Large-size, organic light-emitting device (OLED) panels based on highly reliable gate driver circuits integrated using InGaZnO thin film transistors (TFTs) were developed to achieve ultra-high resolution TVs. These large-size OLED panels were driven by using a novel gate driver circuit not only for displaying images but also for sensing TFT characteristics for external compensation. Regardless of the negative threshold voltage of the TFTs, the proposed gate driver circuit in OLED panels functioned precisely, resulting from a decrease in the leakage current. The falling time of the circuit is approximately 0.9 ㎲, which is fast enough to drive 8K resolution OLED displays at 120 Hz. 120 Hz is most commonly used as the operating voltage because images consisting of 120 frames per second can be quickly shown on the display panel without any image sticking. The reliability tests showed that the lifetime of the proposed integrated gate driver is at least 100,000 h.

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

Increasing demand for increasing higher data rate in order to solve computationally tasks timely and connecting many user equipment simultaneously have requested researchers to develop novel technology in the area of mobile communications. Intelligent reflecting surface (IRS) have been enabling technologies for commercialization of the fifth generation (5G) networks and the sixth generation (6G) systems. In this paper, we investigate a bit-error rate (BER) analysis on IRS technologies for non-orthogonal multiple access (NOMA) systems. First, we derive a BER expression for IRS-NOMA systems with Rician fading channels. Then, we validate the BER expression by Monte Carlo simulations, and show numerically that BER expressions are in good agreement with simulations. Moreover, we investigate the BER of IRS-NOMA systems with Rician fading channels for various numbers of IRS elements, and show that the BERs improve as the number of IRS elements increases.

A high-speed data transmission system is designed for the ground inspection equipment of satellite measurement and control. Based on USB2.0, the system consists of interface chip CY7C68013A, programmable logic processing unit EP4CE30F23C8, analog/digital and digital/analog conversion units. The working principle of data transmission is analyzed, and the system software logic and hardware composition scheme are detailed. The system was utilized to output/capture and store specific data packets. The results show that the high-speed data transmission speed can reach 38MB/s, and the system is effective for satellite test requirements.

This paper emphasizes that information received in disaster situations can lead to disparities in the effectiveness of communication, potentially causing damage. As a result, there is a growing demand for disaster and safety information among citizens. A user-centered disaster and safety information application service is designed to address the rapid dissemination of disaster and safety-related information, bridge information gaps, and alleviate anxiety. Through the Open API (Open Application Programming Interface), we can obtain clear information about the weather, air quality, and guidelines for disaster-related actions. Using chatbots, we can provide users with information and support decision-making based on their queries and choices, utilizing cloud APIs, public data portal open APIs, and solution knowledge bases. Additionally, through Mashup techniques with the Google Maps API and Twitter API, we can extract various disaster-related information, such as the time and location of disaster occurrences, update this information in the disaster database, and share it with users.

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

Slotted-Aloha (S-Aloha) has been widely employed in random access networks owing to its simple implementation in a distributed manner. To enhance the throughput performance of the S-Aloha, connection-based slotted-Aloha (CS-Aloha) has been proposed in recent years. The fundamental principle of the CS-Aloha is to establish a connection with a short-sized request packet before transmitting data packets. Subsequently, the connected node transmits long-sized data packets in a batch of size M. This approach efficiently reduces collisions, resulting in improved throughput compared to the S-Aloha, particularly for a large M. In this paper, we address the short-term fairness of the CS-Aloha, as quantified by Jain's fairness index. Specifically, we evaluate how equitably the CS-Aloha allocatestime slots to all nodes in the network within a finite time interval. Through simulation studies, we identify the impact of system parameters on the short-term fairness of the CS-Aloha and propose an optimal transmission probability to support short-term fairness.

This study compared news coverage of national and international disasters, Hurricane Katrina and the Haiti Earthquake, using textual analysis of The New York Times and The Washington Post. The results reveal that media framing of the historical cases developed in three stages upon the development of post-disaster relief: (1) Call for humanitarian assistance; (2) New Orleans under anarchy and hopelessness vs. Haiti under scrutiny with hope; and (3) Katrina effects. By framing the outcomes of the hurricane as the "Katrina effect," the media used the disaster as a reference point to explain other economic and political issues. In addition, analysis of relevant statements and press releases confirmed that different social actors involved in the relief process, such as donors, facilitators, and beneficiaries, contributed to the media framing of the issue, although the facilitators were most successful in transferring their own frames to media frames. This study makes important contributions to the field as it looks beyond traditional relationships between quantitative measures of media attention and aid allocation. For governmental and nongovernmental organizations in the area of humanitarian assistance, the findings of this study will assist them in media-relations in the future.

