• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.415-423
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• 2023

The purpose of this study was to prepare basic data for improving school library information service by investigating the information usage behavior of specialized high school students. Preferred information sources for each situation requiring information and the level of solving information problems using information sources were investigated, and difference analysis was conducted by department and grade. As a result of the survey, the percentage of students who preferred Internet portal services, personal information sources (teachers, friends, parents), and social media was high, while the percentage of students who preferred traditional print information sources and mass media was very low. The average score of the information problem solving level was 3.55, and the problem solving level in the areas of employment and career/admission was relatively low. Preferred sources of information were similar regardless of grade and department, and the difference between departments in information problem solving level was not statistically significant, but the difference between grades was statistically significant. In addition, there is an academic contribution in this field that specific examples of youth information use behavior have been added. Based on the results of the study, librarians should make efforts to verify the reliability of Internet portal site information, improve and promote library information sources, and expand library use education. In future studies, it was suggested to develop customized information services.

• Journal of the Korea Knowledge Information Technology Society
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• v.12 no.1
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• pp.69-78
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• 2017

KOIN (Korean medicine 人, http://www.koin.re.kr) is a Korean medicine knowledge portal developed for users who are interested in Korean medicine to share relevant information. The purpose of the present study is to seek methods for advancing the quality of the contents and services of KOIN to secure future users of the portal services before fully initiating the KOIN service. The crowd testing method was applied to test the functionality and usability of the current KOIN service, and domestic and international websites providing similar services were investigated and analyzed. About 150 errors were found in this functional testing procedure, but the identified functional problems were all corrected. An average score of 3.33 was calculated in the usability test, in which the reliability and the playfulness showed the highest and lowest score, respectively. We in this paper surveyed the 15 relevant websites with respect to KOIN in the traditional medicine and the modern medicine fields. The strengths and weaknesses of similar websites were analyzed to improve the KOIN services. In particular, it is shown that the evidence-based Korean medicine knowledge is KOIN's biggest strength. Users' needs and demand for the KOIN services will be continuously gathered to provide the Korean medicine knowledge services that the users require.

• Asia Marketing Journal
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• v.15 no.3
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• pp.79-95
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• 2013

New product innovation is a process of embodying new knowledge in a product and technology licensing is getting popular as a means to innovations and introduction of new product to the market in today's competitive global market environment. Incumbents often rely on technology licensing to access new product opportunities created by other firms. Prior research has examined various aspects of technology licensing agreements such as specific contract terms of licensing agreements, e.g., distribution of control rights, exclusivity of licensing agreements, cross-licensing, and the scope of licensing agreements. This study aims to provide answers to an important, but under-researched question: why do some incumbents initiate more licensing agreement for exploratory learning while others do it for exploitative learning along the innovation process? We attempt to extend our knowledge of licensing agreements from an organizational learning perspective. Technology licensing as a specific form of interfirm linkages can be initiated with different learning objectives along the process of new product innovation. The exploratory stages of the innovation process such as discovery or research stages involve extensive searches to create new knowledge or capabilities, whereas the exploitative stages of the innovation process such as application or test stages near the commercialization are more focused on developing specific applications or improving their efficiency or reliability. Thus, different stages of the innovation process generate different types of learning and the resulting technological resources. We examine when incumbents as licensees initiate more licensing agreements for exploratory learning objectives and when more for exploitative learning objectives, focusing on two factors that may influence a firm's formation of exploratory and exploitative licensing agreements: 1) its past radical and incremental innovation experience and 2) its internal investments in R&D and marketing. We develop and test our hypotheses regarding the relationship between a firm's radical and incremental new product experience, R&D investment intensity and marketing investment intensity, and the likelihood of engaging in exploratory and exploitive licensing agreements. Using data collected from various secondary sources (Recap database, Compustat database, and FDA website), we analyzed technology licensing agreements initiated in the biotechnology and pharmaceutical industries from 1988 to 2011. The results of this study show that incumbents initiate exploratory rather than exploitative licensing agreements when they have more radical innovation experience and when they invest in R&D activities more intensively; in contrast, they initiate exploitative rather than exploratory licensing agreements when they have more incremental innovation experience and when they invest in marketing activities more intensively. The findings of this study contribute to the licensing and interfirm cooperation studies. First, this study lays a foundation to understand the organizational learning aspect of technology licensing agreements. Second, this study sheds lights on how a firm's internal investments in R&D and marketing are linked to its tendency to initiate licensing agreements along the innovation process. Finally, the findings of this study provide important insight to managers regarding which technologies to gain via licensing agreements. This study suggests that firms need to consider their internal investments in R&D and marketing as well as their past innovation experiences when they initiate licensing agreements along the process of new product innovation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.919-926
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• 2023

