• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• Industry Promotion Research
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• v.9 no.1
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• pp.223-230
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• 2024

This study aims to determine whether coach leadership behavior and group cohesion mediate in the relationship between grit and training satisfaction among Chinese college students. Data were collected through a survey targeting 313 college students purposively sampled at a university in Guangdong, China. The collected data was analyzed using SPSS PC+ Win ver. 25.0 and SPSS PROCESS macro ver. 4.2. The statistical methods applied were frequency analysis, reliability analysis, correlation analysis, and dual mediation effect analysis. The conclusion of the study is as follows. First, grit, coach leadership behavior, group cohesion, and training satisfaction all showed significant positive correlations. Second, the coach's leadership behavior and group cohesion double-mediated in the relationship between grit and training satisfaction. Based on these results, this study proposed a plan to utilize not only grit but also coach leadership behavior and group cohesion to improve college students' training satisfaction.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.261-269
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• 2024

In this paper, the acoustic performance of an absorptive silencer was enhanced by optimizing an arrangement of multi-layered absorbing materials. The acoustic performance of the silencer was evaluated through transmission loss, and finite element method-based numerical analysis program was employed to calculate the transmission loss. Polyurethane, a porous elastic material frequently used in absorptive silencers, was employed as the absorbing material. The Biot-Allard model was applied, assuming that air is filled inside the polyurethane. By setting the frequency range of interest up to the 2 kHz and the acoustic performance affecting properties of the absorbing materials were investigated when it was composed as a single layer. And the acoustic performance of the silencers with the single and multi-layered absorbing materials was compared with each other based on polyurethane material properties. Subsequently, the arrangement of the absorbing materials was optimized by applying the Nelder-Mead method. The results demonstrated that the average transmission loss improved compared to the single-layered absorptive silencer.

• Journal of Information Technology Services
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• v.23 no.3
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• pp.103-118
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• 2024

Due to the advancement of virtual reality technology, virtual idols are widely used in industrial and cultural content industries. However, it is difficult to utilize virtual idols' social perceptions because they are not properly understood. Therefore, this paper collected and analyzed YouTube comments to identify differences about social perception through comparative analysis between virtual idols and general idols. The dataset was constructed by crawling comments from music videos with more than 10 million views of virtual idols and more than 10,000 comments. Keyword frequency and TF-IDF values were derived from the collected dataset, and the connection centrality CONCOR cluster was analyzed with a semantic network using the UCINET program. As a result of the analysis, it was found that virtual idols frequently used keywords such as "person," "quality," "character," "reality," "animation," while reactions and perceptions were derived from general idols. Based on the results of this analysis, it was found that while general idols are mainly evaluated with their appearance and cultural factors, social perceptions of virtual idols' values are mixed with evaluations of cultural factors such as "song," "voice," and "choreography," focusing on technical factors such as "people," "quality," "character," and "animation." However, keywords such as "song," "voice," "choreography," and "music" are included in the top 30 like regular idols and appear in the same cluster, suggesting that virtual idols are gradually shifting away from minority tastes to mainstream culture. This study aims to provide academic and practical implications for the future expansion of the industry and cultural content industry of virtual idols by grasping the social perception of virtual idols.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.4
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• pp.515-534
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• 2002

Purpose : This study was to provide a meaningful framework for establishment of the direction of Nursing Management area to get ready for Nursing Personal Licensing Examination Board. Method : Using methodological and descriptive research method, the research process was as follows: a preliminary work list which was performed by New Nurse related to nursing management area was made of such key information as the domain of nursing management work, the element of that domain, and the detail work content, by means of a broad review of literature related to nursing management on Licensing Examination for the new nurse. After this preliminary work list sought to obtain examination, consensus, and modification of expert group in the nursing management field, research instrument which was the work list performed by new nurse within nursing management area was to developed. And next, it was to survey various of the importance and the frequency of that work performance by registered nurse, nurse manager, and the professors to take responsible of nursing management. Finally, the educational content for nursing management at the level of baccalaureate was proposed by various analyzing of research results. Results : The work list which was performed by new nurse related to nursing management area consisted of 9 domains, 24 elements, and 80 detail work contents. After it was examined into such various analysis as descriptive statistics, ANOVA, mean range, and so on, of the importance perception of the work and the frequency of work performance, the educational contents for baccalaureate in nursing management which consisted of total 65 contents made up of 18 core contents and 47 essential contents, which new nurses have to necessarily know and perform within the nursing management area. Conclusion : The results of this study will contribute to identifying the work list of new nurses performed in nursing management area and also proposing the educational contents that was taught at the level of baccalaureate in nursing management area based on doing actually in nursing unit.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.6 s.46
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• pp.13-19
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• 2005

