• Journal of Korean Home Economics Education Association
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• v.10 no.1
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• pp.137-151
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• 1998

The purpose of this study is (1)to develop the teaching plan based on Cooperative Learning approach and (2)to investigate the effect of students'Interest on Subject matter and Teaching method and Attitudes to others of the area of Foreign food in Home Economics class. Among those various types of Cooperative Learning's models, this study adopted 'Learning Together'developed by Johnsons. To investigate these purpose, subject matter were analyzed and reconstructed for Cooperative Learning. The tests were developed to evaluate the interest on the Subject matter and teaching methods, and the attitude to others of the students. 108 femail high school students were divided into two groups with 54 students-traditional learning condition, Cooperative Learning condition-and had a 5 session. The subject of the class was Foreign food including Western, Chinese, and Japanes food. Before and after the class, students were tested. The statistical methods used for the study methods used for the study were t-test. The research findings are as follows : When the students in the Cooperative Learning classes were compared before and after the test, (1)Interest on Subject matter were improved considerably(p〈.001) (2)Interest on Teaching methods were improved considerably(p〈.05) (3)Attitude to Others were improved considerably(p〈.001) Therefore when the teaching-learning model based on Cooperative Liarning was used in Home Economics class, their interest on the subject and teaching methods and attitude to others were improved.

• Journal of Korean Society of Archives and Records Management
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• v.8 no.1
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• pp.143-165
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• 2008

This study is concerned with educational services provided for archives with a focus on programs for teachers and students in the classroom. The purpose of this study is to develop the archival-resource based learning model. And the other purpose is to find out the influence of the archival-resource based learning. The researcher and teachers designed two lessen plans for archival-resource based learning and general learning. To compare with the archival-resource based learning and the general learning, the researcher divided into two comparison classes of 6th graders of two elementary schools. Statistical analysis was conducted by analysis of covariance using SPSS WIN 12.0 for t-test.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.21-27
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• 2021

The paper, in an online learning environment which becomes prolonged for reason of COVID-19, on 80 students belonging to the aviation and aeronautics, proposed more concrete direction for raising effectiveness of online class by analyses of various standpoints regarding the relation between 8 detailed elements of self-initiated learning ability and learning satisfaction. As a result of analyses, first, it turned out that, among detailed elements of self -initiated learning ability, relatively high scores were showed in basic self management ability, grasp of resources for learning, attribution of efforts to results, and selection of learning strategies. Second, in correlation between self-initiated learning ability detailed elements and learning satisfaction and technical statistical analyses, the score of the two elements: learning goal setting and continuance of learning execution is low, so that it is necessary to note the two elements. Third, as to self-initiated learning ability, the average of female students is high and in learning satisfaction, the average of male students was high. Fourth, it was found that the first-class students showed significantly high learning satisfaction compared with the second-class students. Fourth it turned out that, regarding a difference, both of self-initiated learning ability and learning satisfaction were dismissed and thus there is no difference. Fifth, as to the effects of self-initiated learning ability on learning satisfaction, both of a corelation analysis and a regression analysis showed significant results. Accordingly, self-initiated learning ability in online class has a very significant effect on learning satisfaction.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.879-894
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• 2003

We evaluated the efficiencies of applying attribute selection methods and prior discretization to supervised learning, modelled by C4.5 and Naive Bayes. Three databases were obtained from UCI data archive, which consisted of continuous attributes except for one decision attribute. Four methods were used for attribute selection : MDI, ReliefF, Gain Ratio and Consistency-based method. MDI and ReliefF can be used for both continuous and discrete attributes, but the other two methods can be used only for discrete attributes. Discretization was performed using the Fayyad and Irani method. To investigate the effect of noise included in the database, noises were introduced into the data sets up to the extents of 10 or 20%, and then the data, including those either containing the noises or not, were processed through the steps of attribute selection, discretization and classification. The results of this study indicate that classification of the data based on selected attributes yields higher accuracy than in the case of classifying the full data set, and prior discretization does not lower the accuracy.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.381-396
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• 2017

Structural health monitoring (SHM) is a necessity for reliable and efficient functioning of engineering systems. Damage detection (DD) is a crucial component of any SHM system. Lamb waves are a popular means to DD owing to their sensitivity to small damages over a substantial length. This typically involves an active sensing paradigm in a pitch-catch setting, that involves two piezo-sensors, a transmitter and a receiver. In this paper, we propose a data-intensive DD approach for beam structures using high frequency signals acquired from beams in a pitch-catch setting. The key idea is to develop a statistical learning-based approach, that harnesses the inherent sparsity in the problem. The proposed approach performs damage detection, localization in beams. In addition, quantification is possible too with prior calibration. We demonstrate numerically that the proposed approach achieves 100% accuracy in detection and localization even with a signal to noise ratio of 25 dB.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.299-308
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• 2014

Due to indiscriminately received spam messages on information society, spam messages cause damages not only to person but also to our community. Nowadays a lot of spam filtering techniques, such as blocking characters, are studied actively. Most of these studies are content-based spam filtering technologies through machine learning.. Because of a spam message transmission techniques are being developed, spammers have to send spam messages using term spamming techniques. Spam messages tend to include number of nouns, using repeated words and inserting special characters between words in a sentence. In this paper, considering three features, SPSS statistical program were used in parameterization and we derive the equation. And then, based on this equation we measured the performance of classification of spam messages. The study compared with previous studies FP-rate in terms of further minimizing the cost of product was confirmed to show an excellent performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.217-232
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• 2021

The Korea film industry has matured and the number of movie-watching per capita has reached the highest level in the world. Since then, movie industry growth rate is decreasing and even the total sales of movies per year slightly decreased in 2018. The number of moviegoers is the first factor of sales in movie industry and also an important factor influencing additional sales. Thus it is important to predict the number of movie audiences. In this study, we predict the cumulative number of audiences of films using stacking, an ensemble method. Stacking is a kind of ensemble method that combines all the algorithms used in the prediction. We use box office data from Korea Film Council and web comment data from Daum Movie (www.movie.daum.net). This paper describes the process of collecting and preprocessing of explanatory variables and explains regression models used in stacking. Final stacking model outperforms in the prediction of test set in terms of RMSE.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.2-109
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• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...