• Title/Summary/Keyword: Design model

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Effects of Caffeine lntake and Stress on Sleep Quality in University Students (대학생의 카페인 섭취와 스트레스가 수면의 질에 미치는 영향)

  • Kim, Sang Hyeon;Gwon, Su A;Kwon, Yu Jin;Kim, Se In;Kim, Ye Jin;Oh, Hye Ran;Ha, Su Young;Cha, Nam Hyun
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.5
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    • pp.161-169
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    • 2022
  • The purpose of this study performed to confirm the effect of caffeine intake and stress on sleep quality of college students. Research respondents and data collection were conducted on 269 college students through Google questionnaires from February 14 to March 13, 2022, and the research design is a descriptive survey study. Statistical analysis was performed using the SPSS 27.0 version as t-test and one way ANOVA. As a result of the study, it was found that most college students consume more caffeine than the average daily caffeine intake of Korean adults, although it is far below the recommended daily caffeine intake of Korean adults. The quality of sleep of college students is stress (r=.32, p=<).001) and caffeine intake (r=.204, p=.001). It was found that there was a positive correlation. Factors affecting sleep quality are body mass index (β=.1.19, p<.001) Stress (β=.3.37, p<.001), smoking (β=-.18, p=.001), caffeine intake (β=.15, p=.005) It was in order, and the explanatory power of the model was 24.8%.

Metaverse Augmented Reality Research Trends Using Topic Modeling Methodology (토픽 모델링 기법을 활용한 메타버스 증강현실 연구 동향 분석)

  • An, Jaeyoung;Shim, Soyun;Yun, Haejung
    • Knowledge Management Research
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    • v.23 no.2
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    • pp.123-142
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    • 2022
  • The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

A Study on A Deep Learning Algorithm to Predict Printed Spot Colors (딥러닝 알고리즘을 이용한 인쇄된 별색 잉크의 색상 예측 연구)

  • Jun, Su Hyeon;Park, Jae Sang;Tae, Hyun Chul
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.45 no.2
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    • pp.48-55
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    • 2022
  • The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

Damage Analysis of Manganese Crossings for Turnout System of Sleeper Floating Tracks on Urban Transit (도시철도 침목플로팅궤도 분기기 망간크로싱의 손상해석)

  • Choi, Jung-Youl;Yoon, Young-Sun;Ahn, Dae-Hee;Han, Jae-Min;Chung, Jee-Seung
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.5
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    • pp.515-524
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    • 2022
  • The turnout system of the sleeper floating tracks (STEDEF) on urban transit is a Anti-vibration track composed of a wooden sleeper embedded in a concrete bed and a sleeper resilience pad under the sleeper. Therefore, deterioration and changes in spring stiffness of the sleeper resilience pad could be cause changes in sleeper support conditions. The damage amount of manganese crossings that occurred during the current service period of about 21 years was investigated to be about 17% of the total amount of crossings, and it was analyzed that the damage amount increased after 15 years of use (accumulated passing tonnage of about 550 million tons). In this study, parameter analysis (wheel position, sleeper support condition, and dynamic wheel load) was performed using a three-dimensional numerical model that simulated real manganese crossing and wheel profile, to analyze the damage type and cause of manganese crossing that occurred in the actual field. As a result of this study, when the voided sleeper occurred in the sleeper around the nose, the stress generated in the crossing nose exceeded the yield strength according to the dynamic wheel load considering the design track impact factor. In addition, the analysis results were evaluated to be in good agreement with the location of damage that occurred in the actual field. Therefore, in order to minimize the damage of the manganese crossing, it is necessary to keep the sleeper support condition around the nose part constant. In addition, by considering the uniformity of the boundary conditions under the sleepers, it was analyzed that it would be advantageous to to replace the sleeper resilience pad together when replacing the damaged manganese crossing.

