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This paper analyzes the trends of identification proofing services(PIPSs) to identify and authenticate users online and proposes a method to improve PIPS based on alternative means of resident registration numbers in Korea. Digital identity proofing services play an important role in modern society, but there are some problems. Since they handle sensitive personal information, there is a risk of information leakage, hacking, or inappropriate access. Additionally, online service providers may incur additional costs by applying different PIPSs, which results in online service users bearing the costs. In particular, in these days of globalization, different PIPSs are being used in various countries, which can cause difficulties in international activities due to lack of global consistency. Overseas online PIPSs include expansion of biometric authentication, increase in mobile identity proofing, and distributed identity proofing using blockchain. This paper analyzes the trend of PIPSs that prove themselves when identifying users of online services in non-face-to-face overseas situations, and proposes improvements by comparing them with alternative means of Korean resident registration numbers. Through the proposed method, it will be possible to strengthen the safety of Korea's PIPS and expand the provision of more reliable identification services.

In the aftermath of the COVID-19 pandemic, the importance of collective efforts in promoting health preventive behaviors is accentuated, bringing sociopolitical factors into focus. To fully capture psychological drivers of health preventive behaviors in risk situations, anchored on the Model of Risk Information Seeking and Processing (RISP; Griffin, Dunwoody, and Neuwirth 1999), in retrospect of the recent COVID-19 pandemic, we explored whether and how individuals’ vaccination behaviors are predicted by RISP-related variables (information insufficiency, affective responses, perceived information gathering capacity, subjective norms) and one’s political identity. Findings from a survey of 705 adult participants in the U.S. showed that the effects of one’s risk information insufficiency on his or her information seeking and affective response regarding the pandemic, which is also related to their risk susceptibility perceptions. More importantly, the impact of political identity on one’s perceived risk susceptibility, and its association with vaccination behaviors are also identified. The findings of this study provide valuable insights for the development of effective health communication strategies for preventive health behaviors.

The aim of this study is to investigate how travelers’ destination visit intentions are influenced by avatar self-congruence in the metaverse platform. We extended the impression management theory by considering both perceived enjoyment and social engagement, and illustrated the interaction effect based on users’ digital age. To achieve this, we conducted an online survey with 302 users. The survey results revealed that avatar self-congruence significantly influenced users in terms of perceived enjoyment and social engagement, thereby affecting their destination visit intentions. This study also revealed a significant interaction effect between digital age type and avatar self-congruence on users’ perceived enjoyment. The results of this study are expected to provide not only a theoretical reference for metaverse research and travel research but also managerial implications for destination management and metaverse applications.

News credibility is on a decline for many democratic countries. Among the countries, South Korea is currently witnessing one of the steepest declining curves. Since people obtain news from various media, for example, television and portals, news credibility can be measured for each of the media separately. Most often, television news credibility is much higher than portal news credibility because people tend to trust traditional media more than online ones. To understand the discrepancy between news credibility of the two media in specific relation to South Koreans’ everyday news use and overall news credibility evaluation, this exploratory study examined how the factors that influence television news credibility and portal news credibility differ from each other by examining the relations of news credibility of the two media to credibility of news I use and of news in general. Drawing from previous research on partisan news use, it assumed that normative beliefs for television as a traditional medium work for television news credibility in the similar way as the mechanisms of selective exposure and bias perception do. It also assumed that the experiences dimension of news trust works for credibility of portal news and of news in general similarly. To verify these assumptions, a regression analysis was conducted from a sample of 58,936 South Koreans collected in 2022. As assumed, results revealed a greater relation between credibility of television news and of news I use, and between credibility of portal news and of news in general respectively. The findings suggest that measurement of credibility should be revised in the way that reflects media characteristics and the differing expectations held by news users.

For Wi-Fi and IoT wireless systems, high-capacity wireless communication technology is being applied using MIMO OFDM. Because of virtu0al subcarriers in the practical MIMO OFDM system, the pilot subcarriers cannot be spaced equally. Thus, it is difficult to obtain a good mean square error (MSE) performance of the channel estimate. This paper proposes applicable methods and the newly discovered locations of pilot subcarriers in four transmitted antennas resulting in a good MSE performance with proposed optimization algorithms.

