• Journal of Christian Education in Korea
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• v.68
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• pp.41-76
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• 2021

This paper begins with the question of whether "on-tact" Christian education, which has become the most new-normal phenomenon since Corona 19, will remain as a decisive form of Christian education even in the post-Corona era. In order to answer that question, this study explored whether on-tact Christian education has its own domain of experience and educational elements that cannot be replaced by face-to-face education, specifically focusing on "types of on-tact Christian Education", "discussion of digital church" and "digital epistemology". Through research on "types of onn-tact Christian education," it confirmed that, when viewed on the basis of 'participation' or 'communication', on-tact Christian education has an independent field of experience and educational elements. Through contemplation on "digital ecclesiology", it found that on-tact education is the decisive channel for Christian education to reach digital generation. It also found a new metaphor from the "network" concept for the public church and the Kingdom of God. This paper also found that we experience the perception of the body that is expanded through the combination between the body and technology in the digital world, and that this is a unique epistemology that occurs only in the digital world. Based on the above points, it affirmed that on-tact Christian education is not simply a means of supplementing face-to-face education in the era of COVID-19, but is a Christian education that has an independent field of experience and educational power that face-to-face education cannot replace. Thus it foresees that on-tact Christian education will continue to expand as a center and form of Christian education even in the post-corona era.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.307-324
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• 2022

This study examined the content diversity of seven authorized science textbooks by comparing the characteristics of the science concept description and the contents of inquiry activities in the "weight of objects" unit. For each textbook, the flow of concept description content and the uniqueness of the concept description process were analyzed, and the number of nodes and links and words with high connections were determined using language network analysis. In addition, for the inquiry activities described in each textbook, the inquiry subject, inquiry type, science process skill, and uniqueness were investigated. Results showed that the authorized textbooks displayed no more diversity than expected in their scientific concept description method or their inquiry activity composition. The learning elements, inclusion of subconcepts, and central words were similar for each textbook. The comparison of inquiry activities showed similarities in their contents, inquiry types, and scientific process skills. Specifically, these textbooks did not introduce any research topics or experimental methods that were absent in previous textbooks. However, despite the fact that the authorized textbook system was developed based on the same curriculum, some efforts were made to make use of its strengths. Since the sequence of subconcepts to explain the core contents differed across textbooks, this explanation process was divided into several types, and although the contents of inquiry activities were the same, the materials for inquiry activities were shown differently for each textbook to improve and overcome the difficulties in the existing experiments. These findings necessitate the continuation of efforts to utilize the strengths of certified textbooks.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.91-106
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• 2022

This study targets four Soho fashion shopping malls that are operating successfully in the online fashion market. This study analyzed the entrepreneurship process by dividing it into three stages. The results of the case study are as follows. In the case of Company S, the founder, who had little work experience, started an e-commerce business with a sense of fashion and entrepreneurship. It is a contemporary, casual brand with competitive prices, design power, and diverse product assortment, and the business performance was achieved through data management and analysis and the diversification of distribution channels. In the case of Company B, the founder, who had little work experience, started a manufacturing and e-commerce business by leveraging their SNS network capabilities and entrepreneurial spirit. It is a contemporary fashion brand with product competitiveness of specific items and start-up characteristics, and performance was achieved through the establishment of brand identity and market expansion. Third, Company M and Company C are examples of Soho fashion shopping malls where the founders with more extensive work experience at the time of founding their respective start-ups focused on brand recognition as their core competitiveness. In the case of Company M, the apparel brand was launched with a wealth of experience and design spirit. It is a fashion designer brand that stands out for its sensibility, and the owner has achieved performance through various entrepreneurial activities that broaden the corporate horizon. Company C is a manufacturing and e-commerce brand that was started with design capabilities and an entrepreneurial spirit. It is a luxury fashion brand that focuses on emotional expression, and the outcomes, such as brand recognition and sales, were achieved through active customer management. The results of this study can be used as basic data in education for and research of Soho shopping malls and the prospective founders.

