• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.245-271
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• 2023

The purpose of this study was to assess the perception of e-book subscription services among the digitally native generation in their twenties, who have a high e-book usage rate. This study employed a mixed-methods approach, combining survey responses and usability testing. It aimed to assess the awareness and usage of e-book subscription services among university students in their twenties, a demographic known for their high utilization of electronic devices and e-books. The survey was conducted among 202 university students, and the responses were categorized and examined based on whether they were users or non-users. As a result of the survey, I found there is different awareness of e-book between users and non-users, on the other hand, convenience and portability are the strong point of e-books for users and non-users commonly also. Usability testing was performed on a group of 10 university students in their twenties who had not previously used the 'Millies Library' application, which is renowned as the most widely-used e-book platform. Following the experiment, participants expressed positive feedback regarding various optional features, convenience, design, and cost-effectiveness. However, they also had negative reactions concerning touch errors, malfunctions, functional practicality, a lack of interest, system issues, and the absence of a library.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.57-65
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• 2019

This study is a scientific approach to psychological factors such as emotional stability among various effects of forest resources. In order to carry out this study, the experiment was conducted on the subjects by setting the forest healing space as various spaces. The subjects who participated in this experiment were the students in their twenties and the average age was 22±1.25 years. The subjects were assessed for emotional words through subjective sequence evaluation in different designated forest healing spot. In addition, the emotional states that they actually perceived were measured by measuring the bio-signals to their perceived emotions. BMP, SDNN, VLF, LF, HF, Amplitude, and PPI were used for the bio-signal reaction experiment applied to this study. The results of this experiment were measured by Friedman test and Wilcoxon test for statistical analysis. n this study, 'good', 'clear', and 'uncomfortable' words were found statistically significant at the spot of forest healing space for subjective emotional vocabulary. In addition, SDNN, HF and Amplitude were statistically significant in the results of quantitative bio-signal measurement at each spot in the forest healing space. Based on the results of this study, we can suggest the application direction and strategic utilization plan of forest healing spot and forest resource utilization field. This is not only a guide for the users who use the facility through the spatial facilities and physical requirements for the emotion based forest-healing, but also can be used as a personalized emotional space design aspect.

• Korean Journal of Forensic Psychology
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• v.11 no.3
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• pp.349-370
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• 2020

This study was to test the effects of unanticipated questions on the number of general and verifiable details. In addition, the number of verifiable details would discriminate truth-tellers and liars more accurately than the number of general details. In a 2(Veracity: truth vs. lie) X 2(Question type: Expected questions vs. Unexpected questions) mixed-design study, truth tellers(N=40) were asked to visit a cafe on campus and liars(N=40) were told to fabricated a story as if they visited the cafe. Then, participants were interviewed about their trip to the cafe and asked four questions(two anticipated questions: 'report the trip in detail', 'describe the place'; two unanticipated questions: 'recall in reverse order', 'report verifiable details'). Each participant's statements were transcribed and coded by trained graduate students for the number of general details and verifiable details. The results showed that truth-tellers mentioned significantly more general details than liars regardless of the question type. On the contrary, there was no significant difference between liars and truth-tellers in the number of verifiable details. High percentages of truth-tellers(62.5%) and liars(80.0%) were classified correctly based on the number of general details whereas only 45.0% of truth tellers and 62.5% of liars were accurately classified by the number of verifiable details. Liars were found to speak more words when asked to provide verifiable details compared to a general open question, but the number of general details did not seem to increase accordingly. The limitations of this study and future research directions were discussed.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Korea Trade
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• v.27 no.1
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• pp.139-158
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• 2023

