• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.225-249
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• 2024

The purpose of this study is to develop an evaluation tool for map-based data visualization platforms and to conduct heuristic usability evaluations on existing platforms representing inter-regional information. We compared and analyzed the usability evaluation criteria of map-based platforms from the previous studies along with Nielsen's (1994) 10 usability evaluation principles. We proposed nine evaluation criteria, including (1) visibility, (2) representation of the real world, (3) consistency and standards, (4) user control and friendliness, (5) flexibility, (6) design, (7) compatibility, (8) error prevention and handling, and (9) help provision and documentation. Additionally, to confirm the effectiveness of the proposed criteria, four experts was invited to evaluate five domestic and international map-based data visualization platforms. As a result, the experts were able to rank the usability of the five platforms using the proposed map-based data visualization usability evaluation criteria, which included quantified scores and subjective opinions. The results of this study are expected to serve as foundational material for the future development and evaluation of map-based visualization platforms.

• Journal of Space Technology and Applications
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• v.4 no.1
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• pp.27-47
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• 2024

In the New space era, satellites are being developed to perform on-orbit service (OOS) missions. Various missions for orbital service include failure repair, refueling, towing, component replacement, and space construction, and in order to do so, a robot arm payload must be mounted. Unlike conventional satellite payloads, the robot arm payload is not move in a fixed state, but is a payload that must move continuously to perform the mission. It is also characterized by the need to perform the mission while being directly exposed to outer space, rather than existing inside the structure of the satellite. Due to the characteristics of these payloads, thermal design and interpretation that can be operated smoothly in an extreme space thermal environment is essential, but there are not many papers on thermal design and interpretation of the robot arm. This paper introduces and summarizes cases of thermal design and interpretation of robot arm payloads developed so far, and finally, it intends to suggest directions for thermal design and interpretation of robot arm payloads to be developed in the future.

• Journal of Space Technology and Applications
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• v.4 no.2
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• pp.121-136
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• 2024

Korea Pathfinder Lunar Orbiter (KPLO), launched in August 2022, is successfully carrying out its mission. Korea's lunar lander and rover programs are expected to proceed in the future. To successfully carry out the mission after the lunar lander has landed on the surface, the performance of the equipment to be mounted should be checked in a laboratory environment similar to the Moon. Scientists and engineers of several countries, including the United States and China, use lunar soil simulant which is developed to resemble lunar soil for simulating the surface of the lunar landing site. Several lunar probe landing sites are being discussed in Korea, and lunar soil simulants such as Korea Hanyang Lunar Simulant-1 (KOHLS-1), Korea Aerospace University Mechanical Lunar Simulants (KAUMLS), and Korea Lunar Simulant-1 (KLS-1), which are similar to the characteristics of lunar mare soil, have been developed. However, those simulants are not useful if the landing site is chosen as a highland area. In this study, we introduce the process of developing KIGAM-L1, a lunar highland soil simulant similar to the chemical composition of the Apollo 16 lunar soil sample and the particle size distribution of lunar soil sample 60500-1, in case the lunar lander lands at highland area.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.345-357
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• 2023

Nuclear forensics has been understood as a mendatory component in the international society for nuclear material control and non-proliferation verification. Radiochronometry of nuclear activities for nuclear forensics are decay series characteristics of nuclear materials and the Bateman equation to estimate when nuclear materials were purified and produced. Radiochronometry values have uncertainty of measurement due to the uncertainty factors in the estimation process. These uncertainties should be calculated using appropriate evaluation methods that are representative of the accuracy and reliability. The IAEA, US, and EU have been researched on radiochronometry and uncertainty of measurement, although the uncertainty calculation method using the Bateman equation is limited by the underestimation of the decay constant and the impossibility of estimating the age of more than one generation, so it is necessary to conduct uncertainty calculation research using computer simulation such as Monte Carlo method. This highlights the need for research using computational simulations, such as the Monte Carlo method, to overcome these limitations. In this study, we have analyzed mathematical models and the LHS (Latin Hypercube Sampling) methods to enhance the reliability of radiochronometry which is to develop an uncertainty algorithm for nuclear material radiochronometry using Bateman Equation. We analyzed the LHS method, which can obtain effective statistical results with a small number of samples, and applied it to algorithms that are Monte Carlo methods for uncertainty calculation by computer simulation. This was implemented through the MATLAB computational software. The uncertainty calculation model using mathematical models demonstrated characteristics based on the relationship between sensitivity coefficients and radiative equilibrium. Computational simulation random sampling showed characteristics dependent on random sampling methods, sampling iteration counts, and the probability distribution of uncertainty factors. For validation, we compared models from various international organizations, mathematical models, and the Monte Carlo method. The developed algorithm was found to perform calculations at an equivalent level of accuracy compared to overseas institutions and mathematical model-based methods. To enhance usability, future research and comparisons·validations need to incorporate more complex decay chains and non-homogeneous conditions. The results of this study can serve as foundational technology in the nuclear forensics field, providing tools for the identification of signature nuclides and aiding in the research, development, comparison, and validation of related technologies.

• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.17-27
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• 2024

This study focuses on the development and evaluation of a chatbot system based on the International Classification of Functioning, Disability, and Health (ICF) framework to enhance community participation among elderly individuals with mild dementia in South Korea. The study involved 12 elderly participants who were living alone and had been diagnosed with mild dementia, along with 15 caregivers who were actively involved in their daily care. The development process included a comprehensive needs assessment, system design, content creation, natural language processing using Transformer Attention Algorithm, and usability testing. The chatbot is designed to offer personalized activity recommendations, reminders, and information that support physical, social, and cognitive engagement. Usability testing revealed high levels of user satisfaction and perceived usefulness, with significant improvements in community activities and social interactions. Quantitative analysis showed a 92% increase in weekly community activities and an 84% increase in social interactions. Qualitative feedback highlighted the chatbot's user-friendly interface, relevance of suggested activities, and its role in reducing caregiver burden. The study demonstrates that an ICF-based chatbot system can effectively promote community participation and improve the quality of life for elderly individuals with mild dementia. Future research should focus on refining the system and evaluating its long-term impact.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.123-139
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• 2024

In the rapidly expanding live-commerce market, one of the newer channels in e-commerce, consumer satisfaction appears to have plateaued. This study, therefore, aims to explore the impact of consumers' internal psychological states on the formation of service attitudes and offers practical solutions based on these findings. Whereas previous studies predominantly focused on external factors, this research examines the role of internal psychological states and perceived value in shaping consumer attitudes. Specifically, this study distinguishes between personal loneliness and social isolation, adding an altruistic dimension by incorporating interpersonal orientation, thereby setting it apart from earlier research. The focus of this study is on the psychological conditions of live-commerce consumers, investigating how factors such as loneliness, social isolation, and interpersonal orientation influence service attitudes, moderated by perceived economic value. The analysis reveals that both social isolation and interpersonal orientation significantly affect service attitudes, with social isolation exerting a stronger influence. However, no statistically significant relationship was found between loneliness and service attitude. Additionally, the effects of social isolation and interpersonal orientation on service attitudes were moderated by perceived economic value, which amplified their influence. These findings underscore the importance of incorporating consumer-centric psychological factors when developing strategies. A tailored approach that aligns with the unique characteristics of the live-commerce channel can help businesses provide customized services. This strategic approach is expected to support the development of effective operational strategies and practical solutions not only for live-commerce but also for future two-way communication channels that businesses and small enterprises may leverage.

• Journal of Platform Technology
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• v.12 no.3
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• pp.41-54
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• 2024

Recently, support for various types of entrepreneurship has been increasing. At the same time, interest in entrepreneurship education is increasing to expand support for entrepreneurship and improve entrepreneurship performance. Therefore, the scope and interest of entrepreneurship education and support is expanding beyond college students and the general public to include high school students. Accordingly, this study seeks to investigate in more depth the relationship between entrepreneurship competency, entrepreneurship attitude, and intention to start a business in order to increase the intention to start a business. As a result of the analysis, entrepreneurship capabilities (creativity, risk-taking, and innovation) had a positive effect on attitude toward entrepreneurship, and attitude toward entrepreneurship had a positive effect on the will to start a business. In addition, the results of the analysis of the mediating effect of entrepreneurship attitude in the relationship between entrepreneurship capabilities (creativity, risk-taking, and innovation) and entrepreneurship will are as follows. First, in the relationship between creativity and entrepreneurial will, entrepreneurial attitude played a partial mediating role. Additionally, in the relationship between risk tolerance and entrepreneurial intention, entrepreneurial attitude played a partial mediating role. Lastly, in the relationship between innovation and entrepreneurial will, entrepreneurial attitude played a complete mediating role. The research conclusion summarizes the results of this study, suggests implications for entrepreneurs and start-ups, as well as limitations of this study and directions for future research..

• Industry Promotion Research
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• v.9 no.3
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• pp.295-309
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• 2024

With the implementation of the Northeast Revitalization Strategy, the tourism industry in Heilongjiang Province had an increasingly greater impact on regional economic development. Based on the tourism panel data of Heilongjiang Province from 2005 to 2021, this paper used DEA-BCC and Malmquist Index to analyze the static and dynamic changes of the tourism industry.The results of the study were as follows: (1) Static: The OE value reached strong DEA effectiveness in 2010, 2013, and 2019, indicated that tourism resources had been fully utilized. The SE value changed dramatically between 0.354 and 1, and the PTE value approached 1. OE was mainly affected by SE changes. (2) Dynamic: The total factor productivity (TFP) was overall greater than 1 and grew at an average annual rate of 13.8%. The variation in TFP was primarily influenced by the index of technological progress, indicated that the tourism industry in Heilongjiang Province made full use of technology for resource development, with a relatively high level of development efficiency. Therefore, the future focus of Heilongjiang Province's tourism industry will be on adjustments in industrial scale, technological innovation, and policy optimization.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.55-61
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• 2024

Recently, the number of aging concrete structures is steadily increasing. This is because many of these structures are reaching their expected lifespan. Such structures require accurate inspections and persistent maintenance. Otherwise, their original functions and performance may degrade, potentially leading to safety accidents. Therefore, research on objective inspection technologies using deep learning and computer vision is actively being conducted. High-resolution images can accurately observe not only micro cracks but also spalling and exposed rebar, and deep learning enables automated detection. High detection performance in deep learning is only guaranteed with diverse and numerous training datasets. However, surface damage to concrete is not commonly captured in images, resulting in a lack of training data. To overcome this limitation, this study proposed a method for generating concrete surface damage images, including cracks, spalling, and exposed rebar, using stable diffusion. This method synthesizes new damage images by paired text and image data. For this purpose, a training dataset of 678 images was secured, and fine-tuning was performed through low-rank adaptation. The quality of the generated images was compared according to three base models of stable diffusion. As a result, a method to synthesize the most diverse and high-quality concrete damage images was developed. This research is expected to address the issue of data scarcity and contribute to improving the accuracy of deep learning-based damage detection algorithms in the future.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.317-325
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• 2024

This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.