This study aims to examine factors influencing the intention to continue using the metaverse, focusing on the impact of social norms. Specifically, direct and indirect effects of technical characteristics (perceive quality, interactivity, perceived playfulness), user characteristics (self-efficacy, personal innovativeness), and social influence factors (descriptive norm, injunctive norm, subjective norm) on continuous use intention were examined. The role of perceived ease of use and perceived usefulness as a mediator was also examined. An online survey was conducted with 165 college students attending universities in two large cities in South Korea who had used the metaverse. As a result, perceived playfulness, descriptive norm, and perceived usefulness directly influenced continuous use intention. Perceived playfulness, interactivity, and subjective norm influenced continuous use intention indirectly through perceived usefulness. This study theoretically extends the Technology Acceptance Model by specifying social influence in metaverse contexts. Practical implications are provided in terms of marketing strategy for the metaverse industry to continue to grow and develop.

As smartphone use is increasing within the middle-aged population, society should pay closer attention to the mental health problems associated with smartphone addiction. This study examines the possibility that depression, anxiety, and ADHD can be interpreted not only as negative aspects, but also as positive aspects, in an addiction-related individual. We used habitual and addictive smartphone behavior as the dependent variables; anxiety, ADHD, depression, and habitual smartphone use as the independent variables; and self-control as a moderating variable. Depression and ADHD in smartphone users were found to be associated with higher levels of addictive smartphone use. Anxiety was having negative effect on addictive smartphone use. However, habitual smartphone use didn’t significantly affect addictive smartphone use. Further analysis indicated that depression, anxiety, and ADHD have mediating effects on habitual smartphone use. This study confirmed that psychological factors in adults, as well as habitual/addictive smartphone use and self-control, significantly influence smartphone overdependence.

In this paper, we propose an image compression scheme which uses a demosaicking network and a channel-wise decoder in the decoding network. For the demosaicing network, we use as the input a colored mosaiced pattern rather than the well-known Bayer pattern. The use of a colored mosaiced pattern results in the mosaiced image containing a greater amount of information pertaining to the original image. Therefore, it contributes to result in a better color reconstruction. The channel-wise decoder is composed of multiple decoders where each decoder is responsible for each channel in the color image, i.e., the R, G, and B channels. The encoder and decoder are both implemented by wavelet based auto-encoders for better performance. Experimental results verify that the separated channel-wise decoders and the colored mosaic pattern produce a better reconstructed color image than a single decoder. When combining the colored CFA with the multi-decoder, the PSNR metric exhibits an increase of over 2dB for three-times compression and approximately 0.6dB for twelve-times compression compared to the Bayer CFA with a single decoder. Therefore, the compression rate is also increased with the proposed method than with the method using a single decoder on the Bayer patterned mosaic image.

Recently, the sixth generation (6G) networks have become tremendous research topics. Intelligent reflecting surface (IRS) technologies have been envisioned, to increase spectrum and energy efficiency for the fifth generation (5G) mobile networks, towards the sixth generation (6G) communications. In this paper, especially for the strongest channel gain user, we investigate the bit-error rate (BER) of non-orthogonal multiple access (NOMA) systems with intelligent reflecting surface (IRS). First, we derive a BER expression in a closed-form of Q functions. Second, we investigate the BER performance improvement of IRS NOMA systems over NOMA systems versus the power allocation. Moreover, we analyze the BER performance improvement of IRS NOMA systems over NOMA systems versus the number of IRS devices. In results, NOMA equipped with IRS technologies could play an important role in the paradigm shift from 5G mobile networks to 6G mobile networks.

This paper deals with studying strategy about impact of deep learning (DL) on the factor of Korean economic growth. To study classification of impact factors of Korean economic growth, we suggest dynamic equation of microeconomy and study methods on economic growth impact of deep learning. Next step is to suggest DL model to dynamic equation with Korean economy data with growth related factors to classify what factor is import and dominant factors to build policy and education. DL gives an influence in many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently, young generations will take a big impact on their job selection because generative AI can do well as much as humans can do it everywhere. Therefore, policy and education methods should be rearranged as new paradigm. However, government and officers do not understand well how it is serious in policy and education. This paper provides method of policy and education for AI education including generative AI through analysing many papers and reports, and experience.

Bundling has become a general promotion strategy in mobile shopping context. Previous research on bundling has mostly focused on bundle frame or price frame. Even though consumers are searching and purchasing the product in mobile channels, little research has investigated the effects of bundling in mobile shopping. The objectives of this study is to examine how different product image of bundle affects consumer’s perception and purchase intention differently, and how brand familiarity moderate this influence. A sample of 140 subjects participated in a within-subjects experiment. A bundling composed of two different product image (a single product vs. bundled products) was evaluated by individuals. Brand familiarity (familiar brand vs. unfamiliar brand) were manipulated to test a set of hypotheses. We found that the effectiveness of bundling does not depend on the type of product image and brand familiarity. The findings of our study provide some implications for researchers and marketers. Although there have been studies on the effects of bundle framing on consumer behavior, we suggest new insights regarding bundling based on product image and mobile shopping context.