Purpose - The study of co-movements between stock markets is a crucial area of finance and has recently received much interest in a variety of studies, especially in international finance. Stock market co-movements are a major phenomenon in financial markets, but they are not necessarily independent of the real market. Several studies support the idea that bilateral trade linkages significantly impact stock market correlations. Motivated by this perspective, this study investigates whether real market integration due to trade agreements brings about financial market integration in terms of stock market co-movement. Design/methodology - Over the 10 free trade agreements (FTAs) signed by the United States, using a dynamic conditional correlations (DCC) multivariate GARCH (MGRACH) model, we empirically measure the degree of integration by finding DCCs between the US market and the partner country's market. We then track how these correlations evolve over time and compare the results before and after trade agreements. Findings - According to the empirical results, there are positive return spillover effects from the US market to eight counterpart equity markets, except Jordan, Morocco, and Singapore. Especially Mexico, Canada, and Chile have large return spillover effects at the 1% significance level. All partner countries of FTAs generally have positive correlations with the US over the entire period, but the size and variance are somewhat different by country. Meanwhile, not all countries that signed trade agreements with the United States showed the same pattern of stock market co-movement after the agreement. Korea, Mexico, Chile, Colombia, Peru, and Singapore show increasing DCC patterns after trade agreements with the US. However, Canada, Australia, Bahrain, Jordan, and Morocco do not show different patterns before and after trade agreements in DCCs. These countries generally have the characteristic of relatively lower or higher co-movements in stock markets with the US before the signing of the FTAs. Originality/value - To our knowledge, few studies have directly examined the linkages between trade agreements and stock markets. Our approach is novel as it considers the problem of conditional heteroscedasticity and visualizes the change of correlations with time variations. Moreover, analyzing several trade agreements based on the United States enables the results of cross-country pairs to be compared. Hence, this study provides information on the degree of stock market integration with countries with which the United States has trade agreements, while simultaneously allowing us to track whether there have been changes in stock market integration patterns before and after trade agreements.

• Journal of Christian Education in Korea
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• v.74
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• pp.57-80
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• 2023

The purpose of this study is to review the practicality of Christian education for sustainable development in a rapidly changing world. The first part of this study identify the concept, meaning, and direction of implementation of "Sustainable Development," which has been studied and published around UNESCO since the early 1980s, and present practical strategies for the sustainable development of Christian education. This study chronologically selected five major reports published by UNESCO--"Our Common Future"(1987), "Agenda 21"(1992), "UN Decade of Education for Sustainable Development 2005-2014"(2002), "Roadmap for Implementing the Global Action Programme for Education for Sustainable Development 2015-2019"(2014) and "Education for Sustainable Development 2030"(2020)--and examined the concept and meaning of "Sustainable Development"(SD). At the same time, in relation to "Education for Sustainable Development"(ESD), the occurrence, change, and implementation method of "Sustainable Development Goals"(SDGs) were examined and presented. This study derived three Christian educational implications necessary to properly establish the next generation of faith, based on a leadership development strategy using the concept of sustainable development, For the sustainability of Christian education, the foundation of education based on the correct biblical interpretation of cultural mandate is first examined, and then the need for curriculum development and class design is proposed using various types of indicators and educational modules. Finally, specific practices for the development of educational leadership to revitalize Christian education are presented through a multi-dimensional approach.

• Journal of the Korea Institute of Building Construction
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• v.23 no.4
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• pp.337-348
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• 2023

The central objective of this study is to curtail the leakage of mortar or cement paste, often resultant of ill-constructed formwork, by implementing thixotropy in the formulation of high-fluidity, standard-strength concrete. When such concrete is utilized in smaller scale construction projects, instances of formwork gaps due to suboptimal construction precision may lead to significant leakage of mortar and paste, a problem not typically encountered with traditional slump-flow concrete. In this investigation, Polyvinyl Alcohol(PVA) and borax are incorporated into the concrete mixture to induce thixotropy. The experimental design includes varying methodologies for integrating PVA and borax, while assessing alterations in diverse concrete performances, including thixotropy and leakage reduction potential that simulates formwork gap conditions. Under the experimental conditions defined within this study, it was found that replacing, rather than merely adding PVA and borax, aids in averting water addition via suspensions. This approach yielded promising results in terms of concrete properties and proved efficacious in stemming leakage in concrete possessing sufficient thixotropy. Notably, when a 6% PVA suspension was substituted, a significant reduction in leakage was observed. Consequently, it is projected that construction quality can be ensured, even with lower precision formwork, by applying thixotropy to concrete through the use of PVA and borax.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.