• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.153-171
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• 2024

Recently, domestic universities have become more competitive among universities due to the Fourth Industrial Revolution and the rapid decline of population. As a result, universities are making great efforts to improve university service quality to enhance university competitiveness as they change from supplier-centered thinking to consumer-centered thinking. Despite the increasing importance of university service quality, research on service quality is mainly focused on companies, and research on service quality in the university education environment is insufficient. Therefore, this study aims to examine the influence relationship between university service quality, trust, reputation, and behavior intention. The results of this study are as follows. First, among the service quality of university, tangibility, assurance, and empathy were found to have a positive (+) effect on the trust of the university, but reliability was found to have no significant effect on the trust of the university. Second, among the service quality of university, tangibility, reliability, and empathy were found to have a positive (+) effect on the reputation of the university, but assurance was found to have no significant effect on the reputation of the university. Third, it was found that the trust of the university had a positive (+) effect on the behavior intention. Fourth, it was found that the university's reputation had a positive (+) effect on the behavior intention. Through the above research results, this study aims to derive an effective management plan for university service quality and to present a plan for establishing a differentiated operating strategy for universities that can respond to students' learning needs and changes in the times in the rapidly changing university education environment.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.271-275
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• 2024

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care. Materials and Methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams - specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts, ChatGPT's answers were evaluated against official answer sheets. Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions. In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5's rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions(P=0.009). Both versions showed similar patterns in incorrect responses. Conclusion: Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4's perfect score in comprehension questions underscores its trainability in specific subjects. However, both versions exhibited weaker performance in mathematical analysis, suggesting this as an area for improvement.

• Journal of Science Education
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• v.48 no.2
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• pp.75-90
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• 2024

Contemporary society faces increasingly diverse risks with expanding impacts. In response, the importance of science education has become more prominent. This study aims to analyze the characteristics of existing research on science-related risk education and derives implications for such education. Using detailed bibliometric analysis, we collected citation data from 83 international scholarly journals (SSCI) in the field of education indexed in the Web of Science with the keywords 'Scientific Risk.' Subsequently, using the bibliometrix package in R-Studio, we conducted a bibliometric analysis. The findings are as follows. Firstly, research on risk education covers topics such as risk literacy, the structure of risks addressed in science education, and the application and effectiveness of incorporating risk cases into educational practices. Secondly, a significant portion of research on risks related to science education has been conducted within the framework of socioscientific issues (SSI) education. Thirdly, it was observed that research on risks related to science education primarily focuses on the transmission of scientific knowledge, with many studies examining formal education settings such as curricula and school learning environments. These findings imply several key points. Firstly, to effectively address risks in contemporary society, the scope of risk education should extend beyond topics such as nuclear energy and climate change to encompass broader issues like environmental pollution, AI, and various aspects of daily life. Secondly, there is a need to reexamine and further research topics explored in the context of SSI education within the framework of risk education. Thirdly, it is necessary to analyze not only risk perception but also risk assessment and risk management. Lastly, there is a need for research on implementing risk education practices in informal educational settings, such as science museums and media.

• Korean Journal of Cognitive Science
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• v.35 no.3
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• pp.187-203
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• 2024

Evaluation activities are beneficial for learning or training. However, they are not actively used due to concerns about the evaluation accuracy of non-experts. Although there are methods to improve accuracy, there is a limitation that additional procedures or processes are required in addition to evaluation. In this study, we aimed to improve evaluation accuracy of non-expert by using common evaluation items and assigning weights based on differences from expert scores. In Study 1, we conducted a simulation with 50 non-experts evaluating essays. Our findings indicate that when non-experts' evaluation methods are different from those of experts, our proposed method using a single common evaluation item improves assessment accuracy. In Study 2, we analyzed data from experimental situation in which non-expert evaluated each other's essays. Consistent with Study 1, our proposed method effectively improved assessment accuracy when non-experts' evaluation methods differed from those of experts. In the discussion section, we addressed the applicability of the method proposed in this study in real world settings.

• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.33-50
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• 2024

This study examined the trends in home economics education research across different curriculum periods and journals. A total of 374 papers related to the 2009, 2015, and 2022 revised curricula were selected and analyzed using network analysis from four journals. First, research trends were analyzed by curriculum period. Keyword frequency analysis revealed that articles on the 2009 and 2015 revised curricula primarily focused on analyzing the curriculum to develop textbooks or programs. In contrast, papers on the 2022 revised curriculum explored broader directions. Topic modeling results highlighted the prominent topics for each period: 'environmental education' in the 2009 revised curriculum, 'safety and health education' in the 2015 revised curriculum, and 'ecological transition' in the 2022 revised curriculum. Second, research trends were analyzed by journal. Keyword frequency analysis by journal exposed specific research tendencies. For example, in the Journal of Home Economics Research, 'perceptions of practical classes' and 'analysis of safety textbooks' were major topics for the 2009 revised curriculum, while 'teaching and learning plans for competence development' dominated research on the 2015 revised curriculum. This study demonstrates that the concepts and goals emphasized in curriculum revisions are reflected in the corresponding research.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.101-117
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• 2024

The aim of this study is to identify the competencies required for fashion-related majors that meet contemporary demands, align with the objectives of university education, and reflect the qualities desired in graduates. To achieve this goal, we conducted content analysis of relevant data and in-depth interviews with experts. First, the content analysis involved coding key information from the introductions, educational goals, desired qualities of graduates, and curricula published on the websites of both South Korea and international fashion-related universities. Additionally, we analyzed the National Competency Standards (NCS) and the Meta-goals of higher education programs set by the International Textile Apparel Association (ITAA), extracting six core competencies. Second, in-depth interviews were conducted with six experts, each with 23 to 31 years of experience in Korean and international apparel industry and academia. The interviews were recorded, transcribed, and keywords were extracted. To ensure the validity of the coding results, cross-checks were performed among the researchers. The analysis identified the following competencies: empathic communication, social responsibility, professional thinking, creative and integrative thinking, global perspective, and challenging leadership. Based on these findings, establishing competencies that meet contemporary demands and developing corresponding curricula are essential steps towards creating a feedback system. Future research should focus on developing and implementing curricula that foster a virtuous cycle, ultimately enhancing students' competency levels.

• Journal of the Korean Society of Earth Science Education
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• v.17 no.2
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• pp.181-193
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• 2024

Summative assessment provides information on how well students have achieved learning objectives, making the development of high-quality assessment items essential for accurate evaluation. This is one of the competencies that teachers must possess. This study aims to analyze summative assessment items created by pre-service elementary teachers, examining their intentions and the difficulties encountered in the item development process. The study involved 45 second-year students enrolled in an elementary teacher training university. They were grouped into teams of three and tasked with developing ten items, documenting the purpose of each item, the answer key, and the challenges faced during item creation. The collected summative assessment items were analyzed using a two-dimensional purpose classification table that includes Klopfer's taxonomy of educational objectives. The intentions behind the summative assessments and the difficulties faced during item development were inductively organized and analyzed through qualitative data analysis. The results revealed that pre-service elementary teachers adequately reflected scientific content elements but did not evenly cover assessment domains. The most challenging aspect for them was adjusting the difficulty level. Although they considered most factors that should be taken into account during item development, these considerations were not reflected in the actual items. These findings suggest that knowledge and experience are crucial in developing summative assessment items, and systematic lectures are necessary for pre-service elementary teachers.