• Journal of Korean Home Economics Education Association
• /
• v.35 no.1
• /
• pp.105-124
• /
• 2023

The purpose of this study was to identify the characteristics of the contents included in the curriculum and 382 syllabi of the department of home economics education of College of Education in Korea and analyze the correlation by detailed area through the keyword network method. In order to analyze the home economics education curriculum and 382 syllabuses of a total of 11 universities, the frequency of keyword occurrence was analyzed using the KrKwic program, also the degree of connection between keywords and various centrality scales were calculated and visualized. The results of this study were as follows. First, as a result of analyzing the entire syllabi, keywords representing various fields such as family, secondary school, clothing, food, consumer, and design appeared evenly, and keywords related to teaching methods such as 'method', 'practice', 'change', and 'principle' were appeared. Those keywords showed high degree of connection and centrality. Second, in the detailed sectoral analysis, core keywords for each area appeared, and each subject were found to reflect the core keywords of the academic base. This study contributes to the conversion of curriculum of the department of home economics education to future-oriented and convergent curriculum.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.179-188
• /
• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.8
• /
• pp.107-118
• /
• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• 한국체육학회지인문사회과학편
• /
• v.54 no.4
• /
• pp.329-345
• /
• 2015

Our study examines the relationship between coach turnover and professional sport team performance using the evidences of Korean professional soccer teams. We collected panel dataset of 304 team-year observations and 96 coaches from Korean professional soccer league during the period of 1983-2013. Among them, our final sample is comprised of 226 observations and 81 coaches manifested for fixed-effect generalized least square (GLS) regression analysis. Drawing on sport management literatures and organizational learning theory, we argue that it takes time for a new head coach to take charge of the team in which the new leader who secure more time to interact with organization members is better able to remodel and improve team performance. Our empirical findings reveal that off-season coach turnover has a positive impact while turnover during the season has its negative influences on team performance. In addition, we find that subsequent team performance in association of off-season coach turnover is comparably better than that of on-season succession. The results show that coach succession rendered from inside the professional soccer team mediates the relationship between coach turnover and team performance. Our findings imply that coach turnover in professional sport teams is a significant factor affecting team performance.

• Journal of the Korean Society for Library and Information Science
• /
• v.57 no.2
• /
• pp.31-50
• /
• 2023

The aim of this study is to explore the purpose and method of reading by examining relevant research from various academic fields. According to the reading theory in the Cho-seon Dynasty, reading was classified as either a way of gaining fame, becoming a gentle man, or solving problems. However, this views have been largely replaced by the belief that reading serves two main purposes: self-discipline and practical usage in this study (Confucian perspectives have been excluded from this approach). The traditional reading method, known as sukookdok-jeongsa, influenced by Chu-tzu's reading, emphasized a fluent reading and a deep reading. A fluent reading (sukookdok) method involved a reading aloud, memorizing, and a repeated reading for the literal decoding. After decoding, a deep reading (jeongsa) involved a reading while taking notes, a reading with reference and a repeated reading for the optimal comprehension. A fluent reading in the traditional reading theory is succeeded by 'a reading for liberal arts' and a deep reading is succeeded by 'a reading for learning'. The sukookdok-jeongsa's various reading methods are useful enough to apply to reading education in the school library. But 'a reading for fun' did not appear in the traditional reading theory.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.205-218
• /
• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.368-375
• /
• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.185-198
• /
• 2023

In marine seismic surveys, the inevitable occurrence of trace gaps in the near offset resulting from geometrical differences between sources and receivers adversely affects subsequent seismic data processing and imaging. The absence of data in the near-offset region hinders accurate seismic imaging. Therefore, reconstructing the missing near-offset information is crucial for mitigating the influence of seismic multiples, particularly in the case of offshore surveys where the impact of multiple reflections is relatively more pronounced. Conventionally, various interpolation methods based on the Radon transform have been proposed to address the issue of the nearoffset data gap. However, these methods have several limitations, leading to the recent emergence of deep-learning (DL)-based approaches as alternatives. In this study, we conducted an in-depth analysis of two representative DL-based studies to scrutinize the challenges that future studies on near-offset interpolation must address. Furthermore, through field data experiments, we precisely analyze the limitations encountered when applying previous DL-based trace interpolation techniques to near-offset situations. Consequently, we suggest that near-offset data gaps must be approached by extrapolation rather than interpolation.

• Journal of Korea Water Resources Association
• /
• v.56 no.12
• /
• pp.939-953
• /
• 2023

Shallow water equations (SWE) serve as fundamental equations governing the movement of the water. Traditional numerical approaches for solving these equations generally face various challenges, such as sensitivity to mesh generation, and numerical oscillation, or become more computationally unstable around shock and discontinuities regions. In this study, we present a novel approach that leverages the power of physics-informed neural networks (PINNs) to approximate the solution of the SWE. PINNs integrate physical law directly into the neural network architecture, enabling the accurate approximation of solutions to the SWE. We provide a comprehensive methodology for formulating the SWE within the PINNs framework, encompassing network architecture, training strategy, and data generation techniques. Through the results obtained from experiments, we found that PINNs could be an accurate output solution of SWE when its results were compared with the analytical method. In addition, PINNs also present better performance over the Artificial Neural Network. This study highlights the transformative potential of PINNs in revolutionizing water resources research, offering a new paradigm for accurate and efficient solutions to the SVE.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.39-57
• /
• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.