• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of Korean Academy of Rural Health Nursing
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• v.18 no.2
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• pp.80-91
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• 2023

Purpose: The purpose of the study quantitatively investigates the experience of unmet healthcare service utilization by rural populations in vulnerable areas during the COVID-19 pandemic based on Andersen's behavior model. At the same time, this study attempts to describe the experiences of unmet healthcare service utilization among participants in vulnerable rural areas by analyzing qualitative contents through open-ended question. Methods: Data were collected from October to November 2022 using Qualtrix, a web-based survey platform. A total of 863 participants completed an online survey. Quantitative data were analyzed using 𝑥² test and logistic regression analysis. Qualitative data were analyzed using content analysis. Results: The factors affecting participants' unmet healthcare service utilization were type of residential area and underlying disease. The qualitative analysis identified; four categories and nine sub-categories. Conclusion: Based on these findings, it is necessary to develop a disaster nursing response model according to the type of residential areas and the number of people.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3913-3923
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• 2023

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance « automated identifiers » for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(²⁴¹Am, ⁵⁷Co, ¹³⁷Cs, ⁵⁴Mn, ⁶⁰Co), and then applied to five standard nuclear materials(0.32-4.51% at.²³⁵U), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for ²³⁸U and 4.7-10.5% for ²³⁵U in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (ST) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify ²³⁵U and ²³⁸U isotopes in LEU or natural U-Th samples if a medium energy resolution detector is was in the measurements.

• Korean Journal of Community Nutrition
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• v.28 no.6
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• pp.466-479
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• 2023

Objectives: A mobile health intervention program was provided for employees with overweight and obesity for 12 weeks, and a process evaluation was completed at the end of the program. We investigated participant engagement based on app usage data, and whether engagement was associated with the degree of satisfaction with the program. Methods: The program involved the use of a dietary coaching app and a wearable device for monitoring physical activity and body composition. A total of 235 employees participated in the program. App usage data were collected from a mobile platform, and a questionnaire survey on process evaluation and needs assessment was conducted during the post-test. Results: The engagement level of the participants decreased over time. Participants in their 40s, high school graduates or lower education, and manufacturing workers showed higher engagement than other age groups, college graduates, and office workers, respectively. The overall satisfaction score was 3.6 out of 5. When participants were categorized into three groups according to their engagement level, the upper group was more satisfied than the lower group. A total of 71.5% of participants answered that they wanted to rejoin or recommend the program, and 71.9% answered that the program was helpful in improving their dietary habits. The most helpful components in the program were diet records and a 1:1 chat with the dietary coach from the dietary coaching app. The barriers to improving dietary habits included company dinners, special occasions, lack of time, and eating out. The workplace dietary management programs were recognized as necessary with a need score of 3.9 out of 5. Conclusions: Participants were generally satisfied with the mobile health intervention program, particularly highly engaged participants. Feedback from a dietary coach was an important factor in increasing satisfaction.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.142-159
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• 2023

As the core technologies of the 4th Industrial Revolution are introduced into luxury hotels, they are taking off as cultural and experiential spaces that provide new products and services to hotel users and new experiences. Therefore, this study investigated the effect of hotel users' perception of the experience of using technological amenity services on their trust and satisfaction, focusing on luxury hotels as smart hotel to identify the essential factors of smart hotels that can lead to continuous competitive advantage and improvements in the future. In addition, the study aimed to find an effective hotel marketing strategy and plan to satisfaction the smart hotel by maximizing customer satisfaction. To verify the research hypothesis, a survey was conducted targeting hotel users with experience using technological amenities in smart hotels within the last two years. As a result of the study, it was confirmed that all hypotheses were adopted except for the relationship between personification, intention to use technical amenities, and perceived performance expectations and satisfaction with smart hotels. Based on these research results, this paper presents theoretical and practical implications. Smart hotels are rapidly changing by introducing various smart technologies. Therefore, it will be meaningful data for securing a sustainable competitive advantage and establishing differentiated hotel management and marketing strategies.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.173-190
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• 2023

In recent times, with the development of virtual convergence technologies, the market for the Metaverse, a digitally virtual space that combines virtuality and reality, is experiencing significant growth. These Metaverses are realizing new value in both reality and virtual spaces through the development of diverse services and content. However, existing research on the Metaverse mostly revolves around its conceptualization and categorization, with limited exploration of intentions to use the Metaverse. Consequently, this study examined the impact of Metaverse service characteristic factors on trust and intention to use within the Metaverse. The results of this study are as follows. First, among the service characteristic factors of the Metaverse, presence, interactivity, and playfulness were found to have a positive impact on Metaverse trust. On the other hand, informativeness was found not to have a significant influence on trust in the Metaverse. Second, Metaverse trust was found to have a positive impact on intention to use the Metaverse. Based on the research results above, this study aims to propose effective communication strategies for activating the Metaverse and developing services within the Metaverse platform.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.206-223
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• 2023

The introduction of Smart technologies such as Artificial Intelligence(AI) systems are have a powerful impact in a variety of industry fields. Some experts predict that smart technology will completely change people's daily life and work styles, causing technological innovation, productivity improvement, and discovery and emergence of new fields. On the one hand, this vision cannot ignore negative views and concerns. Despite many social debates about employment, such as job loss and rising unemployment, there have not been many studies based on employee experience that provide a fundamental solution to the conflict between AI and employment. Therefore, this study finds out the effects and related factors of AI concierge robots for hotel employees, focusing on the hotel industry, and how employees' perceptions of AI concierge robots affect user resistance and turnover intention. This study, conducted a questionnaire survey of 322 hotel employees who had experience working with AI concierge robots in China, and used SPSS and SmartPLS statistical analysis programs to draw conclusions. We found that hotel employees' perceptions of AI concierge robots were significantly related to user resistance and turnover intention, and this association was related to employee self-efficacy, perceived organizational support, quality of AI services and new tasks. In addition, it was found that the quality of AI concierge robots directly or indirectly had the greatest influence on user resistance and turnover intention. The findings of this study provide theoretical implications for academia and practical implications for industry practitioners.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.1
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• pp.36-42
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• 2024

Roll-to-roll processing holds an integral position within the manufacturing landscape, and its significance reverberates across numerous industries. This versatile technology platform encompasses a diverse array of process methods and accommodates a wide spectrum of material categories, making it a cornerstone of modern production. Within this expansive domain, two commonly employed coating techniques, namely the slot die and gravure coating methods, have earned their prominence for their precision and efficiency in delivering flawless coatings. Additionally, the realm of drying processes relies heavily on thermal drying, infrared (IR) drying, and ultraviolet (UV) drying methods to expedite the transformation of materials from their liquid or semi-liquid states to solid, ready-to-use products. The undeniable importance of roll-to-roll processing lies in its ability to streamline manufacturing processes, reduce costs, and enhance product quality. This article embarks on a comprehensive journey to fathom the depth of this importance by delving into the intricacies of these common roll-to-roll process methods. Through rigorous research and meticulous data collection, we aim to shed light on the pivotal role these techniques play in shaping various industries and advancing the world of manufacturing. By understanding their significance, we can harness the full potential of roll-to-roll processing and pave the way for innovation and excellence in production.