• Child Health Nursing Research
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• v.7 no.2
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• pp.191-202
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• 2001

This study was attempted to help in explore new direction about classification of the severity 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 from March 1, to May 31,1999. The results were as follows: 1. The degree of severity of the pediatric patients visiting emergency center shown ranged 0-18 and averaged .87. 2. With the respect to the severity of the pediatric patients visiting emergency center, there were statiscally significant difference in patients' visiting time(F=2.607, p=.025), disease classification(F=9.606, p=.000), consciousness level(F=71.499, p=.000), period of symptom manifestation (F=2.262, p=.030), pediatric patients protector's thinking about pediatric patients state (F=16.833, p=.000), treatment outcome (t=5.362, p=.000), duration of stay at emergency center(F=23.944, p=.000).

• ETRI Journal
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• v.31 no.1
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• pp.10-20
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• 2009

In this paper, a video broadcasting system between a home-server-type device and a mobile device is proposed. The home-server-type device can automatically extract semantic information from video contents, such as news, a soccer match, and a baseball game. The indexing results are utilized to convert the original video contents to a digested or arranged format. From the mobile device, a user can make recording requests to the home-server-type devices and can then watch and navigate recorded video contents in a digested form. The novelty of this study is the actual implementation of the proposed system by combining the actual IT environment that is available with indexing algorithms. The implementation of the system is demonstrated along with experimental results of the automatic video indexing algorithms. The overall performance of the developed system is compared with existing state-of-the-art personal video recording products.

• Journal of The Korean Association For Science Education
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• v.16 no.3
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• pp.270-277
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• 1996

The purpose of this study is to classify and state of evaluating objectives using three-dimensional assessment framework of science inquiry. The first, as an attempt to provide a theoretical base for developing an assessment framework taxonomies and classificatory schemes of educational objectives were analyzed Bloom's taxonomy, Klopfer's specification, NAEP(National Assessment of Educational Progress), and APU(Assessment of Performance Unit) framework. The second, three-dimensional assessment framework use in this study has formed a clear definition of three-dimensional matrix. These three dimensions consists of content, context and process. The third, the model of three-dimensional taxonomy of science inquiry developed in this study is presented. In addition, an example of classification and statement of evaluating objectives based on the model is presented.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.75-78
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• 1997

In this paper we present new sound spectral analysis methods or prosthetic heart valve sounds. Phonocardiograms(PCG) of prosthetic heart valve were analyzed in order to derive frequency domain feature suitable or the classification of the valve state. The fast orthogonal search method and MUSIC (MUltiple SIgnal Classification) method are described or finding the significant frequencies in PCG. The fast orthogonal search method is effective with short data records and cope with noisy, missing and unequally-spaced data. MUSIC method's key to the performance is the division of the information in the autocorrelation matrix or the data matrix into two vector subspaces, one a signal subspace and the other a noise subspace.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.146-154
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• 2007

AutoCor is a method for the automatic acquisition and classification of corpora of documents in closely-related languages. It is an extension and enhancement of CorpusBuilder, a system that automatically builds specific minority language corpora from a closed corpus, since some Tagalog documents retrieved by CorpusBuilder are actually documents in other closely-related Philippine languages. AutoCor used the query generation method odds ratio, and introduced the concept of common word pruning to differentiate between documents of closely-related Philippine languages and Tagalog. The performance of the system using with and without pruning are compared, and common word pruning was found to improve the precision of the system.

• Korean Journal of Human Ecology
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• v.2 no.1
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• pp.38-51
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• 1999

The purpose of this study was to test the validity of Olson Circumplex Model and Beavers Systems Model related to adolescent adjustment. The 830 subjects were selected from the second grade of middle and high schools and adolescents of Juvenile Judge in the city of Taegu. The survey instruments were FACESIII, SFIII, State-Trait Anxiety Inventory, Depression Scale, and Delinquency Scale. Factor Analysis, Cronbach＇s ${\alpha}$. MANOVA, Scheff＇e test were conducted for the data analysis. The major findings of this study were as follows: 1) Family system classification method on Olson Circumplex Model was partially useful in evaluating anxiety, depression, and delinquency of adolescent. 2) Family system classification method on Beavers Systems Model was partially useful in evaluating anxiety and depression of adolescent. (Korean J Human Ecology 2(1) : 38~51, 1999)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1105-1112
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• 2020

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.