• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.546-558
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• 2006

The purpose of this study was to identify heuristically how abduction was used in a context of solving an earth-environmental problem. Thirty two groups of participants with different institutional backgrounds, i,e., inservice earth science teachers, preservice science teachers, and high school students, solved an open-ended earth-environmental problem and produced group texts in which their ways of solving the problem were written, The inferential processes in the texts were rearranged according to the syllogistic form of abduction and then analyzed iteratively so as to find thinking strategies used in the abductive reasoning. The result showed that abduction was employed in the process of solving the earth-environmental problem and that several thinking strategies were used for inferring rules from which abductive conclusions were drawn. The strategies found included data reconstruction, chained abduction, adapting novel information, model construction and manipulation, causal combination, elimination, case-based analogy, and existential strategy. It was suggested that abductive problems could be used to enhance students' thinking abilities and their understanding of the nature of earth science and earth-environmental problems.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.79-86
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• 2024

In this paper, a novel visual tracking method which can utilize the context of object regions is presented. Conventional methods have the inherent problem of treating all candidate regions independently, where the tracker could not successfully discriminate regions with similar appearances. This was due to lack of contextual modeling in a given scene, where all candidate object regions should be taken into consideration when choosing a single region. The goal of the proposed method is to encourage feature exchange between candidate regions to improve the discriminability between similar regions. It improves upon conventional methods that only consider a single region, and is implemented by employing the MLP-Mixer model for enhanced feature exchange between regions. By implementing channel-wise, inter-region interaction operation between candidate features, contextual information of regions can be embedded into the individual feature representations. To evaluate the performance of the proposed tracker, the large-scale LaSOT dataset is used, and the experimental results show a competitive AUC performance of 0.560 while running at a real-time speed of 65 fps.

• Smart Media Journal
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• v.13 no.2
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• pp.95-107
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• 2024

The development of environmentally friendly products and services has become a trend, and the development and utilization of eco-friendly textiles with economic value is gaining attention as a new business model. Analyzing and identifying trends and developments in eco-friendly textiles can provide important information and insights for various stakeholders such as companies, governments, and consumers to help them achieve sustainable growth. For this study, we collected and analyzed data from newspaper articles mainly covering the textile and fashion sector from 2000 to June 2023. A total of 12,331 articles containing the keyword 'eco-friendly textiles' were collected, and after performing morphological analysis on the extracted data, Latent Dirichlet Allocation and Dynamic Topic Modeling analysis were performed to identify topics by year. The results of the study are expected to provide strategic guidance and insights for the sustainable development of the textile industry, thereby helping to promote the research, development, and commercialization of eco-friendly textiles.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.139-148
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• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.105-112
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• 2024

In contemporary society, the expression of emotions and self-reflection are considered pivotal factors with a positive impact on stress management and mental well-being, thereby highlighting the significance of journaling. However, traditional journaling methods have posed challenges for many individuals due to constraints in terms of time and space. Recent rapid advancements in chatbot and emotion analysis technologies have garnered significant attention as essential tools to address these issues. This paper introduces an artificial intelligence chatbot that integrates the GPT-3 model and emotion analysis technology, detailing the development process of a system that automatically generates journals based on users' chat data. Through this system, users can engage in journaling more conveniently and efficiently, fostering a deeper understanding of their emotions and promoting positive emotional experiences.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.571-576
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• 2024

Ontologies are created by domain experts, but the same content may be expressed differently by each expert due to different understandings of domain knowledge. Since the ontology standardization is still lacking, multiple ontologies can be exist within the same domain, resulting in a phenomenon called the ontology heterogeneity. Therefore, we propose a novel ontology matching method that combines SCBOW(: Siames Continuois Bag Of Words) and BERT(: Bidirectional Encoder Representations from Transformers) models to solve the ontology heterogeneity issue. Ontologies are expressed as a graph and the SimRank algorithm is used to solve the one-to-many problem that can occur in ontology matching problems. Experimental results showed that our approach improves performance by about 8% over traditional matching algorithm. Proposed method can enhance and refine the alignment technology used in ontology matching.

• Convergence Security Journal
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• v.24 no.3
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• pp.33-39
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• 2024

The remarkable advancement of artificial intelligence technology is bringing innovative changes to the field of education. In particular, generative AI models like ChatGPT hold great potential in self-directed programming education due to their natural conversational abilities. This study analyzed the learning effects of using ChatGPT in Scratch classes for non-SW majors. Dividing the classes into those using ChatGPT and those not, and conducting the same evaluations and surveys for the ChatGPT-utilizing group, the results showed that ChatGPT significantly enhanced learning outcomes and the utility of ChatGPT was highly evaluated in advanced learning areas such as understanding Scratch's advanced features and algorithms. This study is significant as it empirically demonstrates the potential of generative AI like ChatGPT as an effective tool in programming education.

• Advanced Industrial SCIence
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• v.3 no.3
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• pp.1-7
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• 2024

The purpose of this study is to investigate how supply chain digital transformation affects corporate performance by building supply chain agility and innovation capabilities based on the resource-based view (RBV) theory. The model was verified using structural equation modeling based on a data set of 271 domestic companies, and mediation and moderation analyzes were performed to test the research hypotheses. The study found a positive correlation between supply chain digital transformation and corporate performance that is fully mediated by both supply chain agility and innovation capability, with the potential for the interaction between supply chain agility and innovation capability to have adverse consequences for corporate performance. This study is expected to advance our understanding on the antecedents of corporate performance by integrating supply chain digital transformation and the mediating mechanisms of supply chain agility and innovation capabilities that serve as a conduit between supply chain digital transformation and RBV-based corporate performance.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.70-80
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• 2024

In this study, we explore the purpose, origin, and history of thermal testing in the development of artificial satellite components. We seek to understand precisely the test variables associated with thermal vacuum and thermal cycle tests, including temperature margins and cycle counts, which may differ according to the development model. We analyze specifications detailed in standards from NASA, ESA, MIL, and others. Furthermore, given the recent surge in interest in CubeSats and nanosatellites, this paper aims to identify research trends in customizing satellite development projects according to their budget and duration.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.751-757
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• 2024

This study evaluates the effectiveness of advertising copywriting using the artificial intelligence language model, ChatGPT, and explores its potential applications and limitations within the advertising industry. We established five key research questions and conducted in-depth focus group interviews (FGI) with university students in Busan. The findings reveal that there was no significant preference difference between copies written by ChatGPT and human copywriters. However, ChatGPT's copies were particularly effective in age-targeted advertising but showed limitations in gender targeting and reflecting cultural contexts. Additionally, consumer acceptance of AI copywriting was generally positive, though concerns were raised about the creativity and naturalness of AI-generated copies. This research provides practical insights into how AI can be utilized in advertising content creation and stimulates discussion on the appropriate use of AI technology and ethical considerations within the industry. These results offer important implications for both advertising professionals and the academic community.