• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.37-42
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• 2019

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.173-190
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• 2015

The purpose of this study is to improve bankruptcy prediction models by using a novel hybrid under-sampling approach. Most prior studies have tried to enhance the accuracy of bankruptcy prediction models by improving the classification methods involved. In contrast, we focus on appropriate data preprocessing as a means of enhancing accuracy. In particular, we aim to develop an effective sampling approach for bankruptcy prediction, since most prediction models suffer from class imbalance problems. The approach proposed in this study is a hybrid under-sampling method that combines the k-Reverse Nearest Neighbor (k-RNN) and one-class support vector machine (OCSVM) approaches. k-RNN can effectively eliminate outliers, while OCSVM contributes to the selection of informative training samples from majority class data. To validate our proposed approach, we have applied it to data from H Bank's non-external auditing companies in Korea, and compared the performances of the classifiers with the proposed under-sampling and random sampling data. The empirical results show that the proposed under-sampling approach generally improves the accuracy of classifiers, such as logistic regression, discriminant analysis, decision tree, and support vector machines. They also show that the proposed under-sampling approach reduces the risk of false negative errors, which lead to higher misclassification costs.

• Nuclear Engineering and Technology
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• v.51 no.8
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• pp.1951-1956
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• 2019

As a part of the preliminary decommissioning plan of KEPCO-NF fuel fabrication facility, DCGLs of three target radionuclides, ²³⁴U, ²³⁵U, and ²³⁸U, were derived using RESRAD-BUILD code and contaminated areas of the facility were classified based on contamination levels from the derived DCGLs. From code simulations, one-room modeling results showed that the grinding room in building #2 was the most restrictive (DCGL_gross = 10493.01 Bq/㎡). The DCGL_gross results in contaminated areas from one-room modeling were slightly more conservative than three-room modeling. Prior to the code simulation, field survey and measurements conducted by each survey unit. For a conservative approach, the most restrictive DCGL_gross in each survey unit was taken as a reference to classify the contaminated areas of the facility. Accordingly, seven rooms and 37 rooms in the nuclear-fuel buildings were classified as Class 1 and Class 2, respectively. As expected, fuel material handling and processing rooms such as the grinding room, sintering room, compressing room, and powder collecting room were included in the Class 1 area.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Journal of Oral Medicine and Pain
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• v.24 no.1
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• pp.37-47
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• 1999

This study was performed to investigate the factors related to vibration of temporomandibular joint during mandibular opening movement. For this study, 144 patients with temporomandibular disorders were randomly selected. Angle's classification, lateral guidance pattern, range of maximal mouth opening, preferred chewing side, and affected side were investigated clinically. Mandibular torque rotational movement during opening was recorded with $BioEGN^{(R)}$ and vibration of temporomandibular joint during opening was recorded with $Sonopak^{(R)}$. After clinical diagnosis was made, visual analogue scale(VAS) was used for evaluation of clinical progress of the subject's chief complaints. The author calculated VAS treatment index(VAS Ti) from the record of VAS. The more VAS Ti was, the less remission of subjective symptom was, The data were analyzed with SAS/Stat program and the results of this study were as follows: 1. There were no significant difference in all the variables of joint vibration by age and sex. 2. Integral and peak amplitude in patients of Angle's class I were higher than those of class II or III patients. Integral in patients of group function was higher than that in patients of canine guidance or other types of lateral excursion. 3. As to Angle's classification or lateral guidance type, there were almost not significant difference between subgroup of same class or type and subgroup of different class or type on both sides. And there were also almost not difference between one side and the other side related to preferred chewing side or affected side. 4. Patients with disk displacement with reduction showed higher value of integral and peak amplitude than any other patients. 5. Joint vibration variables significantly correlated with VAS Ti of pain. with clinical range of mouth opening, and with ingredients of mandibular torque rotational movement.

• Journal of the Korea Fashion and Costume Design Association
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• v.7 no.2
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• pp.75-85
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• 2005

This study analyzed the factors of change of women's underwear in the Renaissance, which had a sudden change of shape. First, the spirit of the Renaissance was focused on human-attached importance to glamorous beauty of the body and pursued the glamorous well-proportioned figure as the ideal of the human body. This expressed a woman's beautiful curved lines by reduction of their waist size and emphasis of breast and hip lines. It also created and emphasized one's physical figure, which is a characteristic of sex. Also the materials and size of underwear cleared up the classification of class. Second, through the development of weaving techniques, more textiles were produced, from linen, the most common material used, to silk in underwear and stomacher, and chemise, which was made more splendid be devising elaborate embroidery techniques. Third, as we know that the farthingale was devised in Spain and transmitted to France because of the prevalence of printing and trade, where it changed and developed to more convenient style, this shows that information interchange was active, and we can see the phenomenon that it is developing continually through the prevalence of printing and trade.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.141-148
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• 2009

Recently there have been many research efforts focused on imbalanced data classification problems, since they are pervasive but hard to be solved. Approaches to the imbalanced data problems can be categorized into data level approach using re-sampling, algorithmic level one using cost functions, and ensembles of basic classifiers for performance improvement. As an algorithmic level approach, this paper proposes to use multilayer perceptrons with higher-order error functions. The error functions intensify the training of minority class patterns and weaken the training of majority class patterns. Mammography and thyroid data-sets are used to verify the superiority of the proposed method over the other methods such as mean-squared error, two-phase, and threshold moving methods.

• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.103-110
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• 1998

In case of traditional Rule-Based Reasoning(RBR) and Case-Based Reasoning(CBR), although knowledge is reasoned either by one of them or by the integration of RBR and CBR, there is a problem that much time should be consumed by numerous rules and cases. In order to improve this time-consuming problem, in this paper, a new type of reasoning technique, which is a kind of integration of reduced RB and CB, is to be introduced. Such a new type of reasoning uses Rough Set, by which we can represent multi-meaning and/or random knowledge easily. In Rough Set, solution is to be obtained by its own complementary rules, using the process of RB and CB into equivalence class by the classification and approximation of Rough Set. and then using reduced RB and CB through the integrated reasoning.

• Journal of Chest Surgery
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• v.25 no.10
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• pp.1055-1060
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• 1992

Clinical experience of 21 patients with infective endocarditis was reviewed. Endocarditis involved the left-sided valve in 16 cases, the right-sided valve in 2, and PDA in the remaining 3 patients. Valve abnormalities included leaflet perforation in 9 patients, chordal rupture in 2,; annular abscess in 6; and aorticoleft atnal perforation in 2. Sixteen patients underwent valve replacement[aortic valve replacement in 7 patients, mitral replacement in 4 and double valve replacement in 5], two had VSD closure with pulmonary valve excision, three had ductus arteriousus closure. The patients were classified into two groups. I ] Healed endocarditis group: including the patients who had completed a planned cou-rseof antibiotic therapy[N=10], II ] Active endocarditis group: patients in which operations were performed prior to completetion of antibiotic treatment course[N=11]. The indications for operation included congestive heart failure, embolism, and persistent sepsis. Organisms were predominantly streptococcus[N=5] and staphylococcus [N=4] followed by candida, moraxella, and E-coli. By NYHA functional classification, all patients were in Class III or IV preoperatively. There was only one operative mortality in patient from group II. All patients substantially, improved postoperatively with NYHA classification in class I or II. This study shows that early surgical intervention in patients with active endocarditis has desirable outcome.