Prediction of Stacking Angles of Fiber-reinforced Composite Materials Using Deep Learning Based on Convolutional Neural Networks (합성곱 신경망 기반의 딥러닝을 이용한 섬유 강화 복합재료의 적층 각도 예측)

  • Hyunsoo Hong;Wonki Kim;Do Yoon Jeon;Kwanho Lee;Seong Su Kim
    • Composites Research
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    • v.36 no.1
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    • pp.48-52
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    • 2023
  • Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

A Study on the 3D Precise Modeling of Old Structures Using Merged Point Cloud from Drone Images and LiDAR Scanning Data (드론 화상 및 LiDAR 스캐닝의 정합처리 자료를 활용한 노후 구조물 3차원 정밀 모델링에 관한 연구)

  • Chan-hwi, Shin;Gyeong-jo, Min;Gyeong-Gyu, Kim;PuReun, Jeon;Hoon, Park;Sang-Ho, Cho
    • Explosives and Blasting
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    • v.40 no.4
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    • pp.15-26
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    • 2022
  • With the recent increase in old and dangerous buildings, the demand for technology in the field of structure demolition is rapidly increasing. In particular, in the case of structures with severe deformation of damage, there is a risk of deterioration in stability and disaster due to changes in the load distribution characteristics in the structure, so rapid structure demolition technology that can be efficiently dismantled in a short period of time is drawing attention. However, structural deformation such as unauthorized extension or illegal remodeling occurs frequently in many old structures, which is not reflected in structural information such as building drawings, and acts as an obstacle in the demolition design process. In this study, as an effective way to overcome the discrepancy between the structural information of old structures and the actual structure, access to actual structures through 3D modeling was considered. 3D point cloud data inside and outside the building were obtained through LiDAR and drone photography for buildings scheduled to be blasting demolition, and precision matching between the two spatial data groups was performed using an open-source based spatial information construction system. The 3D structure model was completed by importing point cloud data matched with 3D modeling software to create structural drawings for each layer and forming each member along the structure slab, pillar, beam, and ceiling boundary. In addition, the modeling technique proposed in this study was verified by comparing it with the actual measurement value for selected structure member.

Effects of Anxiety, Resilience, and Self-efficacy on the Professional Competence of Nurses during the COVID-19 Pandemic (COVID-19 팬데믹 동안 간호사의 불안, 회복탄력성, 자기효능감이 전문직 역량에 미치는 영향)

  • Pratibha, Bhandari;Jinsook, Kim;Su Kyoung, Chung
    • Journal of Industrial Convergence
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    • v.21 no.2
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    • pp.43-52
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    • 2023
  • This study was conducted to examine the relationship of the COVID-19-related anxiety, self-efficacy, resilience, and professional competence of nurses working in hospitals where COVID-19 patients are hospitalized. Additionally, this study attempted to identify factors that affect the professional competence of nurses. Using a cross-sectional descriptive study design, an online survey of 120 nurses working at hospitals where COVID-19 patients were hospitalized was conducted between February 9 and February 19, 2021. Pearson's correlations were used to assess correlations between the main variables, and stepwise multiple regression was used to identify factors influencing professional competence. Results of the study showed that the professional competence of nurses was positively correlated with self-efficacy (r=.58, p<.001) and resilience (r=.56, p<.001). The correlation between professional competence and COVID-related anxiety was not significant (r=-.03, p=.766). Factors affecting professional competence included self-efficacy (β=.36, p=.004) and resilience (β=28, p=.021). The model explained approximately 35% of the variance in nurse professional competence (F=33.65, p<.001). To fully demonstrate the professional competence of nurses during the COVID-19 pandemic, institution-based programs should be developed and applied to improve their self-efficacy and resilience. In order to prevent the burnout of nurses in the longer-than-expected COVID-19 pandemic, efforts and policies at the nursing organization level are needed to systematically manage and monitor self-efficacy and resilience of nurses.

Exploring Preservice Teachers' Science PCK and the Role of Argumentation Structure as a Pedagogical Reasoning Tool (교수적 추론 도구로서 논증구조를 활용한 과학과 예비교사들의 가족유사성 PCK 특성 탐색)

