• Convergence Security Journal
• /
• v.22 no.1
• /
• pp.135-140
• /
• 2022

This study aims to analyze exercise cases and issues using smart devices and technologies, and to present the development direction of a smart exercise environment suitable for the wellness life of active seniors with high activity and economic power unlike the existing silver generation. In the fitness industry, the subscription economy that regularly receives or uses necessary exercise tools, services, and digital content is expanding, and business models based on hardware sales and content subscription continue to emerge. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. The linkage of the digital healthcare function, which provides real-time changes to exercise programs based on continuous monitoring and feed back through wearable devices before, after, and during exercise by receiving and selecting exercise programs suitable for individual health status, is the differentiating factor in the smart fitness model.

• Journal of The Korean Association For Science Education
• /
• v.40 no.5
• /
• pp.543-563
• /
• 2020

This study explores how the teaching practices of two teachers changed during scientific modeling classes. It also aims to understand these changes in terms of the teachers' modeling pedagogical content knowledge (PCK) development. The study participants were two elementary school teachers and their fifth-grade students. The teachers taught eight lessons of scientific modeling classes about the human body. The data analysis was conducted for lessons 1-2 and 7-8, which best showed the change in teaching practice. The two teachers' teaching practices were analyzed in terms of feedback frequency, feedback content, and the time allocated for each stage of model generation, evaluation, and modification. Teacher A led the evaluation and modification stages in a teacher-driven way throughout the classes. In terms of feedback, teacher A mainly used answer evaluation feedback in lesson 1-2; however, in lesson 7-8, the feedback content changed to thought-provoking feedback. Meanwhile, teacher B mostly led a teacher-driven model evaluation and modification in lesson 1-2; however, in lesson 7-8, she let her students lead the model evaluation and modification stages and helped them develop models through various feedbacks. The analysis shows that these teaching changes were related to the development of modeling PCK components. Furthermore, the two teachers' modeling PCK differed in teaching orientation, in understanding the modeling stages, and in recognizing the value of modeling, suggesting the importance of these in modeling teaching practice. This study can help improve the understanding of modeling classes by revealing the relationship between teaching practices and modeling PCK.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.309-328
• /
• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.

• Knowledge Management Research
• /
• v.23 no.1
• /
• pp.203-225
• /
• 2022

Competition in the OTT (Over the Top) service market is getting fiercer since global OTT services enter the domestic market and existing platforms are actively reorganized. As powerful competitors with ultra-luxurious content continue to enter the marke with diversity required by users, various efforts are required for OTT service platforms to prevent subscriber churn in order to generate continuous revenue. Thus, this study tried to examine the effect of OTT service characteristics on continuous use intention through an empirical analysis based on Expectation-Confirmation Model(ECM). A total of 386 responses were collected from individuals who have experience or are currently using OTT service and analyzed using AMOS 24. Results show that content curation, content richness, and audience activity had a significant effect on expectation confirmation. Also, expectation confirmation had a significant effect on perceived usefulness and user satisfaction while perceived usefulness had a significant effect on user satisfaction, significantly influencing continuous intention to use OTT. Finally, price fairness was found to strengthen all proposed relationships. The findings are expected to provide useful information for service and content development for subscriber retention, which has the most direct impact on revenue generation of OTT service providers.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.119-142
• /
• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• ALGAE
• /
• v.35 no.2
• /
• pp.189-200
• /
• 2020

Pyropia yezoensis has been used as functional food in East Asia, especially in Korea and Japan, for more than five hundred years. This study aims to evaluate the antioxidant effect of polyphenols and proteins-rich extracts from P. yezoensis (PPPs) against 2,2'-azobis (2-amidinopropane) dihydrochloride (AAPH)-induced oxidative cell damage. Among six Korean local strains obtained from Jinhae (JiH), Haenam (HN), Jangheung (JaH), Jindo (JD), Wando (WD), and Sinan (SA) areas, the extracts of P. yezoensis from SA and JD are relatively higher in polyphenols and proteins contents. SA showed the lowest IC₅₀ scavenging activities against 1,1-diphenyl-2-picryl-hydrazyl and alkyl radicals and displayed protective effects against reactive oxygen species (ROS) in AAPH-induced Vero cells. Especially, the PPPs extracts from SA and JD showed protective activities against AAPH-induced apoptosis, as observed by nuclear staining with Hoechst 33342. Furthermore, in vivo studies of the SA extract in zebrafish showed significantly reduced ROS generation, lipid peroxidation, and cell damage. This is the first study, to our knowledge, to evaluate the antioxidant bioactivity of PPP in the Korean Peninsula using a zebrafish model. Due to SA and JD both located in the west coast of Korea, we deduced that the chemical content of the different PPP extracts was mildly influenced by their geographic location, and this alga has potential of protective activity against AAPH-induced ROS both in vitro and in vivo.