Aim(s): This study aims to explore the relationship between multicultural awareness, multicultural acceptance and sensitivity of nursing students in the midst of rapid changes in multiculturalism, and to explore the direction for improving multicultural awareness as health care workers in the future. A survey was conducted among 135 nursing students from two universities in one region, and 108 students, excluding random responses and dropouts, were the final subjects for analysis. For data analysis, frequency analysis, correlation analysis, reliability analysis and mediation effects were tested using SPSS and process Macros. The results confirmed a statistically significant relationship between multicultural awareness and multicultural acceptance (r=.572, p<.001). The relationship between mutual cultural sensitivity, multicultural acceptance (r=.650, p<.001) and multicultural awareness (r=.456, p<.001) also showed a significant positive correlation. In addition, the effect of mutual cultural sensitivity was confirmed in the relationship between multicultural awareness and multicultural acceptance. As a result, in the relationship between multicultural awareness and multicultural acceptability, intercultural sensitivity ranged from 0.188 to 0.554, and the 95% confidence interval did not include 0; thus, indirect effect was statistically significant. Considering these results, it was confirmed that it is important to increase multicultural awareness and intercultural sensitivity in order to increase multicultural acceptance.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.1
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• pp.1-7
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• 2024

β-Ga₂O₃ is a material with a wide band gap of ~4.8 eV and a high breakdown-voltage of 8 MV/cm, and is attracting much attention in the field of power device applications. In addition, compared to representative WBG semiconductor materials such as SiC, GaN and Diamond, it has the advantage of enabling single crystal growth with high growth rate and low manufacturing cost [1-4]. In this study, we succeeded in growing a 10 mm thick β-Ga₂O₃ single crystal doped with 0.3 mol% SnO₂ through the EFG (Edge-defined Film-fed Growth) method using multi-slit structure. The growth direction and growth plane were set to [010]/(010), respectively, and the growth speed was about 12 mm/h. The grown β-Ga₂O₃ single crystal was cut into various crystal planes (010, 001, 100, ${\bar{2}}01$) and surface processed. The processed samples were compared for characteristics according to crystal plane through analysis such as XRD, UV/VIS/NIR/Spec., Mercury Probe, AFM and Etching. This research is expected to contribute to the development of power semiconductor technology in high-voltage and high-temperature applications, and selecting a substrate with better characteristics will play an important role in improving device performance and reliability.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.437-447
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• 2023

By actively adopting technologies from the Fourth Industrial Revolution, the port industry is trending toward new types of ports, such as automated and smart ports. However, behind the development of these ports, there is an increasing risk of cyber security incidents and threats within ports and container terminals, including information leakage through cargo handling equipment and ransomware attacks leading to disruptions in terminal operations. Despite the necessity of research to enhance cyber security within ports, there is a lack of such studies in the domestic context. This study focuses on Busan Port, a representative port in South Korea that actively incorporates technology from the Fourth Industrial Revolution, in order to discover variables for improving cyber security in container terminals. The research results categorized factors for enhancing cyber security in Busan Port's container terminals into network construction and policy support, standardization of education and personnel training, and legal and regulatory factors. Subsequently, multiple regression analysis was conducted based on these factors, leading to the identification of detailed factors for securing and enhancing safety, reliability, performance, and satisfaction in Busan Port's container terminals. The significance of this study lies in providing direction for enhancing cyber security in Busan Port's container terminals and addressing the increasing incidents of cyber security attacks within ports and container terminals.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.350-357
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• 2023

Marine accidents frequently occur due to the unreasonable operation of ships excluded from ship departure control during marine special weather warnings within smooth water areas. Coastal wave buoys installed in smooth water areas are major reference indicators for ship departure control and can be seen as being directly connected to the safety of ships navigating smooth water areas and the coast. In this study, the location appropriateness of currently operating coastal wave buoys and additional installation in the smooth water areas were assessed by analyzing coastal marine accidents over the past 30 years (1991-2020), the main wind direction and wind speed of each major trading port, and the GICOMS ship track data in 2018. The study results showed that an additional coastal wave buoy should be installed at each of the major trading ports(Inchon Port, Pohang Port, Ulsan Port, and Busan Port) and that the location of the coastal wave buoy needs to be moved in the case of Busan Port. Based on various data analysis in this study, the suggestion for an additional installation and movement of the coastal wave buoy presented in this study is expected to contribute to improving the reliability of ship departure control and resolving safety blind spots.