International Journal of Internet, Broadcasting and Communication
The Institute of Internet, Broadcasting and Communication
- Quarterly
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- 2288-4920(pISSN)
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- 2288-4939(eISSN)
Domain
- Media/Communication/Library&Information ＞ Media/Consumers
Aim & Scope
The International Journal of Internet, Broadcasting and Communication (IJIBC) is an international interdisciplinary journal published by the Institute of Internet, Broadcasting and Communication (IIBC). The journal aims to present the advanced smart convergence of all academic and industrial fields through the publication of original research papers. These papers present the original and novel findings as well as important results along with various articles that have the greatest possible impact on various disciplines from the wide areas of Internet, Broadcasting and Communication (IBC). The journal covers all areas of academic and industrial fields in 8 focal sections: A. Internet Internet and Information Protection Service and Application Security Network System Security Common Security Technology Industry Security and Convergence Security Internet Violation Internet Contents Other Internet related Technology B. Broadcasting Internet Broadcasting (Webcasting, IPTV) DMB (Digital Multimedia Broadcasting) Terrestrial Broadcasting Air Channel Broadcasting Digital Broadcasting Terminals (Set Top Box, Display) Digital Broadcasting Technology, Media and Service Digital Broadcasting Contents Mobile Broadcast, Ubiquitous Convergence, Realistic Broadcasting Other Broadcasting related Technology C. Communication Internet Communication Radio Wire Communication Fixed Communication, Mobile Communication, Satellite Communication Microwave Communication, Optical Communication Multimedia Communication Digital Communication, Data Communication and Computer Communication Mobile Communication Service, System, Terminal, Satellite Navigation, Payload and Control Broadband Communication Network : Network Structural Design/Operation Support, Service and Control, Transmission Network, Subscriber Network Communication Device, Application Service and Information Protection Radio Communication, EMI/EMC, Electromagnetic Device, Electromagnetic Diagnosis and Protection Other Communication related Technology D. Convergence of Internet, Broadcasting and Communication RFID, Mobile RFID and Service Application USN(Ubiquitous Sensor Network) and Application Technology U- Computing Platform, Application, Server Technology U- Computing Device and Peripherals Software: Embeded SW, SW Solution, System Integrated(SI), Internet SW Digital Contents : Computer Graphic, Virtual Reality, Contents Creation Planning, Digital Contents Production and Distribution, Game and u- learning ITS/Telematics : ITS, Telematics Terminal and Device, ITS Application Service, Telematics Application Service u- Robot Technology, u- Health Technology, u- Engineering and Construction Technology, u- Environmental Technology, u- City Technology, u- Eco Technology, u- Medical Technology Nano Information Technology(NIT), Culture Information Technology(CIT), Biomedical Technology(BIT), Environment Information Technology(EIT) HMI(Human Machine Interaction) Technology Other Internet Ubiquitous Convergence related Technology E. Device and Module Optical Application Device, Semiconductor Equipment, Semiconductor Device and System Electrical and Electronics Parts, Home Appliances, Electronic Application Device and Information Appliances Video/Sound Devices, Battery Display - LCD, PDP, FED, EL, Display Parts and Materials, E-Paper, 3D Display Manufacturing Equipment, Measuring and Inspection Equipment, Other Display Mobile Communication, Terrestrial·Satellite, Optical Communication, Multimedia and Antenna Module and Parts Other Devices and Module related Technology F. IT Marketing and Policy RFID Application, Distribution and Business Remote Control System(Medical, Educational, Conferential, etc.), Web Security, Contents Protection, Authorization Shopping Mall Construction, B2B, B2C, Electronic Transaction, Virtual Commerce System Contents(Webzine, Cartooon, Advertisement, Design, DMB, Mobile Device) Internet Movie Theater, Theater Broadcasting Communication Policy, Regulation, Standardization Informatization Policy Other IT Marketing and Policy related Technology G. NMS(New Media Service) CCS (Cloud Computing Service) Technology SNS (Social Network Service) Technology SCS (Social Commerce Service) Technology SUS (Smartphone Utilization Service) Technology CCS,SNS,SCS,SUS Policy and Regulation CCS,SNS,SCS,SUS Marketing CCS,SNS,SCS,SUS Standardization CCS,SNS,SCS,SUS Information Protection CCS,SNS,SCS,SUS Application H. Other IT related Technology"
KSCI KCIVolume 16 Issue 4
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In recent years, experimental building fire accidents occurred frequently in colleges and universities, which caused great economic losses and casualties. In this paper, based on the analysis of the causes of fire accidents, in order to understand the fire development process and the law of smoke spread in the experimental building, Pyrosim is applied to simulate the fire caused by inadvertent use of electrical appliances, and the smoke spread, visibility, temperature, and temperature during the fire development process are studied. The results show that in the process of fire development, both temperature and carbon monoxide concentration exceed the range of human body, and the safety of personnel evacuation does not meet the current national standards. Finally, according to the simulation results, the corresponding conclusions and suggestions for improvement are given in order to provide reference for the fire of experimental buildings in colleges and universities and improve the safety performance.
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The development of the Internet and mobile technology has brought various changes to society. In particular, the growth of video platforms such as YouTube has allowed those who have watched videos through legacy media to enjoy videos freely at the time and place they want. The freedom of time and space, consequently, has changed content use behavior, causing a paradigm shift in media consumption. It has brought unprecedented changes in media consumption patterns such as vertical media, and short-form content. Starting with the new social and cultural changes brought about by the YouTube platform, this paper aims to examine the changes in newly emerged MCN companies and the media industry. In particular, this paper shall examine in depth the implementation of novel revenue diversification strategies by MCN companies, who are aware the limitations of advertising revenue received from YouTube. Such revenue diversification strategies of MCN companies appear to be excellent examples to understand and analyze trends in management strategies, as well as new marketing strategies in the digital age. By examining the changed media industry's latest corporate management strategies, it is possible to derive two implications: management insight and sociological insight.
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This platform is a platform that broadcasts the chef's cooking scenes live and teaches individual subscribers personalized cooking through auctions.The platform delivers the chef's hands-on cooking demonstration to customers in real time, and the auction-winning customers get the opportunity to participate in exclusive live broadcasts with the chef.This provides customers with an immersive learning experience, providing them with an opportunity to enhance not only culinary knowledge but also in-depth understanding and practical cooking skills.This platform places a lot of weight on social contributions beyond just commercial purposes The dishes created by the chef through live broadcasts are delivered directly to the socially underprivileged, especially the vulnerable in need of help, in collaboration with donor organizations. This demonstrates that cooking can be a means of embodying social values, not just commercial activities. In this way of operation, we want to realize a culture of sharing through cooking and combine the platform's existence value with social responsibility. Additionally, the platform provides customers with a variety of sales methods, with some popular content produced as meal kits based on clicks, subscriber reactions, and evaluation by restaurant experts. These meal kits are provided on a regular basis through the subscription system or sold in a way that the general consumer can also purchase individually. Some of the profits from meal kit sales lead to donations again, allowing the platform to have a virtuous cycle structure that continues to create social value. In conclusion, the platform redefines the modern culinary experience through a model that combines advanced culinary education with social sharing. It is creating a sustainable ecosystem that provides subscribers with special cooking experiences and in-depth academic opportunities, and at the same time provides practical help to the socially underprivileged through donations and sharing. Closely combined with culinary education, interaction, and social responsibility, the platform contains innovative attempts to incorporate the educational and social values of cooking to shed light on its new meaning and value.
