• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.186-194
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• 2004

Due to the exponential growth of information on the internet, it is getting difficult to find and organize relevant informations. To reduce heavy overload of accesses to information, automatic text classification for handling enormous documents is necessary. In this paper, we describe structure and implementation of a document classification system for web documents. We utilize SVM for documentation classification model that is constructed based on training set and its representative terms in a directory. In our system, SVM is trained and is used for document classification by using word set that is extracted from information and communication related web documents. In addition, we use vector-space model in order to represent characteristics based on TFiDF and training data consists of positive and negative classes that are represented by using characteristic set with weight. Experiments show the results of categorization and the correlation of vector length.

• Journal of Korean Clinical Nursing Research
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• v.18 no.3
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• pp.354-367
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• 2012

Purpose: This study was done to verify validity and reliability of a neonatal patient classification system (NeoPCS-1). Methods: An expert group of 8 nurse managers and 40 nurses from 8 Neonatal Intensive Care Units in Korea, verified content validity of the measurement using item level content validity index (I-CVI). The participants were nurses caring for 469 neonates. Data were collected from November 11 to December 14, 2011 and analyzed using descriptive statistics, ANOVA, intraclass correlation coefficient, and K-cluster analysis with PASW 18.0 program. Results: Nursing domains and activities included 8 items with 91 activities. I-CVI was above .80 in all areas. Interrater reliability was significant between two raters (r=.95, p<.001). Classification scores for participants according to patient types and nurses' intuition were significantly higher for the following patients; gestational age (${\leq}29$ weeks), body weight (<1,000 gm), and transfer from hospital. Six groups were classified using cluster analysis method based on nursing needs. Patient classification scores were significantly different for the groups. Conclusion: These results show adequate validity and reliability for the NeoPCS-1 based on nursing needs. Study is needed to refine the measurement and develop index scores to estimate number of nurses needed for adequate neonatal care.

• Journal of the Korean Dietetic Association
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• v.7 no.3
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• pp.295-306
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• 2001

The purpose of the study was to develop an internet nutritional counseling program using an expert system to assist obese people to lose weight through behavior modification. The internet counseling program for weight loss was developed by the accumulation of knowledge dealing with eating habits and exercising behaviors in expert system tool, Knowledge Engineering Agent (KEA) by a dietitian without any help of computer expert. To accumulate knowledge into KEA, survey was performed in 150 obese people, dietitians reviewed and consulted each survey case, and the consulted contents were learned and accumulated into KEA. Survey questionnaire was the same as that of the internet consulting program, and it included general characteristics, dietary habits, lifestyle, and exercise patterns related to obesity. Also, the dietitian selected proper factors inferred from the survey questionnaire of each case, and added the conclusions for them. Conclusions were made for helping clients to correct bad eating behaviors and accumulate good behaviors to lose weight. Counseling was divided into two parts; a two-week part and a daily part. Two-week counseling was performed based on 4 step questionnaires, and daily counseling was done for daily food consumption and physical activity. When clients answered survey questionnaires in a counseling internet program, the recommendations on how to eat, to exercise and to deal with stress in a real time for each case, was given. In conclusion, a counseling internet program for weight reduction can be used to give advices how to deal with obesity in a man-to-man way in a real time using KEA where nutritional knowledge based on behavior modification for weight loss was accumulated.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.219-226
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• 2003

It takes a lot of time to classify official documents in schools and educational institutions. In order to reduce the overhead, we propose an automatic document classification method using word information of the titles of documents in this paper. At first, meaningful words are extracted from titles of existing documents and Inverse Document Frequency(IDF) weights of words are calculated against each category. Then we build a word weight dictionary. Documents are automatically classified into the appropriate category of which the sum of weights of words of the title is the highest by using the word weight dictionary. We also evaluate the performance of the proposed method using a real dataset of a middle school.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.282-289
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• 2008

Although there were some technological developments in improving the collaborative filtering, they have yet to fully reflect the actual relation of the items. In this paper, we propose the recommendation system using associative web document classification by word frequency and ${\alpha}$-cut to address the short comings of the collaborative filtering. The proposed method extracts words from web documents through the morpheme analysis and accumulates the weight of term frequency. It makes associative rules and applies the weight of term frequency to its confidence by using Apriori algorithm. And it calculates the similarity among the words using the hypergraph partition. Lastly, it classifies related web document by using ${\alpha}$-cut and calculates similarity by using adjusted cosine similarity. The results show that the proposed method significantly outperforms the existing methods.

• The Journal of Korean Physical Therapy
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• v.24 no.6
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• pp.398-404
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• 2012

Purpose: This is designed to study the effect of weight-support walking training through motor learning on motor functions of children with cerebral palsy, in particular their activity of daily living and balance. Methods: Thirteen children with spastic cerebral palsy, at gross motor function classification system (GMFCS) levels III~IV, underwent treadmill walking training. It used principles of weight support, 4 times a week for 7 weeks, 10 minutes at a time, before and after neurodevelopmental physical therapy. Everyday functions were measured using Functional Independence Measure for Children (Wee-FIM). The ability to keep their balance was measured using electronic measuring equipment from good balance system and the assessment was made before and after the experiment. Results: There were significant differences (p<0.05) between pre and post experiment levels of functional independence in everyday life, in self-care activities, mobility, locomotion and social cognition. With regard to changes in standing balance, there were significant differences before and after the experiment (p<0.05) in GMFCS level III. There was a reduction in the agitation velocity in the x- and y-axes which measures the left-to-right shaking; in GMFCS level IV, velocity moment was reduced. Conclusion: Walking training using a treadmill can help improve the everyday activity and balance in children with spastic cerebral palsy. It can also be served as a useful purpose as a method of intervention in pediatric care.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1347-1355
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• 2015

In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)²PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)²PCA-based face pose classification performs recognition performance with PCA, (2D)²PCA and RBFNNs.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.5 s.142
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• pp.737-744
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• 2005

This study was aimed to provide the measurement data and shape classification of the head of the Korean male children for the headwear sizing systems. Five hundred twenty male children, aged nine to twelve years, participated f3r this study. The 17 regions on the head and height, weight of the subjects were directly measured by the expert experimenters. Factor analysis, cluster analysis, GLM analysis and Tukey HSD test were performed using these data. Through factor analysis, low factors were extracted upon factor scores and those factors comprised $69.76\%$ for the total variances. Three clusters as their head shape were categorized using four factor scores by cluster analysis. Type 1 was characterized by the widest width and Bitragion arc, shortest head length. Type 2 had the longest head length and the widest side width and the highest head length and head circumference. Type 3 was characterized by the smallest head circumstance, head width and side width, and medium head length.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.194-202
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• 2018

Purpose: It is necessary to set up a standardized method for classifying mixed soil layer that contains sand, gravel and boulder for engineering purposes. Method: Different size of soils was classified mixed soil layer by suggests unified soil classification method. Results: This paper suggests unified soil classification model for different size of soils where many authorities have their own system. Conclusion: Soil stratum classification method using appearing frequencies of gravels and weight ratio of boulders could be used to judgement in many cases.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.