• International journal of advanced smart convergence
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• v.11 no.4
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• pp.260-265
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• 2022

This study aimed to determine whether lactate dehydrogenase (LDH) levels in coronavirus disease (COVID-19) patients in Korea were higher than those in patients without COVID-19, and the effect of sex and age on LDH levels. A retrospective, observational study was conducted to measure LDH levels in 247 and 225 female and male COVID-19 patients, respectively, who were admitted to the study hospital between April 1 and October 30, 2020. Serum LDH levels were measured using an automated analyzer. Results: LDH levels were elevated in both male and female patients with COVID-19. Among patients with COVID-19, LDH levels were higher in males than in females, and LDH levels were higher in patients with COVID-19 than in patients in the control group. In the analysis of differences in LDH levels by age, LDH levels in patients with COVID-19 increased statistically significantly with age in males and females (males: p=0.001, females: p=0.001). By examining the differences in LDH levels according to sex and age, this study contributed to the basic biochemical data available in Korea, particularly regarding patients with COVID-19. Further research may be needed to examine confounding variables.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.104-119
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• 2022

Life safety awareness level diagnosis is necessary for customized safety education and continuous safety awareness. As the starting stage of safety education for each life cycle, a scale that has verified the reliability and validity of high school students' life safety awareness has not yet been developed. In this context, the purpose of this study is to develop and validate the life safety awareness scale of high school students and to analyze interindividual differences. Questionnaire data was collected from April to June 2022 from 834 students in the first, second, and third grades of high schools in △△ city in Jeollabuk-do. A final 25-item scale was developed using the preliminary survey, preliminary test, the main test, descriptive statistical analysis, and exploratory and confirmatory factor analysis. This scale consists of four sub-factors: 'safety prevention', 'safety knowledge', 'safety preparation', and 'safety protection'. Good reliability and validity were verified by analysis of content validity and construct validity. The generalizability of the scale was verified by crossover validation between the search group and the crossover group. Based on the interindividual differences analysis, although there was a difference between genders in life safety awareness, there was no difference by grade level and academic achievement. This study is significant in developing the first valid scale that can measure high school students' life safety awareness and providing the necessity and rationale for life safety education by life cycle considering individual gender differences.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.185-192
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• 2022

We presented the research analysis results and policy recommendations for this study as follows. First, we findings it was analyzed that the cremation number in public cremation facilities increased at a high ratio of 7.5% per year on average. Therefore, policies to expand the supply of public cremation facilities in preparation for the continuous increase in cremation demand must be implemented as soon as possible. Second, in this study we users of public enshrinement facilities accounted for 21.0% of total cremation number, and it was analyzed that the ratio increased at an annual average of 9.0%. Therefore, as the supply reaches its limit within 1 year in Suwon City and within 2 years in Seongnam City, policies to expand the supply of public enshrinement facilities in Suwon City and Seongnam City must be implemented urgently. Third, it was analyzed through we research users of public natural burial sites accounted for a very low percentage of 1.6% of total cremation number. Therefore, policies such as creating a pleasant environment for public natural burial sites, improving facilities, and public relations activities to promote the use of public natural burial sites at the government-wide should be preceded.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.163-169
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• 2022

In this paper, we propose a data hiding technique that effectively hides confidential data in the LSB of an image pixel by using the characteristics of the neighboring pixels of the image and the encryption techniques. In the proposed technique, the boundary surface of the image and the flat surface with little change in pixel values are investigated. At the boundary surface of the image, 1 bit of confidential data is encrypted and hidden in the LSB of the boundary pixel to preserve the characteristics of the boundary surface. In the pixels of the plane where the change in pixel value is small, 2 bits secret data is encrypted and hidden in the lower 2 bits of the corresponding pixel. In this way, when confidential data is hidden in an image, the amount of confidential data hidden in the image is greatly increased while maintaining excellent image quality. In addition, the security of hidden confidential data is strongly maintained. When confidential data is hidden by applying the proposed technique, the amount of confidential data concealed increases by up to 92.2% compared to the existing LSB method. The proposed technique can be effectively used to hide copyright information in commercial images.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.120-128
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• 2022

While online social interactions have been more prevalent with the increased popularity of Metaverse platforms, little has been studied the effects of facial expressions in virtual reality (VR), which is known to play a key role in social contexts. To understand the importance of presenting facial expressions of a virtual avatar under different contexts, we conducted a user study with 24 participants where they were asked to have a conversation and play a charades game with an avatar with and without facial expressions. The results show that participants tend to gaze at the face region for the majority of the time when having a conversation or trying to guess emotion-related keywords when playing charades regardless of the presence of facial expressions. Yet, we confirmed that participants prefer to see facial expressions in virtual reality as well as in real-world scenarios as it helps them to better understand the contexts and to have more immersive and focused experiences.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.155-162
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• 2022

The purpose of this study is to conduct an empirical study on brand authenticity targeting sportswear brand consumers. Through this, we intend to provide the accumulation and implications of authenticity research. For the research model, first, the authenticity of sportswear brand companies was selected as an independent variable. Brand image and brand attitude were selected as the next parameters. Finally, the dependent variable was the intention to pay the premium. Structural equation model analysis was conducted for the structural relationship between these variables. The subjects of this study are consumers who have purchased sportswear brands within the past year. Convenience sampling was used for the sample survey, and 262 people were finally selected as valid samples. The survey was conducted as a non-face-to-face online survey due to the COVID-19 infection. For data processing, frequency analysis was conducted using SPSS 23 to identify the individual characteristics of the survey subjects. In addition, exploratory factor analysis and reliability analysis were performed to refine the scale of the survey tool. Next, using AMOS 21, confirmatory factor analysis and correlation analysis were conducted to verify the measurement model. In addition, structural equation model analysis was conducted to verify the hypothesis. As a result of the analysis, all six hypotheses selected from the research model were adopted.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.44-54
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• 2023

This paper provides a comprehensive analysis of the implications of the metaverse on the music industry, focusing on copyright issues and potential solutions. It delves into the concept and characteristics of metaverse platforms, describing them as environments that immerse users in a variety of virtual experiences. A significant portion of the paper is dedicated to exploring music use and copyright infringement in the metaverse. It examines how users incorporate existing music into their content, often leading to legal challenges due to copyright infringement. The paper discusses the role of online service providers (OSPs) in this context and the legal implications of their actions. The paper also addresses the 'safe harbor' provisions for OSPs and examines the balance between protecting rights holders and limiting OSP liability. It highlights the challenges and limitations of copyright enforcement in the metaverse, especially given the unique nature of content on platforms such as Roblox. Finally, the article proposes solutions to simplify music licensing in the metaverse, suggesting a shift from property rules to liability rules and the establishment of Collective Management Organizations (CMOs) to streamline the licensing process and better protect copyright holders' interests.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.