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We investigate the discourse on Twitter among overseas Koreans regarding voting intentions during the COVID-19 pandemic. Employing Snscrape 0.3.4 for data collection, we gathered tweets using a set of predefined keywords related to voting, COVID-19, and overseas Korean experiences. Our content analysis, grounded in both quantitative and qualitative methodologies, followed a rigorous coding scheme developed iteratively to capture the essence of the discourse, focusing on attitudes, subjective norms, and perceived barriers to voting during the pandemic. We found a significant shift in discourse, from initial information sharing and voting encouragement to a focus on the obstacles posed by COVID-19, including the closure of diplomatic missions and the impact of social distancing measures. The findings reveal a strong collective self-efficacy among overseas Koreans, who actively sought and shared voting-related information, encouraged participation, and proposed alternative voting methods. Theoretical implications extend to the realms of self-efficacy and the theory of planned behavior, illustrating how digital platforms can mediate political mobilization and participation in unprecedented circumstances. This study contributes to the understanding of global citizenship and political engagement in the 21st century, emphasizing the importance of structural support and digital platforms in facilitating the exercise of citizenship rights during global crises.
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This paper introduces a decision-making framework for offloading tasks in home network environments, utilizing Distributed Reinforcement Learning (DRL). The proposed scheme optimizes energy efficiency while maintaining system reliability within a lightweight edge computing setup. Effective resource management has become crucial with the increasing prevalence of intelligent devices. Conventional methods, including on-device processing and offloading to edge or cloud systems, need help to balance energy conservation, response time, and dependability. To tackle these issues, we propose a DRL-based scheme that allows flexible and enhanced decision-making regarding offloading. Simulation results demonstrate that the proposed method outperforms the baseline approaches in reducing energy consumption and latency while maintaining a higher success rate. These findings highlight the potential of the proposed scheme for efficient resource management in home networks and broader IoT environments.
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This study develops design principles for creating phygital (physical and digital) cultural heritage experiences, integrating advanced technologies such as VR/AR, digital twins, and interactive storytelling. Through thematic analysis of existing literature and validation via a professional survey, five key principles were identified: Human-Centered Design, Technological Integration, Narrative Fidelity, Cultural Sensitivity, and Sustainability. These principles offer a framework for preserving cultural authenticity while enhancing user engagement and accessibility. This study explores key challenges in integrating sustainability and cultural authenticity into phygital cultural heritage projects and provides cultural heritage professionals with flexible design strategies that leverage digital technologies to create immersive, educational, and culturally respectful experiences. These adaptable strategies ensure that projects remain viable, relevant, and capable of balancing innovation with preserving heritage integrity.
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With the advancement of modern AI technology, the field of computer vision has made significant progress. This study introduces a parking management system that leverages Optical Character Recognition (OCR) and speech recognition technologies. When a vehicle enters the parking lot, the system recognizes the vehicle's license plate using OCR, while the administrator can issue simple voice commands to control the gate. OCR is a technology that digitizes characters by recognizing handwritten or image-based text through image scanning, enabling computers to process the text. The voice commands issued by the user are recognized using a machine learning model that analyzes spectrograms of voice signals. This allows the system to manage vehicle entry and exit records via voice commands, and automatically calculate paid services such as parking fees based on license plate recognition. The system identifies the text areas from images using a bounding box, converting them into digital characters to distinguish license plates. Additionally, the microphone collects the user's voice data, converting it into a spectrogram, which is used as input for a machine learning model to process 2D voice signal data. Based on the model's inference, the system controls the gate, either opening or closing it, while recording the time in real-time. This study introduces a parking management system that integrates OCR and a speech command recognition model. By training the model with multiple users' data, we aim to enhance its accuracy and offer a practical solution for parking management.
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There are various sensor technologies used to obtain target information, such as camera-based position estimation methods, LiDAR, radar, and sensor fusion. Radar technology is capable of estimating long-distance targets and determining positions even in challenging environments, such as rain, snow, fog, and darkness. Sensor data provides position information such as speed, distance, azimuth, and elevation. This paper focuses on distance measurement among these position parameters. The method for acquiring distance information applies the linear limited minimum variance method to improve the signal-to-noise ratio of the received signal, remove interference, and estimate the distance from the radar to the target using the radar equation. Through simulation experiments, the transmission signal is generated by mixing the source signal and the interference signal, and the reception signal is input to the antenna. The target distance is estimated by removing signals other than the desired components from the received signal. The simulation results show that the signal-to-noise ratio is improved by removing the interference signal, and the target distance estimation accuracy is improved.
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The primary objective of this study is to collect, clean, and analyze big data centered around news articles from portal sites and social media pertaining to League of Legends(LoL), a representative game in the esports industry. By extracting valuable information, semantic connections, and context from this unstructured data, we aim to provide practical implications for the esports industry. In order to collect popularity data of the most 'League of Legends' game among e-sports games, Textom, a big data solution service, was used to collect related keywords from October 8, 2023 to June 30, 2024. Textom collected data for Naver and Google. Specifically, 2,024 news sections, 8,874 blog sections, and 2,969 cafe sections were collected on the Naver channel. On the Google channel, 3,734 news sections and 59 Facebook sections were collected. Amounting to 17,660 materials. The collected data was analyzed using Textom and Ucinet 6.0. We conducted TF analysis and TF-IDF analysis through text mining, followed by matrix analysis and semantic network analysis. Additionally, CONCOR analysis was used to derive clusters of keywords with similar meanings. Based on the analysis results, the following conclusions were drawn. First, the most frequent keywords in the collected data were 'LOL', 'game', 'Riot Games Inc.', 'sale', and 'skin'. The TF-IDF ranking was 'game', 'Riot Games Inc.', 'sale', 'skin', and 'T1'. These two analysis results suggest that there is a high level of interest and issues related to purchasing LOL games and the developer. Second, through semantic network analysis, we identified three types of centrality. Considering the overall centrality, keywords related to competitions, developers, the T1 team, and time or seasons showed high centrality. Third, CONCOR analysis resulted in four clusters. First, as the main topic of this study is LOL, Cluster A consisted of keywords related to 'e-Sports Game'. This cluster included the most influential and popular player, Faker, and tournament names such as the World Championship. Cluster B was the 'LOL' cluster, which is the main topic of the study. Keywords related to actual participation, such as game companies, skins, patches, and play, were central to this cluster. Cluster C centered around keywords related to 'Strategy' for winning games, such as 'item build', 'Howling Abyss', 'strategy', 'Rune', 'item', and 'Counter'. Cluster D focused on keywords related to 'Transaction', such as 'sale', 'price', 'deal', 'completion', 'private transaction', 'Ahri', and 'direct payment'.
