• Korean Journal of Remote Sensing
• /
• v.28 no.6
• /
• pp.671-682
• /
• 2012

Strong backscattering features from high-resolution Synthetic Aperture Rader (SAR) image provide useful information to analyze earth surface characteristics such as man-made objects in urban areas. The SAR image has, however, some limitations on description of detail information in urban areas compared to optical images. In this paper, we propose a new classification method using a fused SAR and Electro-Optical (EO) image, which provides more informative classification result than that of a single-sensor SAR image classification. The experimental results showed that the proposed method achieved successful results in combination of the SAR image classification and EO image characteristics.

• Journal of Korean Academy of Nursing Administration
• /
• v.6 no.3
• /
• pp.375-388
• /
• 2000

This study was attempted to help in explore new direction about Clinical Classification System of the pediatric patients visiting emergency center. Data were collected from 276 patients who visited emergency center of E University Hospital during 3 months period form March 1, to May 31, 1999. The results were as follows: 1. Distribution of pediatric patients according to Clinical Classification System, class I(59.9%) topped followed by class II(23.9%), class III(14.1%), class IV(2.0%). Average score of pediatric patients according to Clinical Classification System showed class I.00, class II .02, class III .05, class IV .07. and total mean score of items lowed averaged .01. 2. With the resepect to the Clinical Classification System according to the pediatric patients visiting emergency center, there were stastically significant difference in visiting time($x^2=27.839$, P=.023), experience of admission($x^2=11.365$, p=.010), disease classification($x^2=89.998$, p=.000), state of airway patency($x^2=18.781$, p=.000), consciousness level($x^2=59.774$, p=.000), period of symptom manifestation($x^2=34.112$, p=.000), pediatric patients protector's thinking about pediatric patients state($x^2=49.998$, p=.000), treatment outcome($x^2=72.278$, p=.000), duration of stay at emergency center($x^2=103.062$, p=.000). 3. There were significant correlation between the state of pediatric patients and Clinical Classification System(r=.530, p=.000).

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.792-799
• /
• 2023

Non-reactor nuclear facilities are increasing remarkably in Korea combined with advanced technologies such as life and space engineering, and the diversification of the nuclear industry. However, the absence of a basic classification guideline related to the design of non-reactor nuclear facilities has created confusion whenever related projects are carried out. In this paper, related domestic and international technical guidelines are reviewed to present the classification criteria of non-reactor nuclear facilities in Korea. Based on these criteria, the classification of structures, systems and components (SSCs) for safety controls is presented. Using the presented classification criteria, classification of a hot cell facility, a representative non-reactor nuclear facility, was performed. As a result of the classification, the hot cell facility is classified as the hazard category 3, accordingly, the safety class was classified as non-nuclear safety, the seismic category as non-seismic (RW-IIb), and the quality class as manufacturers' standards (S).

• Journal of Korean Society for Quality Management
• /
• v.48 no.4
• /
• pp.553-566
• /
• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.1
• /
• pp.139-152
• /
• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• Korean Journal of Remote Sensing
• /
• v.36 no.1
• /
• pp.55-65
• /
• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.1
• /
• pp.44-50
• /
• 2011

We propose a multi-class AdaBoost algorithm for en efficient classification of multi-class data in this paper. Traditional AdaBoost algorithm is basically a binary classifier and it has limitations when applied to multi-class data problems even though multi-class versions are available. In order to overcome the problems on the AdaBoost algorithm for multi-class classification problems, we devise an AdaBoost architecture with a training algorithm that utilizes multi-class classifiers for its weak classifiers instead of series of binary classifiers. Experiments on a image classification problem using collected Caltech Image Database are preformed. The results show that the proposed AdaBoost architecture can reduce its training time while maintaining its classification accuracy competitive when compared to Adaboost.M2.

• Journal of Biomedical Engineering Research
• /
• v.31 no.4
• /
• pp.328-334
• /
• 2010

Over the past few years, a considerable number of methods have been proposed and applied for the classification of photoplethysmography (PPG). Most of the previous studies, however, focused on the qualitative description of the pulse type according to specific disease and thus provided ambiguous criteria to interpreters. In order to screen out this problem, we present a quantitative method for the pulse type classification including the second derivative of photoplethysmography (SDPTG). In the PPG signal, we have classified the signal as 4 types using the position and the presence of the dicrotic wave. In addition, we have categorized the SDPTG signal as 7 types using the position and the presence of "c" and "d" wave and the sign of "c" wave. In order to check the efficacy of the proposed pulse type classification rule, we collected pulse signals from 155 subjects with different ages and sex. From the correlation analysis, Class 1(p<0.01) and Class 2(p<0.01) in the PPG signal are significantly correlated with ages. In a similar manner Class A(p<0.01), Class C(p<0.05), Class D(p<0.01), and Class F(p<0.01) in the SDPTG signal are considerably correlated with the ages. From these observations, and some earlier ones [4], [5], we can conclude that since the newly proposed method has objectivity and clarity in pulse type classification, this method can be used as an alternative of previous classification rules including similar age-related characteristics.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.3
• /
• pp.22-28
• /
• 2010

Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.43-58
• /
• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.