Floor response spectra for dynamic response of subsystem such as equipment, or piping in nuclear power plants are usually generated without considering dynamic interaction between main structure and subsystem. This study describes the analytic method in which equipment response spectra can be obtained through dynamic analysis considering equipment-structure Interaction(ESI). In this method, dynamic response of the equipment by this method is based on a dynamic substructure method in which the equipment-structure system is partitioned into the single-degree-ol-freedom system(SDOF) representing the equipment and the equipment support impedance representing the dynamic charactenstics of the structure ai the equipment support. A family of equipment response spectra is developed by applying this method to calculate the maximum responses of a family of SDOF equipment systems with wide banded equipment frequency, damping ratio, and mass. The method is validated by comparing the floor response spectrum from this method with the floor response spectrum generated from the rigorous analysis including equipments on the containment building of a prototypical nuclear power plant. in order to Investigate ESI effect in the response of equipment, response values from the method and the conventional approach without considering ESI are compared for the equipment having the mass less than 1% of the total structural mass. Response spectra from the method showed lower spectral amplitudes than those of the conventional floor response spectra around controlling frequencies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.425-431
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• 2020

Recent advancements in industries and technologies have resulted in an increase in the volume of transportation, management, and distribution of logistics. Radio-frequency identification (RFID) technologies have been developed to efficiently manage such a large amount of logistics information. The use of RFID for management is being applied not only to the logistics industry, but also to the power transmission and energy management field. However, due to the limitation of program development capacity, the RFID device is limited in development, and this limitation is vulnerable to security because the existing strong encryption method cannot be used. For this reason, we designed a chaotic system for security with simple operations that are easy to apply to such a restricted environment of RFID. The designed system is a two-dimensional tent map chaotic system. In order to solve the problem of a biased distribution of signals according to the parameters of the chaotic dynamical system, the system has a cryptographic parameter(𝜇₁), a distribution parameter(𝜇₂), and a parameter(𝜃), which is the constant point, ID value, that can be used as a key value. The designed RFID authentication system is similar to random numbers, and it has the characteristics of chaotic signals that can be reproduced with initial values. It can also solve the problem of a biased distribution of parameters, so it is deemed to be more effective than the existing encryption method using the chaotic system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• Journal of Science Education
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• v.46 no.1
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• pp.17-29
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• 2022

The purpose of this study is to look qualitatively into how efficiently and reasonably a computer can learn themes related to the Nature of Science (NOS). In this regard, a corpus has been constructed focusing on literature (920 abstracts) related to NOS, and factors of the optimized Word2Vec (CBOW, Skip-gram) were confirmed. According to the four dimensions (Inquiry, Thinking, Knowledge and STS) of NOS, the comparative evaluation on the word-level word embedding was conducted. As a result of the study, according to the previous studies and the pre-evaluation on performance, the CBOW model was determined to be 200 for the dimension, five for the number of threads, ten for the minimum frequency, 100 for the number of repetition and one for the context range. And the Skip-gram model was determined to be 200 for the number of dimension, five for the number of threads, ten for the minimum frequency, 200 for the number of repetition and three for the context range. The Skip-gram had better performance in the dimension of Inquiry in terms of types of words with high similarity by model, which was checked by applying it to the four dimensions of NOS. In the dimensions of Thinking and Knowledge, there was no difference in the embedding performance of both models, but in case of words with high similarity for each model, they are sharing the name of a reciprocal domain so it seems that it is required to apply other models additionally in order to learn properly. It was evaluated that the dimension of STS also had the embedding performance that was not sufficient to look into comprehensive STS elements, while listing words related to solution of problems excessively. It is expected that overall implications on models available for science education and utilization of artificial intelligence could be given by making a computer learn themes related to NOS through this study.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.