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Jinwoo Jeong;Isaac Sim;Woohyun Jang;Sangbom Yun;Jungkyu Rho 87
We explore the latest trends and future directions in network security system development, with a focus on emerging technologies aimed at strengthening defenses against increasing cyber threats. Our study reviews recent advancements across critical areas such as encryption, intrusion detection, and secure communication protocols. Additionally, we examine the potential challenges and practical applications of these technologies, especially in the context of satellite networks. Through this research, we provide new insights into how these technologies might evolve to address future security needs, contributing a unique perspective on the practical deployment of these security measures. -
In this paper, I study the Raft leader election process to enhance fault tolerance in a network composed of minimal nodes by considering various failure situations that may occur during consensus in a private blockchain network. In the process of processing network partition situations, node failure situations, and leader node failure situations, an Activity Score variable is set, so that the platform is configured with the minimum number of nodes in a network partition situation or node failure situation, and when successful leader election is required, it can be modified. Leader election is conducted according to the Raft algorithm, and leader node election and network failures are minimized based on trust to enhance fault tolerance even in a platform environment where the minimum number of nodes is operated. Excellent performance of over 12% on average was confirmed.
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Interactive media art is a contemporary art form that fundamentally relies on the active participation and interaction of visitors to reach its full potential. This art form is intricately connected to the ongoing advancements in touchscreen technology, which serve as the primary interface through which audiences engage with the artwork. Unlike traditional static art forms, interactive media art transforms visitors into active participants, whose interactions are essential for completing and continuously evolving the artistic experience. Artists and groups such as TeamLab and Miguel Chevalier are at the forefront of this innovative approach, using touchscreen technology to create immersive, dynamic environments. In these installations, visitors engage with the art through touch, gesture, and movement, which in turn influences and transforms the artwork in real time.The combination of touchscreen technology and artistic expression will redefine the boundaries of creativity. As artists continue to embrace these technological advances, audiences will experience innovative and innovative art that invites them to participate in new ways.
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In this paper, I study the application of blockchain technology in environments that require accurate handling of large-scale data, such as artificial intelligence, to enhance prediction accuracy and data performance. To address data privacy concerns and to strengthen trust in Data Privacy and security, I have researched the application-based performance of zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) using formulated approaches. For performance evaluation, I designed and developed a smart contract based on the proposed content to ensure the implementation of zk-SNARKs. The results indicate that when compared to traditional pseudonymization algorithms like Pseudonymization and tokenization, zk-SNARKs improve confidentiality by 5-10%, data privacy by over 10%, and security by more than 20%.
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We explore the effectiveness of AI-driven markerless motion capture (MoCap) tools compared to the traditional marker-based OptiTrack system, known for its high accuracy in capturing precise movements. Through detailed comparative analysis, we assessed various free markerless MoCap tools, including Move One, Radical, Deep Motion, Plask, Rokoko, and Movmi, focusing on critical aspects such as pose accuracy, movement smoothness, and ground detection. Our findings indicate that Move One is the most versatile tool, offering excellent pose accuracy, smooth MoCap, and reliable ground detection, making it a strong contender for various animation tasks. We found that Radical excels in minimizing jitter, making it suitable for projects requiring smooth motion, while Deep Motion performs best in ground detection, which is crucial for accurate foot placement. Although markerless systems still do not fully match the precision of marker-based systems, we suggest that they present viable alternatives depending on the specific needs of a project. As AI technology continues to advance, we expect the gap between markerless and marker-based to narrow, expanding the potential applications of markerless MoCap in the industry.
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Taewook Kim;Heejun Youn;Seunghyun Lee;Soonchul Kwon 133
In strawberry cultivation, maturity classification plays an important role in ensuring the efficiency and quality of harvesting. In this study, we propose an Improved Faster R-CNN model to address these challenges, using MobileNetV3-Large as the backbone network to achieve a lightweight model, and introducing RoI Align to improve the spatial accuracy of the feature map. Experiments are conducted using the KGCV_Strawberry dataset, with precision, recall, F1 score, and mean average precision (mAP) measured for performance evaluation. The experimental results show that the proposed model achieves an average precision of 71.35%, recall of 71.07%, and F1 score of 71.21% across all classes. In particular, the proposed model achieves 63% performance on mAP0.5 and 58% performance on mAP0.5:0.95, which is comparable to existing ResNet-based models while achieving faster inference speed. The proposed model achieves a processing speed of 27.6543 ms, which is about 2 ms faster than existing ResNet-based models. This indicates that the goal of creating a lightweight model with improved image processing capability was achieved with minimal performance degradation. This research is expected to contribute to the development of automated strawberry cultivation systems in greenhouse environments and has the potential to be applied to various agricultural environments in the future. -
One of the biggest drawbacks of online education using virtual environments is that teachers cannot see students' facial expressions. In offline classes, teachers usually observe students' expressions to determine if they are focused or enjoying the lesson, and they can adjust their teaching accordingly. For example, if a teacher notices that students are losing focus, they can slow down the pace of the lesson or tell an interesting story to regain their attention. However, in a virtual environment, it is impossible to see students' expressions, making it difficult to gather any information about them. As a result, instructors may feel like they are teaching in isolation and are unable to appropriately respond to students' reactions. This can easily lead to a lack of interaction between the teacher and students. This issue has already been raised in other studies, and research has been conducted to measure student engagement and attention. However, existing systems typically measure overall engagement for the entire class or represent the data in numbers or graphs, which doesn't provide impactful real-time feedback to the instructor. This study proposes an online education system that visually displays each student's level of engagement and attention in real time to address this issue. The key advantage of this system is that it allows teachers to quickly and intuitively grasp students' reactions and adjust their teaching in real time accordingly.
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This study explores the significance of AI-driven smart cities and their geopolitical implications, focusing on efficient smart economies, social and environmental connectivity, and sustainability. France is leading in AI development and adoption within Europe, possessing the necessary infrastructure and technology for smart city implementation, yet it must address social inequality and the digital divide. Japan is leveraging AI for smart city development to tackle its aging population, excelling in technology innovation and infrastructure, but it faces challenges in social acceptance and data privacy. Ireland, as a European hub for major IT companies, is well-positioned for AI smart city construction, though it must overcome issues like housing shortages and infrastructure expansion. The Netherlands must address social conflicts and housing shortages caused by high population density and increasing immigration. For successful AI smart city development, it is crucial to integrate immigrants, expand housing, and create economic opportunities. South Korea's major platforms like Naver and Kakao, are poised to play a central role in the AI smart city era, leveraging their vast data analytics capabilities, robust telecommunications infrastructure, and strong user base to enhance global competitiveness.
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This research develops a smart livestock monitoring system leveraging artificial intelligence with YOLOv8 and OC-SORT technologies to precisely monitor and analyze cow behavior, enhancing detection and tracking capabilities in complex environments. It delves into cows' movement speed and acceleration to uncover behavior patterns and health status, focusing on estrus-related behaviors for optimal breeding strategies. The study identifies changes in activity, social interactions, and mating behaviors as crucial estrus indicators, contributing significantly to livestock management innovations. By offering methods for visual behavior analysis representation, it simplifies the interpretation of findings, advancing livestock monitoring technology. This work not only contributes to smarter livestock management by providing an AI-driven cow behavior tracking model but also opens new avenues for research and efficiency improvements in the field.