  • Youngsun Kwak
    • Journal of the Korean Society of Earth Science Education
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    • v.16 no.1
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    • pp.56-71
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    • 2023
  • The purpose of this study is to explore the role and effectiveness of argumentation structure and the developmental characteristics of science PCK with Earth science preservice teachers who used argumentation structure as a pedagogical reasoning tool. Since teachers demonstrate PCK in a series of pedagogical reasoning processes using argumentation structures, we explored the characteristics of future-oriented family resemblance-PCK shown by preservice science teachers using argumentation structures. At the end of the semester, we conducted in-depth interviews with 15 earth science preservice teachers who had experienced lesson design and teaching practice using the argumentation structure. Qualitative analysis including a semantic network analysis was conducted based on the in-depth interview to analyze the characteristics of preservice teachers' family resemblance-PCK. Results include that preservice teachers organized their classes systematically by applying the argumentation structure, and structured classes by differentiating argumentation elements from facts to conclusions. Regarding the characteristics of each component of the argumentation structure, preservice teachers had difficulty finding warrant, rebuttal, and qualifier. The area of PCK most affected by the argumentation structure is the science teaching practice, and preservice teachers emphasized the selection of a instructional model suitable for lesson content, the use of various teaching methods and inquiry activities to persuade lesson content, and developing of data literacy and digital competency. Discussed in the conclusion are the potential and usability of argument structure as a pedagogical reasoning tool, the possibility of developing science inquiry and reasoning competency of secondary school students who experience science classes using argumentation structure, and the need for developing a teacher education protocol using argumentation structure as a pedagogical reasoning tool.

Development and Implementation of a Travel Education Program in Home Economics to Improve Subjective Well-being of Middle School Students (중학생의 주관적 웰빙 향상을 위한 가정과 여행 교육 프로그램의 개발 및 실행)

  • Shin, Ji Hye;Park, Mi Jeong
    • Journal of Korean Home Economics Education Association
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    • v.35 no.2
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    • pp.91-110
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    • 2023
  • This study aimed to develop and implement a home economics education program with a travel theme for middle school students and investigate whether it improves their subjective well-being. The ADDIE design model was used to develop and implement the home economics education program titled 'Happiness Travel with You and I'. The program consisted of six sessions focusing on time management, dietary life, and housing life. The 'Happiness Travel with You and I' program was implemented for first-year male and female students at middle school B in Cheongju City. The results revealed that the program reduced negative emotions in adolescents and positively influenced their perception of the value of life. Students rated the program highly in terms of satisfaction with class content, class interest, and recognition of the importance of the class. This study is significant as it is the first attempt to test the effect of a travel-themed home economics education program and suggests the expansion and promotion of travel and leisure education in the field of home economics education.

Disentangling Trade Effects of the Korea - China FTA: Trade Liberalization or Political Conflicts?

  • HuiHui Yin;Juyoung Cheong
    • Journal of Korea Trade
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    • v.27 no.3
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    • pp.21-42
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    • 2023
  • Purpose - This paper investigates the trade effect of the Korea-China Free Trade Agreement (KCFTA) which coincides with political conflicts between the two countries due to the deployment of the Terminal High Altitude Area Defense (THAAD) in Korea. The two events occurred in the same year and both are likely to affect trade between two countries but in opposite directions. Therefore, it is crucial to distinguish between the trade effects from the KCFTA event and those from the THAAD event to evaluate the true FTA effects. However, this would be difficult when using only annual data. Accordingly, ex post studies to examine the trade effects of KCFTA are lacking in trustworthiness while many ex ante studies that conjecture the positive trade effects neglect the THAAD deployment impact. This paper aims to fill that gap. Design/methodology - Given that the KCFTA and THAAD events occurred in the same year but in different months, we use the monthly data from 2000 to 2019 of Korea's exports to bracket this period. We employ the difference-in-difference (DID) method within a gravity equation specification that uses hi-dimensional fixed effects to address various endogeneity issues and seasonal effects. We identify the net impact of KCFTA ratification from these two near-simultaneous events to quantify the effects of trade liberalization between these two countries. Findings - After isolating the THAAD effects on trade, the analysis creates a positive and statistically significant coefficient estimate of the KCFTA impact. In contrast, failing to isolate the THAAD effect produced a negative and statistically significant coefficient estimate of the KCFTA impact. Our results indicate that KCFTA independently increased Korea's exports to China by 10.2%, but that this increase was fully mitigated by the THAAD event. Further, our results verify that unobserved heterogeneity and multilateral resistance are technically difficult to account for in those estimations as that rely solely upon annual data, as this type of data are inadequate to control for the potential for endogeneity. Originality/value - This paper is one of the first studies to carefully evaluate the net trade effects of the KCFTA on Korea's largest trading partner while isolating the impact of simultaneously occurred political events that may influence trade in opposing directions. Our findings indicate that the lack of prior evidence of positive trade effects of the KCFTA when using annual data may be attributed to a failure to identify the impact of each event separately. This analysis supports using the correct modeling specification to avoid misleading conclusions when evaluating any important international trade policy.