• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.49-57
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• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.177-188
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• 2022

The support vector machine (SVM) has been successfully applied to various classification areas with a high level of classification accuracy. However, it is infeasible to use the SVM in analyzing massive data because of its significant computational problems. When analyzing imbalanced data with different class sizes, furthermore, the classification accuracy of SVM in minority class may drop significantly because its classifier could be biased toward the majority class. To overcome such a problem, we propose the DOC-SVM method, which uses divide-oversampling and conquers techniques. The proposed DOC-SVM divides the majority class into a few subsets and applies an oversampling technique to the minority class in order to produce the balanced subsets. And then the DOC-SVM obtains the final classifier by aggregating all SVM classifiers obtained from the balanced subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.510-518
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• 2022

The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.25-31
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• 2022

The purpose of this study is to explore the implications for the systematic operation of the AI curriculum by analyzing the current status of the AI major curriculum in universities. To this end, This study analyzed the relevant curriculum of domestic universities(a total of 51 schools) and overseas QS Top 10 universities based on the industry demand-based standard of AI major curriculum developed through prior research. The main research results are as follows. First, in the case of domestic universities, Python-centered programming subjects were lacking. Second, there were few subjects for advanced learning such as AI application and convergence. Third, the subjects required to perform the AI developer job were insufficient. Fourth, in the case of colleges, the ratio of AI mathematics-related subjects was low. Based on these results, this study presented implications for the systematic operation of the AI major education.

• Education of Primary School Mathematics
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• v.25 no.2
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• pp.161-178
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• 2022

Korean uses a dual numeral system consisting of native and Chinese words. This dual numerical system is customarily selected in real life, mixed with two methods, or irregularly transformed. Therefore, the burden on both students and teachers is increased in the learning guidance process of numeral. This study recognized the need to improve the difficulty of learning guidance due to the dual numeral system. To this end, the context in which the numeral system method is selected, various modified cases, and related guidance contents of the current curriculum and textbooks were analyzed and organized. As a result of the analysis, there were characteristics of the selection and deformation of the numeral system method, which appears according to the actual situation using numerical. However, the criteria for characteristics were ambiguous and there were no specific guidance guidelines in the curriculum and textbooks. In this case, since the role of the teacher is more important, the teacher should be aware of the detailed characteristics of the actual situation related to the dual numeral system and let the student understand through experience and practice on various aspects of the use of the dual numeral system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1904-1913
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• 2021

With the proliferation of smart phones and the development of IoT technology, it has become possible to collect personal data for public purposes. However, users are afraid of voluntarily providing their private data due to privacy issues. To remedy this problem, mainly three techniques have been studied: data disturbance, traditional encryption, and homomorphic encryption. In this work, we perform simulations to compare them in terms of accuracy, message length, and computation delay. Experiment results show that the data disturbance method is fast and inaccurate while the traditional encryption method is accurate and slow. Similar to traditional encryption algorithms, the homomorphic encryption algorithm is relatively effective in privacy preserving because it allows computing encrypted data without decryption, but it requires high computation costs as well. However, its main cost, arithmetic operations, can be processed in parallel. Also, data analysis using the homomorphic encryption needs to do decryption only once at any number of data.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.189-201
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• 2022

In this study, we propose factor augmentation to improve forecasting power of cryptocurrency return. We consider financial and economic variables as well as psychological aspect for possible factors. To be more specific, financial and economic factors are obtained by applying principal factor analysis. Psychological factor is summarized by news sentiment analysis. We also visualize such factors through impulse response analysis. In the modeling perspective, we consider ARIMAX as the classical model, and random forest and deep learning to accommodate nonlinear features. As a result, we show that factor augmentation reduces prediction error and the GRU performed the best amongst all models considered.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.11-20
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• 2022

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

• Advances in nano research
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• v.13 no.3
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• pp.207-216
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• 2022

In this paper, a novel model is developed for frequency behavior of single walled carbon nanotubes. The governing equation of motion is constructed method based on the Sander theory using Rayleigh-Ritz's method The frequencies enhances on increasing the power law index using simply supported, clamped and clamped free end conditions. The frequency curve for C-F is less than other conditions. It is due to the physical constraints which are applied on the edge of the CNT. It is observed that the C-F boundary condition have less frequencies from the other two conditions. The frequency phenomena for zigzag are insignificant throughout the aspect ratio. Moreover when index of power law is increased then frequencies increases for all boundary conditions. The natural frequency mechanism for the armchair (10, 10) for various values of power law index with different boundary conditions is investigated. Here frequencies decrease on increases the aspect ratio for all boundary conditions. The frequency curves of SS-SS edge condition is composed between the C-C and C-F conditions. The curves of frequency are less significant from small aspect ratio (L/d = 4.86 ~ 8.47) and decreases fast for greater ratios. It is found that the frequencies via aspect ratios, armchair (10, 10) have higher values from zigzag (10, 0). It is due to the material structure which is made by the carbon nanotubes. The power law index have momentous effect on the vibration of single walled carbon nanotubes. The present frequency result is also compared numerically experimentally with Raman Spectroscopy.

• Korean Journal of Community Nutrition
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• v.27 no.5
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• pp.361-372
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• 2022

Objectives: The purpose of this study was to explore the effectiveness of the STEAM (Science, Technology, Engineering, Arts, and Mathematics) education program on the use of specific food groups in improving nutrition-related knowledge and attitude, dietary behavior, creative problem solving, and STEAM attitude. Methods: We selected two classes at a kindergarten in Jeollabuk-do, South Korea. A total of 44 kindergarteners from the two classrooms participated in this study. The experimental group and the control group were formed with 22 students each. The experimental group attended 11 STEAM classes on the use of the grain, fruit, and milk food groups. First, we performed the paired t-test to examine changes from pre-to-post classes for both groups. Then, we used ANCOVA to compare post-test scores between the experimental and control groups with the adjustment of pre-test scores. Results: The results demonstrate that the STEAM education program on the use of the food groups significantly improved (1) nutrition-related knowledge and attitude, and dietary behavior (P < 0.001), (2) creative problem solving (P < 0.001), and (3) STEAM attitude (P < 0.001) in the intervention group when compared with the control group. Conclusions: The STEAM education program on the use of food groups is effective in enhancing nutrition knowledge and attitude, dietary behavior, creative problem solving, and STEAM attitudes among kindergarten students.