• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.1-10
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• 1990

The purpose of the study was to analyze air quality in underground shopping centers using pattern recognition methods. In order to perform this, the concentraion of air pollutants such as $CO, NO_2, NO_x, SO_2$, and particulate matters was measured at the 11 different shopping centers in Seoul metropolitan area and the total of 47 samples were obtained at random based on the size of shopping centers. To introduce a new concept of the "average concentration" for the indoor air quality analyses, the various multivariate statistical analyses have been studied. Thus, a cluster analysis was applied to separate the samples into pseudo-patterns and a disjoint principal component analysis was used to generate homogeneous patterns after removing outliers from the pseudo-patterns. The 6 homogeneous patterns were then obtained as follows:the first pattern was a group of clean sites;the second a group of sites having high dust concentration;the third a group of sites having high dust and $NO_x$ concentration;the fourth a group of sites having low dust and $SO_2$ concentraion and high CO concentration;the fifth a group of sites having high $NO_2 and SO_2$ concentration;and the final a group of miscellaneous sites. Thus, the average concentration could be estimated for each pattern.h pattern.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.62-69
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• 2020

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• Journal of Korean society of Dental Hygiene
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• v.10 no.2
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• pp.335-344
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• 2010

Objectives : Targeting Korean adults, the food pattern are grasped. And, its correlation with oral disease is analyzed. In order to offer basic data to developing the nutritional policy and nutritional program for the future prevention from oral disease, a research was conducted by utilizing the Korean National Health and Nutrition Examination Survey 2005(the 3rd term). Methods : The subjects in this study were 6,526 adults in more than fully 19 years among 9,047 persons who participated in the food intake survey out of those who completed the health interview survey. The statistical analysis was analyzed by using SPSS 12.0 program. Results : 1. As a result of Group Analyzing was indicated to dangerous-type food pattern and protection-type food pattern. 2. As a result of analyzing the answers for having dental caries in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had not the statistically significant difference. 3. As a result of analyzing the answers for having periodontal disease in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had the statistically significant difference(p<0.05). 4. As a result of analyzing the food pattern factors that have influence upon both major oral illnesses in the annually personal recognition, the person, who has the dangerous-type food pattern, had high risk level of the periodontal disease in the annually personal recognition. Conclusions : In the above results, as a result of surveying and analyzing importance of the food pattern in the incidence of both major oral illnesses, it is considered that there will be necessity of continuing to research into developing the nutritional policy and nutritional program in order to prevent oral illness in the future.

• Structural Monitoring and Maintenance
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• v.6 no.3
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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.

• Journal of Power Electronics
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• v.12 no.1
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.106-110
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• 2000

The researches classifying the artificial flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including user defined function is developed and the total procedure is made up the digital signal processing, feature extraction, feature selection, classfier design. Specially it is composed with and discussed using the ststistical classfier such as the linear discriminant function classfier, the empirical Bayesian classfier.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1256-1261
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• 2008

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When multi-point is measure by using a pattern recognition, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.