• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.317-342
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• 2024

The purpose of this study is to explore ways to improve accessibility to school library materials through analysis of elementary school students' information search behavior in DLS. Accordingly, the DLS search process was recorded for 26 students attempting a DLS search in the school library, and data was collected through a pre-search questionnaire on overall information needs and a post-search questionnaire on the search process and results. As a result of the analysis, satisfaction was found to be low when the main purpose of DLS use was simple leisure reading, when the search time and number of search words were long, and when there were too many search results. Accordingly, it was emphasized that curriculum subject-related metadata elements should be developed and a curriculum subject-specific thesaurus should be built and used to build lists and support user searches. In addition, it was suggested that the basic functions provided in external searches should be included, and a foundation should be laid in terms of resources and curriculum to systematically provide information utilization education to elementary school students who lack the ability to select search terms and judge the suitability of results after the search. It was proposed to provide an integrated search service with external resources and a personalized book recommendation service.

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.381-391
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• 2024

This study examined the factors influencing safety scores in air conditioning installation projects, with a specific focus on the characteristics of safety managers and project-specific details. Given the increase in industrial accidents within this sector and the lack of research on smaller-scale operations, this analysis of 7,046 safety data records from Company A aimed to address this gap. The results indicate that the month of project commencement has the most significant impact on safety scores(correlation coefficient of 0.21), followed by the age of the safety manager(correlation coefficient of 0.06). Interestingly, the educational background of the managers did not appear to influence safety outcomes. Furthermore, project cost was found to have a negative correlation with safety scores(-0.1), suggesting that lower-cost projects may be associated with higher risk levels. These findings underscore the importance of developing tailored safety checklists that take into account the specific timing and scale of air conditioning installation projects. Additionally, the results suggest that incorporating both experienced(older) and less experienced(younger) safety managers into safety strategies may be beneficial for achieving optimal safety outcomes. This balanced approach could leverage the strengths of both groups, potentially enhancing overall risk assessment and mitigation efforts.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• Journal of Space Technology and Applications
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• v.4 no.2
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• pp.153-168
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• 2024

Sounding rockets are cost-effective and rapidly deployable tools for directly exploring the ionosphere and microgravity environments. These rockets achieve their target altitudes quickly and are equipped with various scientific instruments to collect real-time data. Perigee Aerospace plans its inaugural test launch in the first half of 2024, followed by a second performance test launch in January 2025. The second launch, scheduled off the coast of Jeju Island, aims to reach an altitude of approximately 150 km with a payload of 30 kg, conducting various experiments in the suborbital region. Particularly in mid-latitude regions, the ionosphere sporadically exhibits increased electron densities in the sporadic E layers and magnetic fluctuations caused by the equatorial electrojet. To measure these phenomena, the sounding rocket version of ionospheric anomaly monitoring by magnetometer and plasma-probe (IAMMAP), currently under development at the KAIST Satellite Research Center, will be onboard. This study focuses on enhancing our understanding of the mid-latitude ionosphere and designing observable missions for the forthcoming performance tests.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.365-384
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• 2024

The development of scanning technology is accelerating for safer and more efficient automated inspection than human-based inspection. Research on automatically detecting facility damage from images collected using computer vision technology is also increasing. The pixel size, quality, and quantity of an image can affect the performance of deep learning or image processing for automatic damage detection. This study is a basic to acquire high-quality raw image data and camera performance of a mobile tunnel scanning system for automatic detection of damage based on deep learning, and proposes a method to quantitatively evaluate image quality. A test chart was attached to a panel device capable of simulating a moving speed of 40 km/h, and an indoor test was performed using the international standard ISO 12233 method. Existing image quality evaluation methods were applied to evaluate the quality of images obtained in indoor experiments. It was determined that the shutter speed of the camera is closely related to the motion blur that occurs in the image. Modulation transfer function (MTF), one of the image quality evaluation method, can objectively evaluate image quality and was judged to be consistent with visual observation.

