• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.195-202
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• 2011

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.213-217
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• 1998

This study overviews existing evaluation methods of classification accuracy using confusion matrix proposed by Cohen in 1960's, and proposes ISDd(Index of Spatial Distribution by distance) and ISDs(Index of Spatial Distribution by scatteredness) for the evaluation of spatial distribution of satellite image classification errors, which has not been tried yet. Index of spatial distribution offers the basis of decision on adoption/rejection of classification results at sub-image level by evaluation of distribution, such as status of local aggregation of misclassified pixels. So, users can understand the spatial distribution of misclassified pixels and, can have the basis of judgement of suitability and reliability of classification results.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.190-193
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• 2000

In this paper, we investigate the distribution of classification accuracies of multiclass problems in the feature space and analyze performances of the conventional feature extraction algorithms. In order to find the distribution of classification accuracies, we sample the feature space and compute the classification accuracy corresponding to each sampling point. Experimental results showed that there exist much better feature sets that the conventional feature extraction algorithms fail to find. In addition, the distribution of classification accuracies is useful for developing and evaluating the feature extraction algorithm.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.909-917
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• 2003

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Korean Journal of Materials Research
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• v.16 no.10
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• pp.633-638
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• 2006

Silica powders with spherical shape and narrow size distribution were prepared by large-scale ultrasonic spray pyrolysis applying the droplet classification apparatus. On the other hand, silica powders prepared by large-scale ultrasonic spray pyrolysis without droplet classification apparatus had broad size distribution. Droplet classification apparatus used in this paper applied the principles of cyclone and dispersion plate with small holes. The droplets formed from the ultrasonic spray generator applying the droplet classification apparatus had narrow size distribution. The droplets with fine and large sizes were eliminated by droplet classification apparatus. The optimum flow rate of the carrier gas and diameter of the hole of the dispersion plate were studied to reduce the size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis. The size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis at the optimum preparation conditions was 0.76.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.85-87
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• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

• Journal of Korean Critical Care Nursing
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• v.13 no.2
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• pp.24-35
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• 2020

Purpose : This study aimed to determine whether the Korean Patient Classification System for Neonatal Care Nurses (KPCSN) properly measures neonatal intensive care needs and to compare the scale's results with those of the Workload Management System for Critical Care Nurses (WMSCN). Methods : Data were collected from the medical records of 157 patients who were admitted to the NICU of a university hospital, in D city. Two types of patient classification systems were applied to investigate the total points and distributions to investigate the total points and distributions by categories and compare relationships and classification groups between two scales. Finally, the score distribution among the classification groups was analyzed when the KPCSN was applied. Results : Scores on the KPCSN for the feeding, monitoring, and measure categories were 19.16±15.40, 16.88±3.52, and 9.13±4.78, respectively. Classification group distribution of the KPCSN was as follows : 1.9% for the first group, 24.2% for the second group, 58% for the third group, and 15.9% for the fourth group. The classification group distribution of the WMSCN was as follows: 35.7% for the third group, 61.1% for the fourth group, and 3.2% for the fifth group. Finally, the scores by categories were analyzed according to KPCSN classification group, and the characteristics of the patients' nursing needs were identified for each classification group. Conclusion : Results of this study indicate that the KPCSN effectively measures feeding needs, which account for many nursing activities in neonatal intensive care. Comparisons between the KPCSN and WMSCN classification group scores and distribution ratios verified the correlation and significance of nursing requirements.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.122-128
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• 1998

This paper presents compression of image sequences based on the classification of interframe difference image blocks. classification process consists of image activity classification and energy distribution classification. In the activity classification, interframe difference image blocks are classified into activity blocks and non-activity blocks using the edge detection. In the distribution classification, activity blocks are further classified into vertical blocks, horizontal blocks, and small activity blocks using the AC energy distribution features. The RBFN, trained with numerical classification results, successfully classifies difference image blocks according to image details. Image sequence compressing based on the classification of interframe difference image blocks using the RBFN shows better compression results and less training time than the classical sorting method and the MLP network.