• Journal of Electrical Engineering and Technology
• /
• 제9권3호
• /
• pp.1051-1059
• /
• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• Communications for Statistical Applications and Methods
• /
• 제30권5호
• /
• pp.501-516
• /
• 2023

Knowledge distillation (KD) refers to extracting knowledge from a large and complex model (teacher) and transferring it to a relatively small model (student). This can be done by training the teacher model to obtain the activation function values of the hidden or the output layers and then retraining the student model using the same training data with the obtained values. Recently, relational KD (RKD) has been proposed to extract knowledge about relative differences in training data. This method improved the performance of the student model compared to conventional KDs. In this paper, we propose a new method for RKD by introducing a new loss function for RKD. The proposed loss function is defined using the area difference between the teacher model and the student model in a specific hidden layer, and it is shown that the model can be successfully compressed, and the generalization performance of the model can be improved. We demonstrate that the accuracy of the model applying the method proposed in the study of model compression of audio data is up to 1.8% higher than that of the existing method. For the study of model generalization, we demonstrate that the model has up to 0.5% better performance in accuracy when introducing the RKD method to self-KD using image data.

• 한국경영과학회지
• /
• 제18권2호
• /
• pp.57-81
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• 제28권3호
• /
• pp.217-232
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.629-640
• /
• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Communications for Statistical Applications and Methods
• /
• 제30권3호
• /
• pp.331-341
• /
• 2023

Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.

• 한국의상디자인학회지
• /
• 제23권3호
• /
• pp.87-98
• /
• 2021

Due to the influence of COVID-19, many changes have been made in the education methods in universities. In respomse, this study intendsto present an efficient learning method by identifying the impact of professor images on learning immersion and the learning satisfaction of classes taught with blended learning for university students majoring in beauty at H University in Seoul. For final analysis, 232 of the 234 questionnaires administered from May 17, 2021 to June 2, 2021 were analyzed. For statistical analysis, SPSS 21.0 was utilized; frequency analysis was conducted to identify demographic characteristics, factor analysis was used to verify the research model, and regression analysis was conducted to verify the hypothesis. First, images of professors have been shown to affect learning immersion. Second, the professor image were shown to affect learning satisfaction. Third, education immersion has been shown to affect educational satisfaction. In order to overcome the limitations of online lectures in universities that suddenly began with onset of COVID-19, it is believed that students' satisfaction can be increased by applying blended learning as a way to improve the quality of classes.

• 영재교육연구
• /
• 제19권1호
• /
• pp.119-140
• /
• 2009

영재교육진흥법에 의거 영재교육의 양적 확대라는 취지에서 사이버영재교육이 본격적으로 시행되고 있지만 그 중요성에 비해 사이버영재교육에 대한 운영 및 교육방법 등과 같은 이슈들에 대한 연구가 미비한 상황이다. 이에 본 논문에서는 현재 운영 중인 영재교육기관과 관련 교육법 등을 비교 분석하여 사이버영재교육원에 대한 개념을 새롭게 정의하고, 사이버공간에서 효율적으로 적용 가능한 사이버영재교육용 교수-학습 모형과 투입전략을 개발하였다. 개발된 사이버영재교육 교수-학습 모형과 투입전략에 대한 검증을 위하여 2008년 경기도 사이버영재교육지원 센터 초등정보반학생들을 대상으로 각 모형과 전략별로 만족도와 참여도를 조사 분석하였다.

• 한국도로학회논문집
• /
• 제19권4호
• /
• pp.1-7
• /
• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• 대한치과기공학회지
• /
• 제44권4호
• /
• pp.132-143
• /
• 2022

Purpose: The study aims to analyze differences in the satisfaction level of dental technology students regarding in-person and online classes. It also aims to provide fundamental resources for the improvement of major subject class methods that will improve students' self-directed learning abilities, thereby affecting their class satisfaction. Methods: In this study, a self-administered questionnaire was conducted from November 8 to November 30, 2021, for 256 dental technology students. The collected data were analyzed using the IBM SPSS Statistics ver. 21.0 statistical program. Frequency and percentage, mean, standard deviation, t-test, ANOVA, post-hoc test, correlation analysis, and linear regression analysis were performed to analyze the data. Results: In the self-directed learning abilities, the attitude of the learners was shown to have the highest positive (+) correlation in both in-person and online classes, with a statistically significant effect (p<0.001) on class satisfaction in major subject classes. Moreover, the explanatory power of the model was 52.2% and 39.7%, respectively. Conclusion: We concluded from the study that there is a need for professors to improve teaching methods to increase learners' self-directed learning competence, through problem-based learning, discussion learning, team-based collaborative learning, and mentor-mentee learning, thereby enabling learners to lead classes themselves.