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This paper proposes a solution for innovating crime prevention and real-time response through the development of the Smart Drone Police System. The system integrates big data, artificial intelligence (AI), the Internet of Things (IoT), and autonomous drone driving technologies [2][5]. It stores and analyzes crime statistics from the Statistics Office and the Public Prosecutor's Office, as well as real-time data collected by drones, including location, video, and audio, in a cloud-based database [6][7]. By predicting high-risk areas and peak times for crimes, drones autonomously patrol these identified zones using a self-driving algorithm [5][8]. Equipped with video and voice recognition technologies, the drones detect dangerous situations in real-time and recognize threats using deep learning-based analysis, sending immediate alerts to the police control center [3][9]. When necessary, drones form an ad-hoc network to coordinate efforts in tracking suspects and blocking escape routes, providing crucial support for police dispatch and arrest operations [2][11]. To ensure sustained operation, solar and wireless charging technologies were introduced, enabling prolonged patrols that reduce operational costs while maintaining continuous surveillance and crime prevention [8][10]. Research confirms that the Smart Drone Police System is significantly more cost-effective than CCTV or patrol car-based systems, showing a 40% improvement in real-time response speed and a 25% increase in crime prevention effectiveness over traditional CCTV setups [1][2][14]. This system addresses police staffing shortages and contributes to building safer urban environments by enhancing response times and crime prevention capabilities [4].
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Recently, with the rapid advancement of high-speed internet and interactive streaming technology, numerous video conferencing platforms such as Zoom, ZED, and Google Meet have been developed.. Furthermore, the COVID-19 pandemic, in particular, served as a catalyst for the global spread of online meetings and online education. This led to a significant reduction in people's resistance to online education and meetings. Nowadays, it has become common for people to use video conferencing platforms like Zoom not only for meetings but also for online education, seminars, and classes. Along with this trend, more specialized online platforms have been released, and research has been conducted on platforms that people can use more comfortably and for longer periods. This study specifically analyzes the current online video conferencing platforms from the perspective of Cognitive Overload and proposes a method to reduce Cognitive Overload. Additionally, a system was developed to address this issue. In other words, this research aims to analyze the limitations of previous online video conferencing and education platforms regarding Cognitive Overload and identity recognition, and to propose an online video conferencing and education platform that can overcome these challenges.
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Jihyun Ryu;Seungho Kim;Sungjae Park;Dahee Lee;Junhyuk Jo;Dongha Shim 181
Wall-climbing robots have been safer alternatives to humans in hazardous industrial tasks. Propeller-based wall-climbing robots have gained attention because of their ability to travel on a wall surface with an arbitrary angle. In this study, the mechanical structure and thrust analysis of the robot is introduced, considering lightweight, efficient movement, and driving stability based on conventional propeller-driven wall-climbing robots. Additionally, the thrust analysis of the propeller was conducted through Computational Fluid Dynamics (CFD) simulation to enhance operational efficiency. This analysis shows that the height of the propeller from a contacting wall surface is a significant design parameter for the thrust. Furthermore, a 3D-printed prototype robot based on the described contents is manufactured. This research is expected to provide insights for the structural design of propeller-based wall-climbing robots. -
This study explores the integration of Generative AI into Test-Driven Development (TDD) to efficiently produce code that accurately reflects programmers' requirements in software engineering. Using the Account class as an example, we analyzed the code generation capabilities of leading Generative AI models-OpenAI's ChatGPT, GitHub's Copilot, and Google's Gemini. Our findings indicate that while Generative AI can automatically generate code, it often fails to capture programmers' intent, potentially leading to functional errors or security vulnerabilities. By applying TDD principles and providing detailed test cases to the Generative AI, we demonstrated that the generated code more closely aligns with the programmer's intentions and successfully passes specified tests. This approach reduces the need for manual code reviews and enhances development efficiency. We propose a development process that combines TDD with Generative AI, leveraging the strengths of both to efficiently produce high-quality software. Future research will focus on extending this approach to more complex systems and exploring automatic test case generation techniques.
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Yosua Setyawan Soekamto;Leonard Christopher Limanjaya;Yoshua Kaleb Purwanto;Bongjun Choi;Seung-Keun Song;Dae-Ki Kang 203
The exponential increase in publications and the interconnected nature of sub-domains make traditional methods of information extraction and organization inadequate. This inefficiency can impede scientific progress and innovation. To address these challenges, this research leverages the ability of Bidirectional Encoder Representations from Transformers for keyword extraction (KeyBERT) and integrates with K-Means clustering to organize topics from large datasets effectively. Analyzing a dataset of 47,627 articles from SCOPUS in the domains of Reinforcement Learning and Computer Vision. An ablation study demonstrates the generalizability of the approach across these fields, with the optimal number of clusters determined to be three using the Elbow Method. The results demonstrate that KeyBERT is effective in extracting and organizing topics within these domains, with a particular focus on applications such as medical imaging, autonomous driving, and real-time detection systems. This methodology offers a scalable solution for organizing vast academic datasets, enabling researchers to extract meaningful insights efficiently and apply this approach to other domains. -
This paper presents a novel deep learning-based radio frequency identification (RFID) tag collision detection method for ultra-high frequency (UHF) RFID. UHF RFID technology provides longer communication range compared to NFC, barcode, and QR code technology. However, due to the longer range, multiple tags in wide range may reply simultaneously such that a reader receives superposed signal of multiple tags. Multiple tag signals interfere with each other such that reader's tag reading speed is decreased. In order to detect these tag collisions, previous studies utilized analytical methods rather than theoretical ones. Hence, a deep learning-based solution can improve the detection performance. For deep learning, training datasets are generated from mathematical equations, which are specified by the standard, with various delay times, amplitude differences, phase differences and noise level among tag signals. Arbitrary delay time, phase difference, and amplitude difference are used in every run of simulation. Simulation results show that the detection performance using the proposed method is about 5 dB better than that of existing method.
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How Monetization Shapes Webtoon Narratives: A Comparative Analysis of Solo Leveling and Tower of GodIn this study, we analyze the transformation of webtoon storytelling patterns following the implementation of the 'Free if You Wait' monetization system in the industry. Initially offered without cost, webtoons underwent substantial industrial and creative shifts with Lezhin Comics' introduction of paid services in 2013 and KakaoPage's establishment of the 'Free if You Wait' model. Our objective was to explore how monetization has influenced storytelling techniques in webtoons. Specifically, we conducted a comparative analysis of Solo Leveling and Tower of God, examining how the payment model influenced narrative structures, episode pacing, and plot progression. Through this analysis, we conclude that Solo Leveling, optimized for the 'Free if You Wait' model, employs fast-paced storytelling, frequent cliffhangers, and substantial episode content that fosters reader immersion and incentivizes paid engagement. In contrast, Tower of God emphasizes prolonged narrative arcs and deep character relationships, maintaining a relatively slower pace.
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Measures to enhance a cloud-based smart farm management system were proposed to improve its efficiency and maintenance. The existing system had achieved efficiency and stability by utilizing a web-based operating program with a general-purpose microcomputer and Linux. However, this system faced issues with synchronization and maintenance while concurrent tasks were being performed. Synchronization issues were solved by implementing an embedded DB, and the system was upgraded to allow over-the-air (OTA) software updates. Additionally, a method was also proposed to enable remote maintenance using tunneling. It was determined that applying the proposed method can contribute to the widespread adoption of smart farms, in addition to reducing maintenance costs. Furthermore, this system can also be expanded into a universal system applicable to different service models in the future.