• Knowledge Management Research
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• v.23 no.2
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• pp.193-210
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• 2022

The rapid development of mobile technology and the improvement of network speed are providing convenience to various services, and mobile shopping malls are no exception. Although efforts are being made to promote sales by combining various technologies such as customized recommendations using big data and specialized personalization services based on artificial intelligence, most mobile shopping malls have the same detailed page information structure including detailed product information. In this context, in this study, it was determined that the content layout of the product detail page and the mobile product detail page layout tailored to the consumer's preference should be presented according to the consumer's preference. Based on Higgins' Regulatory Focus Theory, a study of consumer propensity revealed that the content layout arrangement on a product detail page, when presented in an F-shape, informs the consumer that it is organized. If presented in a Z-shape, vivid information was recognized, and it was examined whether the product attitude and purchase intention were affected. As a result, when the content layout composition was presented as a layout arrangement in the form of a sense of unity and organization, prevention-focused consumers were positively affected by product attitudes and purchase intentions, and promotion-oriented consumers felt freedom. When presented in an arrangement, it was confirmed that the product attitude and purchase intention were affected.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• Korean Journal of Animal Reproduction
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• v.24 no.4
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• pp.375-383
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• 2000

The nuclear maturation and developmental competence of immature, oocytes collected from donors at various morphology of corpus luteum (CL) and fertilized in vitro was investigated by comparing the meiotic activity and the yields of embryos. Ovaries were divided and classified into 4 groups as the following criteria : Group 1 ; ovaries showed evidence of recent ovulation (corpus hemorragicum). Group 2 ; apex of CL was red or brown. Vasculization was limited to periphery of CL. Group 3 ; apex of CL was orange or tan. Vasculization was covered over apex of CL. Group 4 ; CL was light yellow to white and firm in texture and the vascular network on the surface of CL had disappeared. Modified TCM 199 was used for maturation in vitro of immature oocytes and development was induced by using TLP-PVA as a basic medium. When oocytes collected from each group of donors had been matured for 4, 14, and 24 hours in vitro, the proportion of oocytes reaching metaphase I and metaphase II were not different among oocytes from 4 group of ovaries. Mature metaphase II stage of oocytes in each group was first observed at 14 hours, whereas completion of maturation of. oocytes in each group was at 24 hours. Luteal morphology of ovaries had little effect on the proportion of embryos reached 2 cells and 8 cell stage. However, the proportion of embryos cleaved to morula and blastocyst stage was significantly higher in the oocytes obtained from group 1 and 3 than in the oocytes from group 2 and 4 (p＜0.05). This data suggest that reproductive status of the donor significantly influence the yield of in vitro embryos.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.187-192
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• 2023

In this study, trends in ICT education were investigated by analyzing the frequency of appearance of keywords related to machine learning and using conversion of iteration correction(CONCOR) techniques. A total of 304 papers from 2018 to the present published in registered sites were searched on Google Scalar using "ICT education" as the keyword, and 60 papers pertaining to ICT education were selected based on a systematic literature review. Subsequently, keywords were extracted based on the title and summary of the paper. For word frequency and indicator data, 49 keywords with high appearance frequency were extracted by analyzing frequency, via the term frequency-inverse document frequency technique in natural language processing, and words with simultaneous appearance frequency. The relationship degree was verified by analyzing the connection structure and centrality of the connection degree between words, and a cluster composed of words with similarity was derived via CONCOR analysis. First, "education," "research," "result," "utilization," and "analysis" were analyzed as main keywords. Second, by analyzing an N-GRAM network graph with "education" as the keyword, "curriculum" and "utilization" were shown to exhibit the highest correlation level. Third, by conducting a cluster analysis with "education" as the keyword, five groups were formed: "curriculum," "programming," "student," "improvement," and "information." These results indicate that practical research necessary for ICT education can be conducted by analyzing ICT education trends and identifying trends.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.368-375
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• 2022

Gut contents analysis is essential to predict the impact of organisms on food source changes due to variations of the habitat environment. Previous studies of gut content analysis have been conducted using traditional methods, such as visual observation. However, these studies are limited in analyzing food sources because of the digestive process in gut organ. DNA metabarcoding analysis is a useful method to analyze food sources by supplementing these limitations. We sampled marine fish of Pennahia argentata, Larimichthys polyactis, Crangon affinis, Loligo beka and Sepia officinalis from Gwangyang Bay and Yeosu fisheries market for analyzing gut contents by applying DNA metabarcoding analysis. 18S rRNA v9 primer was used for analyzing food source by DNA metabarcoding. Network and two-way clustering analyses characterized the relationship between organisms and food sources. As a result of comparing metabarcoding of gut contents for P. argentata between sampled from Gwangyang Bay and the fisheries market, fish and Copepoda were analyzed as common food sources. In addition, Decapoda and Copepoda were analyzed as common food sources for L. polyactis and C. affinis, respectively. Copepoda was analyzed as the primary food source for L. beka and S. officinalis. These study results demonstrated that gut contents analysis using DNA metabarcoding reflects diverse and detailed information of biological food sources in the aquatic environment. In addition, it will be possible to provide biological information in the gut to identify key food sources by applying it to the research on the food web in the ecosystem.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.