• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.899-908
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• 2019

Massive in-depth neural networks with numerous parameters are powerful machine learning methods, but they have overfitting problems due to the excessive flexibility of the models. Dropout is one methods to overcome the problem of oversized neural networks. It is also an effective method that randomly drops input and hidden nodes from the neural network during training. Every sample is fed to a thinned network from an exponential number of different networks. In this study, instead of feeding one sample for each thinned network, two or more samples are used in fitting for one thinned network known as a Hybrid Dropout. Simulation results using real data show that the new method improves the stability of estimates and reduces the minimum error for the verification data.

• Education of Primary School Mathematics
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• v.20 no.4
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• pp.287-303
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• 2017

In order to understand the characteristics of affect of elementary school students in this study, we used a questionnaire developed by Hannula (2012) to measure elementary students' beliefs and affective factors about mathematics based on the emotional, cognitive, and motivational dimensions of the affect of personal level. Statistical analysis and one-way ANOVA were conducted to identify the characteristics of elementary school students' beliefs and affective factors about mathematics according to mathematics achievement level, grade level, and gender. Regression analysis was performed to analyze the correlation between the factors. The results of this study are compared with the results of the previous study which used comparative study of elementary school students in Finland and Chile using the questionnaire used in this study.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.859-872
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• 2016

Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.591-601
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• 2020

The self-organizing map (SOM) is a unsupervised learning method projecting high-dimensional data into low-dimensional nodes. It can visualize data in 2 or 3 dimensional space using the nodes and it is available to explore characteristics of data through the nodes. To understand the structure of data, cluster analysis is often used for nodes obtained from SOM. In cluster analysis, the optimal number of clusters is one of important issues. To help to determine it, various cluster validity indexes have been developed and they can be applied to clustering outcomes for nodes from SOM. However, while SOM has an advantage in that it reflects the topological properties of original data in the low-dimensional space, these indexes do not consider it. Thus, we propose a new cluster validity index for SOM based on connectivity between nodes which considers topological properties of data. The performance of the proposed index is evaluated through simulations and it is compared with various existing cluster validity indexes.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Analytical Science and Technology
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• v.33 no.2
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.23-32
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• 2015

Modeling of fighter pilots, which is a fundamental technology for war games using defense M&S (Modeling & Simulation) becomes one of the prominent research issues as the importance of defense M&S increases. Especially, the recent accumulation of combat logs makes it possible to adopt statistical learning methods to pilot modeling, and an HMM (Hidden Markov Model) which is able to utilize the sequential characteristic of combat logs is suitable for the modeling. However, since an HMM works only by using one type of features, discrete or continuous, to apply an HMM to heterogeneous features, type integration is required. Therefore, we propose a dPCA-HMM method, where dPCA (Discrete Principal Component Analysis) is combined with an HMM for the type integration. From experiments conducted on combat logs acquired from a simulator furnished by agency for defense development, the performance of the proposed model is evaluated and was satisfactory.

• Journal of the Korean Society of Earth Science Education
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• v.5 no.1
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• pp.60-67
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• 2012

The purpose of this study is to find out the effect of hypothesis formulations using abduction on science processing skills and the creative thinking activities. As the subject, 2 classes in the $6^{th}$ grade of B elementary school located in Busan were selected. Through the pre/post inspection design between experiment and comparison class, the units of science courses in the second semester of $6^{th}$ grade '1. A change in the weather' and '2. Various gases' were applied. The results were as follows: Firstly, the test on science processing skills showed that there was not statistic meaningful differences between the two groups. And, in the sub-parts, there was not statistic meaningful differences between the two groups. Secondly, it was observed that it would have a meaningful effect to improve the creative thinking activities of students who performed hypothesis formulation using abduction. Especially, through this, the experimental class gave a positive effect on the 'Fluency' and 'Elaboration', one of lower categories of the creative activities. The results of 'Flexibility' and 'Originality' in the experimental class were higher than those of students in the comparative class. However, according to statistical analysis, this result is meaningless. Thirdly, on the survey about the hypothesis formulation using abduction, many students thought that this learning method was very interesting and helpful to study science. In addition, it was observed that the ability to use abduct thinking was improved more than ever before.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.1
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• pp.54-64
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• 2015

Purpose: The purpose of this study was to investigate the effects of simulation in nursing education based on caring for elderly cognition disorder patients. The education consisted of a caring program for patients that included a process of assessment of a patient's mental status, diagnosis of the patient's health condition, and intervention to address the problems by using therapeutic communication. Methods: A nonequivalent control group pretest-posttest design was used. A total of 69 subjects (undergraduate students) participated in the education and they were assigned to two groups: the experimental group (n=32) and the control group (n=37). Data-gathering structured questionnaires that included communication competence, academic self-efficacy, and attitudes about the elderly. The data were collected from October 2013 to December 2013, and statistical analyses were conducted with-test and t-test using the SPSS 21.0 program. Results: With respect to education, there was significant improvement in communication competence in the experiment group (t=2.41, p=.022) compared with in the control group (t=.69, p=.494). However, there was no statistically significant difference in academic self-efficacy and attitude about the elderly. Conclusion: Simulation-based education should continue to be developed further for better elderly-patient care. Integrated education in particular using a high-fidelity simulator will contribute to improvements in nursing competence in this area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.554-564
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• 2012

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.