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Jongho Lee;Ayami Kondo;Shigeyuki Igarashi;Mayumi Tokuda;Hyeonseok Kim 235
We developed and validated a portable tablet-based system to assess brain motor control abilities by engaging participants in a manual tracking task with both visible and invisible targets, thereby eliciting feedback and feedforward control mechanisms. We measured the accuracy of these mechanisms using error terms, comparing 1) the performance of the dominant and non-dominant hands and 2) the intervals of feedback and feedforward control. We showed that the dominant hand demonstrated greater accuracy than the non-dominant hand, particularly when tracking a faster-moving visible target. Furthermore, the non-dominant hand transitioned from feedback to feedforward control at a slower target speed compared to the dominant hand. This suggests differential motor control processing between hands. We present this tablet-based system as an accessible and versatile tool for assessing feedback and feedforward control during target tracking tasks, based on feedback-error learning theory. It enables efficient analysis of motor development in children, motor decline in older adults, and stroke rehabilitation outcomes from a brain motor control perspective. -
In this paper, a Quadrature Voltage Controlled Oscillator of a wireless transceiver operating in the 5GHz UNII band of the wireless LAN 802.11a standard was proposed. In addition, a new structure of low-noise, low-power Quadrature Coupled VCO was proposed using the quadrature phase output as an input to the switching current source. If this structure is applied to other circuits such as a structure in which the current source is separated or a common use of a current source, a phase noise characteristic of 17 dB better than the existing VCO can be obtained. In particular, it is designed to operate with low power in a simple structure compared to the existing in-phase QVCO. The circuit was designed to operate with a supply voltage of 1.2V by the TSMC 0.13㎛ RF CMOS process. The measured VCO has a large tuning range of 20% operating at frequencies of 4.5 - 5.6 GHz, and phase noise of -117 dBc/Hz or less was obtained at the 1 MHz offset. The output phase error of the proposed QVCO was less than 0.5 degrees, and the total power consumption was able to obtain 5.3 mW at 1.2V.
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Infrared-based scanners are utilized as a promising method for detecting objects that contact on a surface. In this system, infrared transmitters and receivers are positioned at opposite ends of the plane, facing each other. Traditionally, this system employed a one-to-one scanning method, where a single infrared transmitter emits a light signal that is detected by a corresponding receiver on the opposite side. While this method offers advantages such as fast response times and system simplicity, it is limited by its inability to detect multiple objects simultaneously. To address this limitation, recent applications have adopted the one-to-many scanning. In this scanning method, a single infrared transmitter emits a light signal that is detected by multiple receivers on the opposite side. The results are then read in real-time to determine the position and size of the object. With the recent advancements in computing power, the response speed and accuracy of one-to-many scanning have significantly improved. However, in most cases, this method has been limited to object detection on simple planes, and there is no analytical method available to support performance prediction when considering various sensor installation configurations with various form-factors. In this study, we mathematically modeled an infrared sensor array system to predict the performance of various sensor configurations installed on two-dimensional planes or curved surfaces. Additionally, we assess the critical effect of inevitable positional errors (including orientation mismatches) on the system's performance. The unique approach introduced in this paper will provide highly reliable quantitative predictions, aiding in the design of sensor network form factors tailored for various applications in the future.
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Semiconductors are crucial components in communication technology, playing important roles in various communication systems. They are essential for signal processing, data transmission, and ensuring the stability of communication networks. In particular, high-performance semiconductor chipsets and processors enable ultra-fast data transmission and ultra-low latency in communication technology. For example, semiconductors are indispensable in smartphones, wireless networks, and satellite communication systems. For semiconductor packaging products, nondestructive internal analysis for defect analysis and process improvement without causing deformation of system packaging is an important part of the product development process. In this study, nondestructive analysis techniques using X-ray equipment are discussed. The results of this study can provide fast and accurate nondestructive analysis of semiconductor packaging products and can play a significant role in supporting the growth of the communication industry.
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Yong-Jun Lee;Jun-Hyeok Lee;Jin‐Ho Choi;Min-Seo Jung;Young-Min Kim;Tae-Heon Yang;Dongbum Pyo 273
The development of a radial pulse simulator is pivotal for advancing wearable medical devices and enhancing pulse diagnosis methods prevalent in Oriental medicine. Such a simulator can be utilized for the calibration of wrist-worn wearable device sensors, as well as for training medical professionals in pulse diagnosis. This study introduces a novel, simple, and cost-effective pulse simulator that can generate a wide range of blood pressure waveforms. This simulator was designed and constructed as a prototype pulse simulator using two precision solenoid valves, an air chamber, a Half-CAM, a pneumatic sensor, and electronic control systems. By regulating air pressure through controlled opening and closing of the solenoid valve, the simulator can produce the desired pulse waveform. The performance of the proposed simulator was evaluated by replicating age-related radial pulses. Pulse waveforms generated by the simulator for four representative age groups (10, 50, 60, and 90 years) were compared with corresponding in vivo data. The experimental results demonstrated that the RMSE (Root Mean Square Error) estimate between the simulated in vivo pulse data and the actual in vivo pulse data was within 10% in all age groups. These findings demonstrate that fine pneumatic control by a solenoid valve allows the generation of sophisticated waveforms and validate that the proposed pulse simulator is capable of generating a diverse range of pulse waveforms. -
Jun Sik Shin;Yoon-Gi Ku;Jin‐Ho Choi;JoonHyeok Kang;Woo Joo Kim;Young-Hwan Park;Tae-Heon Yang;Dongbum Pyo 282
This study proposes the design of a compact haptic actuator that can be integrated into laparoscopic scissors. In laparoscopic surgery, surgical proficiency is crucial owing to visual and spatial constraints, and a haptic feedback device with diverse force profiles can significantly contribute to skill improvement. Active actuators like AC or DC motors are too bulky for handheld devices like haptic laparoscopic scissors and suffer from instability issues that disrupt the interaction with the physical environment. To address these constraints, we designed a haptic brake based on the properties of magnetorheological (MR) fluid. The proposed haptic brake can generate a torque of up to 78.4 N·mm using the viscosity change of MR fluid under a magnetic field, with a power consumption of 1.5 W. Simulation results and theoretical calculations were used to derive the optimum design variables, enabling the implementation of a compact and efficient haptic feedback mechanism. This study is expected to contribute to enhancing the performance of laparoscopic-surgery simulators, thereby improving the realism and user experience of virtual surgical training by providing effective haptic feedback in actual laparoscopic surgical environments. -
This study presents a comprehensive evaluation of various machine learning models for predicting heart failure outcomes. Leveraging a data set of clinical records, the performance of Logistic Regression, Support Vector Machine (SVM), Random Forest, Soft Voting ensemble, and XGBoost models are rigorously assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The analysis reveals that the XGBoost model outperforms the other techniques across all metrics, exhibiting the highest AUC score, indicating superior discriminative ability in distinguishing between patients with and without heart failure. Furthermore, the study highlights the importance of feature importance analysis provided by XGBoost, offering valuable insights into the most influential predictors of heart failure, which can inform clinical decision-making and patient management strategies. The research also underscores the significance of balancing precision and recall, as reflected by the F1-score, in medical applications to minimize the consequences of false negatives.
