which has CCD camera, processor, radio transceiver and FPGA was implemented and demonstrated to be operated well.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.343-350
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• 2015

This paper presents a context recognition method using environmental sound signals, which is applied to a mobile-based client monitoring system. Seven acoustic contexts are defined and the corresponding environmental sound signals are obtained for the experiments. To evaluate the performance of the context recognition, MFCC and LPCC method are employed as feature extraction, and statistical pattern recognition method are used employing GMM and HMM as acoustic models, The experimental results show that LPCC and HMM are more effective at improving context recognition accuracy compared to MFCC and GMM respectively. The recognition system using LPCC and HMM obtains 96.03% in recognition accuracy. These results demonstrate that LPCC is effective to represent environmental sounds which contain more various frequency components compared to human speech. They also prove that HMM is more effective to model the time-varying environmental sounds compared to GMM.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.507-517
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• 2003

Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.

• Bulletin of the Korean Chemical Society
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• v.9 no.4
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• pp.223-231
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• 1988

Some pattern recognition methods have been used to characterize Korean ancient porcelainsherds using their elemental composition as analyzed by instrumental neutron activation analysis. A combination of analytical data by means of statistical linear discriminant analysis(SLDA) has resulted in removal of redundant variables, optimal linear combination of meaningful variables and formulation of classification rules. The plot in the first-to-second discriminant scores has shown that the three distinct territorial regions exist among porcelainsherds of Kyungki, Chunbuk-Chungnam, and Chunnam, with respective efficiencies of 20/30, 22/27 and 14/15. Similar regions have been found to exist among punchong porcelain and ceradonsherds of Kyungki, Chungnam and Chunbuk, with respective efficiencies of 7/9, 15/16 and 6/6. Classification has been further attempted by statistical isolinear multiple component analysis(SIMCA), using the sample set selected appropriately through SLDA as training set. For this purpose, all analytical data have been used. An agreement has generally been found between two methods, i.e., SLDA and SIMCA.

• Genomics & Informatics
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• v.10 no.4
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• pp.263-265
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• 2012

We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1837-1839
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• 2000

This paper describes PD patterns in GIS recognized by using neural network proposed in this paper PD sources in GIS were classified by four states and PD signals were expressed by $\Phi-Q$ distribution, ${\Phi]-Q_m$ distribution, $\Phi-N$ distribution and Q-N distribution. Then statistical operators were extracted from each distributions. As a result, the PD pattern recognizing rate in GIS using neural network proposed in this paper was increased.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.3
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• pp.134-141
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• 2011

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.