In this paper, the results of experiments conducted in indoor wired and outdoor wireless environments to reduce the interference impact of 5G mobile communication on fixed satellite services using RF Interference Reduction Module (IRM) are presented. Test results for interference signals with a 100MHz bandwidth in a wired environment demonstrate that RF IRM can reduce interference by more than 20dB in a 100MHz bandwidth. The performance of RF IRM was also tested in the S-band wireless environments. In the S-band wireless environments with interference signals having a 20MHz bandwidth, RF IRM exhibits interference reduction performance of 12-15dB, for signals with a 50MHz bandwidth, it shows 10-12dB interference reduction, and for signals with a 100MHz bandwidth, it demonstrates 5-10dB interference reduction. This paper shows that RF IRM can be used not only within transceivers but also in wireless environments to mitigate interference.

In this paper, we have undertaken a comprehensive investigation into the current state of anti-drone technology due to the increasing concerns and risks associated with the widespread use of drones. We carefully analyze anti-drone technology, dividing it into three crucial domains: detection, identification, and neutralization methods. This categorization enables us to delve into intricate technical details, highlighting the diverse techniques used to counter evolving drone threats. Additionally, we explore the legal and regulatory aspects of implementing anti-drone technology. Our research also envisions potential directions for advancing and evolving anti-drone tech to ensure its effectiveness in an ever-changing threat environment.

Satellite based communication is networks in which users in a wide area can access without wired-based ground infrastructure. In particular, the need is emerging due to the recent Ukraine-Russia war. Satellite network systems acquire data that is difficult to observe on Earth as well as communication networks and are also used for research and development, which allows additional data to be produced. However, due to the nature of communication networks existing in outer space, certain vulnerabilities are revealed, and attacks based on them can be exposed. In this paper, we analyze vulnerabilities that may arise due to the nature of satellite communication networks and describes current research, countermeasures, and future research directions.

As robotic technology expands into various fields, robots need to execute some complicated tasks in diverse environments. However, the previous robotic software solutions were limited to independent systems. We can not adapt to diverse functionalities and environments. This makes it hard to provide rapid and effective services and leads to costs and losses in the development process. To overcome these problems, we propose a robot software framework with behavior trees based on ROS2. This framework simplifies complex robot behaviors through behavior trees and makes it easy to modify, extend, and reuse robot behaviors. Furthermore, ROS2 standardizes connections between software modules, enhances the robot's flexibility, and enables independent development and testing of software. Our framework aims to provide a foundation for high-quality robot service provision by supporting the modularity, reusability, independent development, and testing required by intelligent robots that need to provide services in various environments.

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver’s state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

The Republic of Korea (ROK) Army is developing the Army Synthetic Battlefield Training System and plans divisional-level Live, Virtual, and Constructive (LVC) integrated training. This study proposes a plan to apply VR/AR/MR (Virtual Reality/Augmented Reality/Mixed Reality) technology to LVC integrated training systems to enhance the efficiency and effectiveness of future LVC integrated training. The study investigated immersive military training systems in the ROK and advanced countries. As a result, we confirm that immersive technology can significantly improve the efficiency and effectiveness of military training. Accordingly, we review the key technologies required for building a defense training system with immersive features and propose training subjects that can be enhanced in effectiveness and efficiency when built with an immersive approach. We also propose a plan to apply immersive technology to the Live, Virtual, and Constructive systems for the development of future LVC integrated training system.

The pixel art style evolved from the shortcomings of computer image display technology has gradually transformed from a technical limitation into a widely recognized form of artistic expression since development in the early 20th century. This study analyzes the application and characteristics of the expandability of pixel art style design content in sub industries such as physical goods, environmental design, website design, digital art and illustration in the design field. It aims to explore the visual expression and sustainable development form of pixel art style under the development of new media technology that contradicts traditional technological concepts. The research results show that although the pixel art style generally pursues external visual features such as pixelated visual effects, sawtooth and matrix arrangements, its expansion in the art field shows a unique diversity of visual expressions. It has become an important means to convey nostalgic emotions and cultural values. Through this research, we hope to inspire more academic researchers and technology practitioners to explore the development potential of the pixel art style in emerging fields and promote its innovative application in design practice.