• Korean Journal of Construction Engineering and Management
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• v.25 no.4
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• pp.45-52
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• 2024

Estimating the sales price of a residential building development project is difficult because of it has many complex variables such as location, environment, and economic conditions. Many previous studies related to influence factors of the sales price is to identify by survey of experts and it is few studies by comparing with actual sales price. Accordingly, the purpose of this study is to identify the factors influenced on the projects by using correlation analysis from collected actual data in this study. For the purpose, first, the factors such as economy, location, housing, financial environmental factors were identified from previous studies. Second, data were collected on actual sale prices and selected factors. Finally, the actual sales price and factors were compared and analyzed by using correlation analysis. As a result, the R² values of economy, location, housing and financial environmental factors were over 0.5 respectively. Therefore, it was confirmed that these factors were significantly correlated with actual sales price. The results of this study are expected to be utilized as basic data for research and development of a new sale prices prediction model.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.85-100
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• 2024

ESG management refers to corporate management that takes into account environmental, social, and governance factors, while digital transformation goes beyond the mere automation or digitization of existing tasks to drive an innovative change in the essence of work and the way value is created. Therefore, digital transformation can help companies achieve ESG goals and implement sustainable business practices, establishing a complementary relationship between digital transformation and ESG management for corporate sustainability and growth. This relationship maximizes the synergy of integrating digital transformation with ESG management, enabling companies to utilize resources efficiently and prevent redundant investments, ultimately enhancing sustainable management performance. In this study, we propose the simultaneous promotion of business process reengineering (BPR), in which both digital transformation and ESG management are integrated. This is because the collection, analysis, and decision-making processes related to various data for promoting ESG management must be organically integrated with digital transformation technologies. Therefore, we analyzed each ESG management objective presented in the K-ESG guidelines and identified the corresponding digital transformation technologies through expert interviews and a review of prior research. The K-ESG guidelines serve as a useful ESG diagnostic system that enables companies to identify improvement tasks and manage performance based on goals through self-assessment of ESG levels. By developing a model based on the K-ESG guidelines for the integrated promotion of digital transformation and ESG management, companies can simultaneously improve ESG performance and drive digital innovation, reducing redundant investments and trial-and-error while utilizing diverse resources efficiently. This study provides practical and academic implications by developing a concrete and actionable new research model for researchers and businesses.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.83-92
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• 2024

Recently, the importance of HR analytics for data-based decision-making in establishing and operating an effective human resource management system for companies is increasing. In addition, there is growing interest in the effect of employees' perceptions of organizational justice on positive organizational behavior. Therefore, in this study, among the various factors affecting teamwork behavior, we analyzed the impact on teamwork behavior such as perception of organizational justice and organizational culture. Organizational justice has a significant impact on the formation of members' attitudes, but its meaning may vary depending on the organizational context. In this study, we divided organizational justice into four types (procedural, distributive, interpersonal, and informational fairness) and confirmed their impact on teamwork behavior. In addition, organizational culture was divided into hierarchy culture and innovation culture, and how to regulate these relationships was examined. To analyze these relationships, individual-level data collected from 657 people at domestic companies were used for analysis. According to the analysis results, in a hierarchical culture, procedural justice and information justice had a positive influence on teamwork behavior through the mediating process of job satisfaction, and in an innovative culture, interpersonal justice and information justice had a positive influence on teamwork behavior through job satisfaction. It was confirmed to have a (+) effect. These research results provide implications for people management by indicating that, although organizational justice is important to members and organizations, it may be perceived differently and have different meanings depending on the organizational context. Through the use of the information presented in this study, we will provide value that can effectively and efficiently implement a company's human resource management system.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.109-119
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• 2024

This study aims to contribute to the initial research on on-device AI in an environment where generative AI-based services on mobile and other on-device platforms are increasing. To derive success strategies for generative AI-based chatbot services in a mobile environment, over 200,000 actual user experience review data collected from the Google Play Store were analyzed using the LDA topic modeling technique. Interpreting the derived topics based on the Information System Success Model (ISSM), the topics such as tutoring, limitation of response, and hallucination and outdated informaiton were linked to information quality; multimodal service, quality of response, and issues of device interoperability were linked to system quality; inter-device compatibility, utility of the service, quality of premium services, and challenges in account were linked to service quality; and finally, creative collaboration was linked to net benefits. Humanization of generative AI emerged as a new experience factor not explained by the existing model. By explaining specific positive and negative experience dimensions from the user's perspective based on theory, this study suggests directions for future related research and provides strategic insights for companies to improve and supplement their services for successful business operations.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.23-37
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• 2024

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.