• The KIPS Transactions:PartD
• /
• v.12D no.4 s.100
• /
• pp.569-582
• /
• 2005

As information and business communication via Internet are growing up, web-based software is wide spread and more important on the viewpoint of software qualify than stand-alone. Research on verification of web content links and web-based Program was tried, but has short on covering various types of web based software and making experiments to be applied in real testing practice. This paper suggests a modeling technique to be applied to dynamic and various types of web-based software. First, it identifies each elements consisting of web-based software and then construct a model of Object Control Flow Graph and Object Relationship Diagram. We can generate test cases covering all test paths of ORD or invoking key points test route. Suggested modeling method and test case selection technique are verified by applying five types of web-based software and compared with other web-based test techniques.

• Environmental Analysis Health and Toxicology
• /
• v.23 no.2
• /
• pp.129-138
• /
• 2008

The purpose of this study was to investigate the effects of frankincense oil in skin aging animal model. Skin aging was induced by both the irradiation of UVB and the application of squalene monohydroperoxide (Sq-OOH) to the back of experimental animals for 4 weeks. And at the same time experimental materials were applied topically. Six to seven weeks female SHR-1 hairless mice were divided into five groups including normal (N: saline), control (C: UVB+Sq-OOH+saline), vehicle control (VC: UVB+Sq-OOH+jojoba oil), positive control (PC: UVB+Sq-OOH+0.01% retinoic acid) and experimental (E: UVB+Sq-OOH+3% Frankincense oil) groups, five animals each group. Lipid lamella and lipid content in stratum corneum of the E group were almost intact with a regular arrangement which were similar to the N group. Collagen fibers in dermis of the E group were almost intact with a regular arrangement which were similar to the N group. Relatively much less number of mast cells and inflammatory cells were found in the E group compared to the C group. The activities of XO, SOD and CAT were no significant difference between the E and N groups. In conclusion, the application of frankincense oil to the skin aging animal model reduced both the generation of free radicals and the damage of skin tissues. Therefore, frankincense oil can be used practically for the prevention or improvement of skin aging in terms of health promotion and beauty for the people.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.75-77
• /
• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.

• Journal of The Korean Association For Science Education
• /
• v.36 no.1
• /
• pp.29-43
• /
• 2016

There have been much efforts to reconstruct the science curriculum focusing on Disciplinary Core Ideas(DCI) in many countries such as America and Europe, the most practical effort has been to design a curriculum with learning progressions(LPs). LPs describe stepwise how students can systematically move toward the understanding of more sophisticated ideas or scientific activities and explain in succession the process of understanding the ideas while the students learn. In this study, a LP for ecosystems has been developed, and the developed LP is then evaluated accordingly. The Ecosystem is one of the DCI of the life science in Next Generation Science Standards(NGSS). The development process of the LP was set at step 4(Development, Assessment, Analysis, and Amendment), and developed through an iterative process of sequences. As a result of analyzing the developed LP, an assessment based on the LP provides reliable information to identifying student ability. This study proposes the development process of the LP and its methodological aspects to use Core Achievement Standards, Ordered Multiple-Choice items and the Rasch model. In addition, using the empirically proven LP suggests a way of strengthening curriculum linked to educational content, teaching methods and assessment. Utilizing the proposed development process in this study will be to present the standard into the direction of becoming part of the curriculum. Currently, the state of domestic research for the LP is still lacking. This study determined the development process of the LP and the need to conduct future research on the LPs.