• The Journal of Korean Physical Therapy
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• 제26권5호
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• pp.315-323
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• 2014

Purpose: This study was conducted in order to investigate the effect of motor ability on mastery motivation in children with cerebral palsy. Methods: Sixty children with cerebral palsy (5~12 years) and their parents participated in the study. Data on general characteristics and disability condition, Gross Motor Functional Classification System, Manual Ability Classification System, and The Dimensions of Mastery questionnaire were collected for this study. Independent t-test, and ANOVA were used for analysis of the effect of The Dimensions of Mastery questionnaire according to general and disability condition, Gross Motor Functional Classification System, and Manual Ability Classification System. Linear regression analysis was performed to determine the effects of Gross Motor Functional Classification System and Manual Ability Classification System on The Dimensions of Mastery questionnaire. SPSS win. 22.0 was used and Tukey was used for post hoc analysis, level of statistical significance was less than 0.05. Results: The Dimensions of Mastery questionnaire score showed statistically significant difference according to gender, region, type, disability rating, Gross Motor Functional Classification System, and Manual Ability Classification System (p<0.05). Gross Motor Functional Classification System and Manual Ability Classification System were the effect factor on The Dimensions of Mastery questionnaire significantly (p<0.05). Conclusion: These results suggest that motor ability of children with cerebral palsy was an important factor having an effect on The Dimensions of Mastery questionnaire.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• 한국경영과학회지
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• 제32권1호
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.

• 대한기관식도과학회지
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• 제17권2호
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• pp.104-107
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• 2011

Background and Objectives Scope classification is designed to classify acute epiglottitis according to laryngoscopic findings. There is no report about the utility of classification; the difference between the diagnosis and the prognosis by the Scope classification was not found. The aim of this study was to evaluate the utility of Scope classification in patients with acute epiglottitis. Subject and Method 127 patients who had been admitted to our hospital were diagnosed with acute epiglottitis. The patients were classified by the Scope classification. We compared demographic characteristics, clinical symptoms and signs, laboratory findings, and clinical course among the patient groups and divided the results according to the Scope classification. Results There are no significant differences among the groups in demographic characteristics, clinical symptoms and signs, laboratory findings, and clinical course. Conclusion The Scope classification of acute epiglottitis does not seem to be a method to evaluate the severity of acute epiglottitis. Thus, we need to develop multidisciplinary approaches for acute epiglottitis.

• 통상정보연구
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• 제6권3호
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• pp.41-61
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• 2004

Today, most companies are needed to become interested on e-Biz and information management system. Especially, Data classification format system was very important for application to effective and efficiency management decision support. They should include main entry which consists of department, employee's name, title, publication date. Now, each company is using eleven different methods on data classification format system. In this paper finding result was as follows, in other words, general management document case using the nine date classification methods and special report management document ca se using the twodata classification methods. The aim of this study is to investigate problems that the present data classification format system has and some concerns that should be taken into account in case of the modification of the data classification system and change into a new one. This study is based on the survey in that the company managergave to 35 companies throughout the nation. As a result, the survey indicates that the crucial concerns of the participating managers are ineffective management information source and the duplication of data classification systems. This paper is the transcendental study the introduction of data classification format systems to business companies in Korea. This paper provided the fundamental data for the effective business process reengineering in business activity for management information.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• 한국도서관정보학회지
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• 제41권4호
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• pp.5-33
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• 2010

이 연구는 교육학 분야의 학문체계와 KDC, NDC, DDC, LCC의 분류체계 및 한국연구재단의 연구분야분류표의 교육학 분야의 분류체계에 대해 비교 분석한 후, 이를 토대로 KDC 교육학 분야의 분류체계를 개선할 수 있는 방안을 제시하고자 시도하였다. 분석결과 KDC 제5판의 교육학 분야는 학문발전의 추세를 반영하는 분류항목의 추가, 교육학 세부 영역의 등위류 분류용어의 적절한 전개, 세부 주제의 추가 전개, 분류기호의 오류 및 분류항목의 상관색인 누락 등에 대한 개선이 필요한 것으로 나타났다. 이 연구에서는 이러한 문제들을 해결하기 위한 개선 방안을 제시하였다.

• Journal of Biosystems Engineering
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• 제39권2호
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• pp.96-100
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• 2014

Purpose: The objective of this study was to develop a classification index and the grade levels for a five-grade energy efficiency classification of agricultural dryers in Korea. Methods: The classification index and the grade levels were determined by using the performance test data published by the FACT over the last eight years to reflect a state of the art technology for agricultural dryers in Korea. The five grades were designed to have the classified dryers distributed normally over the grades with 15% for the $1^{st}$ grade, 20% for the $2^{nd}$ grade, 30% for the $3^{rd}$ grade, 20% for the $4^{th}$ grade and 15% for the $5^{th}$ grade. Results: The classification index was defined as the total amount of fuel and electrical energy consumed per 1% of the wet basis moisture content evaporated from a unit mass of grain or agricultural crops during the drying process: 1 MT of paddy rice for grain dryers and 1 kg of red pepper for agricultural crop dryers as the standard mass. Conclusions: The grade levels for the five-grade energy efficiency classification of grain dryers, kerosene dryers, and electric dryers were proposed in terms of the classification index value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.6080-6096
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• 2019

Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.

• 대한공간정보학회지
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• 제8권2호
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• pp.47-60
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• 2000

본 연구에서는 인공위성영상을 이용한 토지피복 분류방법 중 파라메트릭한 분류와 비-파라메트릭한 분류의 대표성을 띤 최대우도 분류법과 신경망을 이용한 분류방법을 사용하여 분류정확도를 비교하였다. 분류정확도의 평가에 있어서 일반적인 분석가들이 사용하는 훈련지역에 대한 분류 정확도의 분석뿐만 아니라, 시험지역에 대한 정확도분석을 하였다. 그 결과, 최대우도분류기에 비하여 신경망의 분류기가 일반적인 훈련데이터의 분류에 있어서 약 3% 우월하였으며, 지상검증데이터를 사용한 분류결과에서는 시험에 사용된 두 분류기 모두 빈약한 분류결과를 나타내었으나, 신경망에 의한 분류가 최대우도에 비하여 약 10%정도 보다 신뢰할 수 있는 결과를 얻을 수 있었다.