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Currently, undergoing intense competition, it becomes a great challenge for the online community group buying (hereinafter referred to as OCGB) platforms to re-tend customers. Although researches have conducted on customers' purchase intention in the context of OCGB, there are limited studies on factors influencing customers' stickiness intention. This study develops a conceptual framework to clarify the factors influencing customers' stickiness intention towards OCGB platform by integrating TPB model, Trust Transfer Theory as well as social capital theory. A questionnaire is conducted and 502 valid samples are collected to testify the proposed conceptual model. It turns out that trust in members, trust in the website and perceived behavioral control are important influencing factors of stickiness intention. Furthermore, trust in website partially mediates the association between trust in members and stickiness intention. This research improves our understanding of the mechanisms of customers' embeddedness in the online group buying community.
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Ki-Hong Kim;Ah-Reum Kim;Jun-Sik Park;Byung-Kwan Kim;Jae-Heon Son;Hwan-Jong Jeong 315
The purpose of our study was to analyze the changes in positive and negative emotions when implementing a residential health tourism program that combined forest and hot spring environments for young and middle-aged people. To achieve the purpose of this study, we designed a health tourism program utilizing Yeonginsan National Recreation Forest and hot spring facilities located in Asan-si, Chungcheongnam-do. The subjects of the study were 10 young and middle-aged people (20s-30s) and 22 middle-aged people (40s-50s). The forest environment health tourism program included a 1-hour walk in Yeonginsan National Recreation Forest, and the hot spring environment health tourism program included a 1-hour hot spring bath in Asan Hot Springs. Afterwards, they stayed in a glamping facility exposed to the forest environment and did camping activities. In order to investigate the changes in positive and negative emotions, the PANAS (positive affect and negative affect schedule) questionnaire was conducted before and after the application of the health tourism program. As a result, positive emotions increased by only 2-30 seconds in all groups, and negative emotions decreased. In summary, forest and hot spring health tourism programs appear to be sufficiently helpful in relieving stress and emotional stability. -
Recently, brand collaboration is attracting the attention of the industry as one of the strategies for brand differentiation. It is a marketing activity in which two or more brands of the same kind or different kinds create new brands together to target consumers, and it has developed into a form of productive collaboration that combines and creates new values beyond the level of mutual complementation between individual brands, and is being attempted in many fields. The sportswear industry is also recovering as it has passed the COVID-19 pandemic and has shifted to endemics. This study is to understand consumers' perceptions of sports collaboration brands on social media and provide basic data to related industries. Social media channels are Naver and Google sites. Naver channels collected data from blogs and news sections, Google channels collected data from news and Facebook sections, and used Textom version 6.0, a big data analysis solution, for data collection. The collection period was collected from May 11, 2023, during the transition from the COVID-19 pandemic to the end of the pandemic, to June 30, 2024. The collected data are 2,667 blogs and 761 news on Naver channels. They are 222 news and 41 Facebook on Google channels. The collected data was converted into standardized data through preprocessing. TF and TF-IDF were analyzed through text mining. Sixty keywords were extracted in consideration of the frequency of keywords and the importance in the sentence. Semantic matrix and Concore analysis were performed on the extracted keywords. Big data analysis programs such as Textom and Ucinet were used for big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'collaboration' showed the highest frequency with 4,860 times in relation to TF. Next, brand(3,835), sports(1,934), product(1,442), 'X'(1,125), global(1,116), release(1,064), and Nike(937), were shown in the highest order. In terms of TF-IDF, 'Nike' was the highest at 2153. Next, Puma(1,996), product(1,731), 'X'(1,659), collection(1,516), Golf(1,494), and release(1,476) were found to be high. As a result, 60 keywords were extracted and the centrality was analyzed through semantic matrix analysis. Finally, through CONCOR analysis, they were clustered into marketing strategy cluster, 'sports brand cluster', 'recommendation cluster', and 'Nike cluster'. For the results of these four cluster analysis, basic practical data were presented based on the main interest, perception, and context of 'sports brand collaboration' of consumers.
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In the K-content industry, K-pop is a vital cultural phenomenon that extends beyond music to influence fashion, beauty, tourism, and the character industries. In particular, the idol characterization industry plays a vital role in fostering a bond between artists and fans and strengthening brand identity. For example, BTS's BT21 and New Jeans' rabbit characters are effectively expanding into a variety of products and media content. These characters appear across diverse platforms, contributing to a broader engagement with fans. Moreover, generating revenue through characters serves as a tool to deeply convey the artist's musical worldview and concept, beyond short-term profits. Idol characters have the complementary function of clearly presenting the artist's image to the public and enhancing the character's prominence. Webtoons, animations, and games featuring idol characters contribute to securing and expanding new fandoms. Interactions with these characters can boost brand loyalty and increase product sales. The study reveals that character-based marketing not only strengthens fandom and enhances brand loyalty but also effectively conveys the artist's vision. It highlights the crucial role of leveraging digital platforms to boost profitability and engage audiences. Furthermore, the study explores the long-term impact of these strategies on the K-Pop industry, showing how they facilitate global expansion, market entry, and sustained growth, thereby reinforcing K-Pop's position as a leading force in global entertainment.
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In the context of art and society in the early Qing Dynasty, Chen Shu was known foremost as a morally upright woman. She was regarded as a model of virtue, being both a good wife and a caring mother, who effectively utilized her own talents and strengths. In this era, women's worth was largely determined by standards set by men, and this also extended to artistic fields. Female artists' expressions were often suppressed or made to conform to particular ideals, with femininity in art being described dismissively using phrases like "fat-free habits," "womanhood," and "popularity," aiming to diminish any uniqueness in their style. In the case of Chen Shu, however, she transcended these restrictions. Her talent and achievements in painting were not constrained to a single genre. She made notable contributions in multiple areas, including flowers and birds, human figures, and landscapes, distinguishing her from other female artists of her time who often specialized narrowly. Unlike many of her contemporaries, Chen Shu achieved a high level of artistic accomplishment that earned her recognition not only as a skilled painter but also as an innovator. Even when evaluated through the strict lens of the traditional social norms of her time, Chen Shu stands out as a remarkable painter whose skills and contributions go beyond her gender. Her work embodies a rare depth and artistic quality, making her worthy of study and admiration by modern scholars. We can see her contributions not just as an inspiration within the context of female achievements, but as invaluable to our broader understanding of Qing Dynasty art and society as a whole..
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As a significant aspect of photography, artists and photographers use it as a creative tool, yet existing image styles often remain too limited and predominantly rely on the body as a medium, lacking a systematic approach. In response, we aim to explore and organize the stylistic elements and creative processes involved in selfie photography. By examining these through an interdisciplinary lens, we identify and apply the 'participatory observation method' as a systematic approach to selfie photography creation. In this paper, we analyze the connection between participatory observation and selfie photography, investigating how this method shapes selfie imagery and its pioneering role in cultural research. Our approach positions selfie photography as a cultural research tool, serving as both a medium and a methodology that integrates observational techniques with creative expression. Through this interdisciplinary blend of observation and selfie photography, we aim to establish a more systematic methodology that can deepen the study of cultural representation and self-expression.