With significant advancements in the field of artificial intelligence, many novel algorithms and technologies have emerged. Currently, AI painting can generate high-quality images based on textual descriptions. However, it is often challenging to control details when generating images, even with complex textual inputs. Therefore, there is a need to implement additional control mechanisms beyond textual descriptions. Based on ControlNet, this passage describes a combined utilization of various local controls (such as edge maps and depth maps) and global control within a single model. It provides a comprehensive exposition of the fundamental concepts of ControlNet, elucidating its theoretical foundation and relevant technological features. Furthermore, combining methods and applications, understanding the technical characteristics involves analyzing distinct advantages and image differences. This further explores insights into the development of image generation patterns.

This study explores the feasibility and potential of using virtual reality (VR) technology to enhance the museum viewing experience through digital media. In an increasingly digital world, museums face the challenge of adapting to changing visitor expectations. This study explores the integration of virtual reality and digital media as a means to engage, educate, and attract museum visitors in a novel, immersive way. By analyzing its advantages, challenges, and practical implications, this study aims to elucidate the feasibility of this transformative approach.

AI image generation technology has become a popular research direction in the field of AI, which is widely used in the field of digital art and conceptual design, and can also be used in the process of 3D texture mapping. This paper introduces the production process of 3D texture mapping using AI image technology, and discusses whether it can be used as a new way of 3D texture mapping to enrich the 3D texture mapping production process. Two AI deep learning models, Stable Diffusion and Midjourney, were combined to generate high-quality AI textures. Finally, the lmage to material function of substance 3D Sampler was used to convert the AI-generated textures into PBR 3D texture maps. And applied in 3D environment. This study shows that 3D texture maps generated by AI image generation technology can be used in 3D environment, which not only has short production time and high production efficiency, but also has rich changes in map styles, which can be quickly adjusted and modified according to the design scheme. However, some AI texture maps need to be manually modified before they can be used. With the continuous development of AI technology, there will be great potential for further development and innovation of AI-generated image technology in the 3D content production process in the future.

With the continuous development of the game field, the subdivision of game types is becoming more and more precise. There are also more and more games that draw on some achievements in other fields in their game design. Among them are meta-fiction games based on metafiction. This article will explore whether the combination of a meta-fiction game and a side-scrolling game with a unique visual experience is appropriate. If the side-scrolling game is the external form of the game, then the meta-fiction game is the internal content core of the game. The degree of fit between the external form and the internal core will undoubtedly determine the lower limit and upper limit of the game quality. Based on alienation theory, this article takes several highly rated side-scrolling games on the market as examples to conduct a theoretical analysis on the compatibility of side-scrolling games and metafiction games. The analysis results suggest that compared to first-person games, side-scrolling games with a unique third-person perspective have the best compatibility with meta-games.

In recent years, we have witnessed a growing popularity of virtual reality (VR) technology. Understanding the factors that contribute to a comfortable VR viewing experience is crucial for its successful implementation. This study specifically explored the role of gender in determining the ideal viewing distance for VR animation. To do so, we enlisted 100 participants, comprising 41 males and 59 females, and had them engage in a VR animation viewing task, during which we recorded their preferred viewing distances. Our findings revealed that there was no significant distinction between males and females regarding their favored VR animation viewing distances. These results suggest that when creating VR content, gender may not be a noteworthy factor to take into account when determining the optimal viewing distances for a comfortable experience.

Periodic assessment of reservoir capacity is essential for better water resources management and planning for the future water use. Reservoirs and water storage structures raised on the rivers are subjected to sedimentation and he sedimentation is caused by deposition of eroded sediment particles carried by the streams. Knowledge of reservoir sedimentation is important to estimate avaliable storage capacity for optimum reservoir operation and scheduling water release. In recent years, remote sensing and GIS techniques have emerged as an important tool in carrying out reservoir capacity analysis and water management. The reduction in storage capacity as compared to the original capacity at the time of reservoir impounding is indicative of sediment deposition. In this study, the application of GIS and remote sensing techniques were applied to assess the sediment deposition, losses in the reservoir storage and the revised cumulative capacity. Satellite images covering Pyodongdong reservoir were analyzed using Erdas Imagine and ArcGIS softwares.Cumulative capacities at different levels were also calculated and we estimated that the revised live storage was 84.2Mft3 in 2021 and 64.3Mft3 in 2022 while the original capacity was 22.8 and 53.6Mft3 in 2021 and 2022.