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We analyze the digital marketing strategies of Rare Beauty, a cosmetic brand founded by Selena Gomez in 2020, focusing on inclusivity and mental health advocacy as core pillars of its brand mission. Through an in-depth review of the brand's website design, SEO performance, social media engagement, and online review management-the key elements of a firm's digital marketing activities-we reveal Rare Beauty's success in authentically connecting with diverse audiences and fostering brand loyalty. Our analysis uncovers noteworthy findings: while Rare Beauty excels in creating a mission-driven aesthetic across digital platforms, there are areas for improvement, particularly in enhancing user experience by improving website readability, refining the review filtering system, and expanding social media engagement. Optimizing technical SEO could further increase discoverability. We propose these recommendations to strengthen Rare Beauty's online presence and demonstrate how the brand's unique approach offers valuable insights for industry professionals aiming to integrate social values into digital marketing strategies.
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Prompted by the challenges posed by an ageing society, this study contemplates design orientations from the perspective of inclusive design. It explores the adaptation of digital medical interfaces for the elderly to enhance design inclusiveness, catering to the senior user group and optimizing interactive experience in the medical system. This study employs the concept of inclusive design and analyzes its characteristics through literature. It distills the elements of the digital medical interface design for the elderly from three aspects: functional purpose, interactive behavior, and emotional expression. Using user research methods such as in-depth interviews and field research, it creates user personas and behavioral analysis diagrams for elderly patients with chronic diseases, organizing and categorizing their pain points. This study proposes principles for service touchpoint improvement based on inclusiveness. We optimize pain points and streamline the design process for age-friendly services, helping the elderly adapt to and integrate with digital life. By infusing inclusive design principles, we enhance the accessibility and inclusiveness of service design, elevating the service experience for the elderly. Our approach to age-friendly service design offers a valuable entry point for research focused on elderly-centered services and provides actionable strategies for developing agefriendly medical service processes.
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With the continuous progress of science and technology, the film industry is rapidly developing in the direction of digitalization and intelligence, thereby the concept of "film industrialization" being also mentioned more and more frequently. The continuous development of digital special effects technology has brought brand new changes to Chinese film industry and became an indispensable part of the modern film production. This study analyzed the impact of the development of digital special effects technology on film industry and the practical experience of Chinese film industrial ization by taking the Wandering Earth series as an example. The industrialization development trend of Chinese films was elaborated in terms of standardization of post-production visual effects process, specialization of production industry standard, and promotion of standardization of film industry. The key founding of this study is that digital special effects technology has not only changed the visual effects of films, but has also profoundly influenced the production mode and market pattern of the film industry. Therefore, we see digital effects technology as both irreplaceable and full of potential in modern film production. In our work, digital special effects play a pivotal role in advancing film industrialization. We anticipate that, as digital effects technology continues to evolve, China will make significant strides in film industrialization, providing robust support for the ongoing growth and prosperity of the market.
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As Artificial Intelligence (AI) continues to permeate various sectors such as healthcare, finance, and transportation, the importance of securing AI systems against emerging threats has become increasingly critical. The proliferation of AI across these industries not only introduces opportunities for innovation but also exposes vulnerabilities that could be exploited by malicious actors. This comprehensive review delves into the current landscape of AI security, providing an in-depth analysis of the threats, challenges, and mitigation strategies associated with AI technologies. The paper discusses key threats such as adversarial attacks, data poisoning, and model inversion, all of which can severely compromise the integrity, confidentiality, and availability of AI systems. Additionally, the paper explores the challenges posed by the inherent complexity and opacity of AI models, particularly deep learning networks. The review also evaluates various mitigation strategies, including adversarial training, differential privacy, and federated learning, that have been developed to safeguard AI systems. By synthesizing recent advancements and identifying gaps in existing research, this paper aims to guide future efforts in enhancing the security of AI applications, ultimately ensuring their safe and ethical deployment in both critical and everyday environments.
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This study focuses on optimizing the layout of mobile educational content using AI technology, with a particular emphasis on vertical aspect ratio design. Against the backdrop of changing educational content consumption patterns due to the increased mobile device usage and advancements in AI technology, this research analyzes the characteristics and effects of vertical aspect ratio design and explores its potential combination with AI technology. The research methodology combines John Yablonski's UX laws and the concept of human effective field of view with AI technology to analyze the impact of vertical aspect ratio design on the educational content user experience and learning effectiveness. Results show that vertical aspect ratio design effectively focuses users' attention, reduces cognitive load, and contributes to increased learning immersion. Specifically, when combined with AI technology, vertical aspect ratio design proves effective in providing personalized learning experiences, enhancing learning abilities, developing creativity, and optimizing data analysis across various domains. This study is expected to contribute to the qualitative improvement of educational content by emphasizing the importance of vertical aspect ratio design in mobile learning environments and proposing optimization methods using AI technology. Future studies are anticipated to further develop these findings, providing important guidelines for mobile educational content development and the advancement of AI educational technology.
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The difference of this paper is that it analyzes the latest machine learning and deep learning tools for various tasks of program such as program search, understanding, completion, and review. In addition, the purpose of this study is to increase the understanding of various tasks of program by examining specific cases of applying various tasks of program based on tools. Recently, machine learning (ML) and deep learning (DL) technologies have contributed to automation and improvement of efficiency in various software development tasks such as program search, understanding, completion, and review. This study examines the characteristics of the latest ML and DL tools implemented for various tasks of program. Although these tools have many strengths, they still have weaknesses in generalization in various programming languages and program structures, and efficiency of computational resources. In this study, we evaluated the characteristics of these tools in a real environment.
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Initializing blocks of a file is a frequently used function on computers. One of the simplest ways to initialize a file block is to open the file and initialize all data. This allows you to completely erase the data the file previously contained. In this paper, we design a system that initializes file blocks within a specific folder. When you specify a folder you want to initialize, the files in the folder are found, the file data is read, and the read data is initialized and saved in the file. In this way, all file blocks in a specific folder are initialized. The experiment was performed on the function proposed in this paper. As a result of the experiment, it was confirmed that initialization of file blocks in a specific folder worked normally.
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Datasets are a foundational step in the development of any Artificial Intelligence (AI) powered solutions. In cybersecurity, especially in malware detection and mitigation, cybersecurity AI datasets focusing on malware can play a critical role in improving accuracy and efficiency of AI models. In this paper we explore several recent techniques used in construction of malware AI datasets, identify gaps and recommend practical solutions to address them. Specifically, we explore various frameworks and techniques for improving data collection, preprocessing and dataset validation. Furthermore, we explore various recent approaches applied in AI based malware detection. In a special way we examine shallow learning, deep learning, bio-inspired computing, behavior-based detection, heuristic-based approaches, and hybrid approaches. We then draw our observations and recommend specific strategies for improving the process of malware AI dataset construction as well as detection techniques. Through our research we also contribute to the ongoing much needed efforts for combating malware attacks by providing a framework for building quality malware focused cybersecurity AI datasets, there by improving the current state of the art AI-powered malware detection systems.
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Gyounghyun Kim;Heejun Youn;Seunghyun Lee;Soonchul Kwon 432
One of the key elements for maximizing user immersion in virtual reality (VR) is the development of intuitive and sensory interaction methods. While physical devices such as controllers in existing VR equipment are used to control the user's movement intentions, their drawback is that they cannot reflect detailed muscle strength. In this study, we designed a novel interaction method that increases user immersion by reflecting the activity of leg muscles in the VR environment, moving away from the traditional hand-centered control method. In the experiment, surface electromyography (sEMG) was used to measure the muscle activity of the gastrocnemius and tibialis anterior muscles in six participants. Within the VR program, various virtual objects were implemented that responded to the movement and strength of the lower limbs, allowing for a detailed reflection of the user's lower limb movements in the VR environment. The results showed that the interaction method using lower limb muscle activity demonstrated higher user immersion and satisfaction compared to the conventional controller-based method. Additionally, participants reported feeling as if they were using their entire body, greatly enhancing the sense of realism in the VR experience. This study presents a new interaction paradigm utilizing lower limb movements in VR technology and demonstrates its potential for application in various fields such as VR games, rehabilitation training, and sports simulation. -
Currently, many industrial fields are pursuing research and development toward a hyper-connected society. However, as we become a hyper-connected society that perceives virtual reality as if it were reality, accurate classification of data to recognize objects, emotions and facial expressions must be accompanied. In other words, only when data meaning objects, emotions, and facial expressions are accurately classified will reliability of cognition and recognition be obtained not only in the physical world but also in a hyper-connected society. In addition, errors in perception and recognition of objects, emotions, and facial expressions can be reduced through big data analysis, and it will be protected from secondary incidents and damages. Therefore, in this study, we try to find out whether the classification of data is well done in the stage where AI with automatic cognition ability recognizes and recognizes objects, emotions, and facial expressions, and whether the data classified according to characteristics is a reliable classification result. In the experiment, when classifying data using a decision tree, we plan to conduct a study to find out whether the classification criteria of the data affect the classification criteria according to the degree of correlation between variables.
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MongoDB, a document-based database, is suitable for distributed management environments of large-scale databases due to its high scalability, performance, and flexibility. Recently, as MongoDB has been widely used as a new database, many studies have been conducted including data modeling for MongoDB and studies on applying MongoDB to various applications. In this paper, we propose a data modeling method for implementing Seoul public transportation data with MongoDB. Seoul public transportation data is public data provided by the Korea Public Data Portal. In this study, we analyze the target data and find design patterns such as polymorphic pattern, subset pattern, computed pattern, and extended reference pattern in the data. Then, we present data modeling with these patterns. We also show examples of implementation of Seoul public transportation database in MongoDB. The proposed modeling method can improve database performance by leveraging the flexibility and scalability that are characteristics of MongoDB.
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The purpose of this study was to investigate the association between smartphone use and consumption of high-caffeine drinks among adolescents. We studied with secondary data from 2022(18th) Korea Youth Risk Behavior (KYRBS). The respondents of this study were 51,850 participants. Data analysis was performed using IBM SPSS 25 ver. Descriptive statistics, chi-square analysis and complex sample logistic regression analysis. As a research result, participants reporting 3 times over of high-caffeine drink consumption showed 1.65 times higher of smartphone us(OR 1.65; 95% CI 1.220-2.243) and participants reporting 3 times and less of high-caffeine drink consumption showed 1.17 times higher smartphone use than '≤4 hours smartphone use. Our study results will be provided with basis information for the developing an intervention program to reduce smartphone usage time and high-caffeine drink consumption for adolescents high.
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This study investigates an educational approach for enhancing foreign language writing skills in cyber university students by promoting the self-directed use of ChatGPT. Using a case study method, the research explores both the potential benefits and limitations of ChatGPT, a generative AI that can support writing tasks by providing real-time feedback, text summaries, and support for various writing forms, including essays and stories. While ChatGPT offers advantages such as reducing instructors' feedback workload and fostering improvements in students' writing, concerns arise regarding ChatGPT's provision of inaccurate information and its potential to encourage plagiarism if students submit ChatGPT-generated content without proper revision. To address challenges, this study proposes instructional strategies for creating effective prompts that can elicit meaningful feedback from ChatGPT, alongside methods for students to integrate and reflect upon this feedback throughout each stage of the writing process. These instructional strategies are designed to enhance students' independent learning and encourage the responsible use of ChatGPT in educational settings.
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Computerized cognitive training utilized to enhance cognitive function in dementia patients enables them to autonomously execute and acquire tasks while obtaining prompt and precise feedback on their performance. We are designed to highlight the efficacy and clinical relevance of computerized cognitive training programs as therapies for elderly adults with mild dementia. In accordance with the Cochrane Handbook for Systematic Reviews of Interventions, we conducted a review of pertinent literature across various databases, including the Korean Information Service System, Research Information Sharing Service, National Assembly Digital Library, DBpia, and PubMed, encompassing research from 2003 to 2023. Utilizing rigorous inclusion and exclusion criteria, we examined a final sample of 12 research. The data indicated that following computerized cognitive training interventions for senior adults with mild dementia, ADAS-Cog exhibited the most substantial effect size (g=-1.400), succeeded by MMSE (g=0.631), DRS (g=0.522), BNT (g=0.335), and GDS (g=-0.304), ranked by intervention efficacy. The findings allow us to assert that computerized cognitive training programs significantly enhance cognitive function, alleviate depressive symptoms, and improve language abilities in elderly individuals with mild dementia.
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This study attempted to identify factors affecting the subjective health perception of middle-aged women. Secondary data analysis was conducted based on the first year of the 9th National Health and Nutrition Survey by the Korea Disease Control and Prevention Agency, and 969 middle-aged women aged 40 to 59 were the final analysis subjects. We set the following variables to increase the value of the paper: age, economic activity status, subjective body type perception, perceived stress, average daily sleep time, and daily sitting time. Data analysis was performed using the SPSS WIN 25.0 program, and descriptive statistics, t-test, correlation, and hierarchical multiple regression analysis were performed. Through this study, we found that subjective health perception was mainly influenced by perceived stress, subjective body shape perception, age, and daily sitting time. In order to increase the level of subjective health perception, it is necessary to provide necessary health management programs by age, and it is necessary to establish a management program that can change body shape perception into positive thinking.
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South Korea has transformed from one of the world's poorest countries into one of its wealthiest. Since the Korean War, the nation has not only elevated its standard of living through technological innovations but has also become a prolific producer of globally popular cultural content. This rise in the popularity of K-culture has attracted learners from various countries to the Korean language. Located strategically between China and Japan, Korea draws numerous foreign language learners, including international students and industrial trainees from countries such as Vietnam and Uzbekistan. Pronouncing Korean accurately poses challenges due to the pronunciation habits rooted in the learners' native languages. Previous research focused on analyzing the pronunciation characteristics of Chinese or Vietnamese speakers and proposed the use of a Support Vector Machine (SVM) discriminator. This study aims to refine the parameters of the SVM's hyperplane to better distinguish pronunciation variations. It introduced research that leverages this discriminator to facilitate more precise Korean pronunciation among non-native speakers.