Due to the COVID-19 pandemic, society's interest in the metaverse has recently increased rapidly. By using avatars, which are virtual objects that represent users within the metaverse, people enjoy a higher degree of freedom in the virtual world and various interactions with other people become possible. In most metaverse platforms, the user selects one from a limited number of pre-made avatar templates. Afterwards, the user must individually select the avatar's hair style, eyes, nose, and mouth shape from among those presented within the metaverse platform. Creating an avatar that resembles the user is not an easy task because the presented options for facial features and hairstyles are limited. In this paper, we present a method to automatically generate a 3D head model from a user's 2D face image and implement a prototype system. We show that a 3D head model creation system can be built at low cost and in a short time by utilizing open source at each stage of model creation. In this study, the process of applying facial texture to the 3D face shape was omitted and focused on 3D modelling work.

Augmented reality (AR), which integrates virtual media into reality, is increasingly utilized across various industrial sectors, thanks to advancements in 3D graphics and mobile device technologies. The IT industry is thus carrying out active R&D activities about AR platforms. Google plays a significant role in the AR landscape, with a focus on ARCore services. An essential aspect of ARCore is the use of anchors, which serve as reference points that help maintain the position and orientation of virtual objects within the physical environment. However, if the accuracy of anchor positioning is suboptimal when running AR content, it can significantly diminish the user's immersive experience. We are to assess the performance of these anchors in this study. To conduct the performance evaluation, virtual 3D objects, matching the shape and size of real-world objects, we strategically positioned ourselves to overlap with their physical counterparts. Images of both real and virtual objects were captured from five distinct camera trajectories, and ARCore's performance was analyzed by examining the difference between these captured images.

This paper constructed a dataset of beef carcass images with a specific resolution, and evaluated its classification performance using artificial intelligence. The dataset was also analyzed based on color and texture features. Grade information by color texture was scored using linear or average values. To analyze the dataset with a higher level of precision, regression analysis was performed based on scores rather than grades. IOCLBP(Improved opponent color local binary pattern) was used for color texture analysis, and the correlation coefficient for each color was high in Blue-gray color space. The scores for each grade in Blue- gray space were first given linearly, and the average and exponential approximation of the actual scores were also evaluated. The evaluation involved scoring using the regression mean and the approximate exponential function from the linear score results. The correlation coefficients according to the scoring method were 0.641, 0.701, and 0.675 respectively. These results showed that scoring by average was most effective. Finally, the grades were divided into non-overlapping score ranges to check the color texture analysis scores of the training dataset. The regression coefficient of the dataset was 0.947, indicating its reliability.

In the Fourth Industrial Revolution era, creative ideas are creating enormous value. This study conducted a case study on curriculum management plans aimed at protecting ideas and their results, recognizing the importance of intellectual property (IP), and cultivating basic knowledge about intellectual property. In particular, this study looked at ways to quickly learn related issues regarding new intellectual property rights related to computer software and artificial intelligence. In addition, research was conducted on ways to learn about efficient protection and utilization of inventions through actual examples. This study checked the importance and necessity of the interaction and communication between instructors and learners through the status of distance learning in domestic universities and a case study of distance learning of convergence subjects. We aim to continuously research effective class management methods and contribute to academic development through case studies of convergence subjects.

This study aims to investigate the factors influencing preventive behavior among foreigners residing in Korea. Drawing upon the Health Belief Model (HBM), this study seeks to gain insights into the decision-making processes underlying preventive behavior within this specific population. A comprehensive online survey was conducted among 364 foreigners. An analysis revealed that perceived barriers and perceived benefits played crucial roles as mediators, mediating the relationship between the examined factors and preventive behavior. The insights gained from this study have implications for public health interventions and self-preventive product businesses aiming to promote and sustain self-preventive behavior practices among foreigners residing in Korea, even after all COVID-19 restrictions have been lifted.

In this paper, we designed a IoT(Internet of Things) sensor block embedded lightweight house deck structures that can be implemented using Carbon Fiber Reinforced Polymer(CFRP). Deck-Sensor interconnection interface block via IoT connectivity Hub that can mount external environmental sensors such as fire sensors on the Deck body itself was also proposed. Additionally we described the configuration of devices for data acquisition and analysis based on IoT environmental detection sensors that can be commonly installed and used on these deck bodies. On the other hand, received sensing data based monitoring user interface(UI) also developed and used for sensing data analysis for remote monitoring center. Through the implementation of such IoT-based sensor data transmission and collection analysis devices and UI software, this paper confirmed the availability of CFRP based lightweight House deck structure and possibility of CFRP deck-based IoT sensor data networking and analysis functions.