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

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

As technology advanced dramatically in the late 20th century, a new era of science arrived. The emerging era of scientific discovery, variously described as e-Science, cyberscience, and the fourth paradigm, uses technologies required for computation, data curation, analysis, and visualization. The emergence of the fourth research paradigm will have such a huge impact that it will shake the foundations of science, and will also have a huge impact on the role of data-information infrastructure. In the digital age, the roles of data-information professionals are becoming more diverse. As eScience emerges as a sustainable and growing part of research, data-information professionals and centeres are exploring new roles to address the issues that arise from new forms of research. The functions that data-information professionals and centeres can fundamentally provide in the e-Science area are data curation, preservation, access, and metadata. Basically, it involves discovering and using available technical infrastructure and tools, finding relevant data, establishing a data management plan, and developing tools to support research. A further advanced service is archiving and curating relevant data for long-term preservation and integration of datasets and providing curating and data management services as part of a data management plan. Adaptation and change to the new information environment of the 21st century require strong and future-responsive leadership. There is a strong need to effectively respond to future challenges by exploring the role and function of data-information professionals in the future environment. Understanding what types of data-information professionals and skills will be needed in the future is essential to developing the talent that will lead the transformation. The new values and roles of data-information professionals and centers for 21st century researchers in STEAM are discussed.

In the user-centered design paradigm, information systems are created entirely tailored to the users who will use them. When the functions of a complex system meet a simple user interface, users can use the system conveniently. While web personalization services are emerging as a major trend in portal services, portal companies are competing for a second service, such as introducing 'integrated communication platforms'. Until now, the role of the portal has been content and search, but this time, the goal is to create and provide the personalized services that users want through a single platform. Personalization service is a login-based cloud computing service. It has the characteristic of being able to enjoy the same experience at any time in any space with internet access. Personalized web services like this have the advantage of attracting highly loyal users, making them a new service trend that portal companies are paying attention to. Researchers spend a lot of time collecting research-related information by accessing multiple information sources. There is a need to automatically build interest information profiles for each researcher based on personal presentation materials (papers, research projects, patents). There is a need to provide an advanced customized information service that regularly provides the latest information matched with various information sources. Continuous modification and supplementation of each researcher's information profile of interest is the most important factor in increasing suitability when searching for information. As researchers' interest in unstructured information such as technology markets and research trends is gradually increasing from standardized academic information such as patents, it is necessary to expand information sources such as cutting-edge technology markets and research trends. Through this, it is possible to shorten the time required to search and obtain the latest information for research purposes. The interest information profile for each researcher that has already been established can be used in the future to determine the degree of relationship between researchers and to build a database. If this customized information service continues to be provided, it will be useful for research activities.

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

Smart home in the 4th industrial revolution environment is where all devices in the home are connected to each other to provide the optimal living environment desired by the user. Artificial intelligence speakers are being used as a way to manage and control all devices used in this environment. The function of an artificial intelligence speaker ranges from simple music playback to serving as an interface that controls and manages all devices in a smart home space. In this study, we investigated and analyzed the usability of artificial intelligence speakers based on the current status of domestic and overseas markets and the survey contents of two organizations (Korea Consumer Agency and Korea Information and Communication Policy Institute (KISDI)). In addition, we investigated and analyzed the usability of artificial intelligence speakers. Based on the results of responses from users from two related organizations, major problems were derived, and major improvement measures, such as discovering new functions and improving voice recognition performance, were also described.

Recent advancements in cloud service virtualization technologies have witnessed a shift from a Virtual Machine-centric approach to a container-centric paradigm, offering advantages such as faster deployment and enhanced portability. Container orchestration has emerged as a key technology for efficient management and scheduling of these containers. However, with the increasing complexity and diversity of heterogeneous workloads and service types, resource scheduling has become a challenging task. Various research endeavors are underway to address the challenges posed by diverse workloads and services. Yet, a systematic approach to container orchestration for effective cloud management has not been clearly defined. This paper proposes the DRA-Engine (Dynamic Resource Allocation Engine) for resource scheduling in container orchestration. The proposed engine comprises the Request Load Procedure, Required Resource Measurement Procedure, and Resource Provision Decision Procedure. Through these components, the DRA-Engine dynamically allocates resources according to the application's requirements, presenting a solution to the challenges of resource scheduling in container orchestration.

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

In recent times, an absence of effective crowd management has led to numerous stampede incidents in crowded places. A crucial component for enhancing on-site crowd management effectiveness is the utilization of crowd counting technology. Current approaches to analyzing congested scenes have evolved beyond simple crowd counting, which outputs the number of people in the targeted image to a density map. This development aligns with the demands of real-life applications, as the same number of people can exhibit vastly different crowd distributions. Therefore, solely counting the number of crowds is no longer sufficient. CSRNet stands out as one representative method within this advanced category of approaches. In this paper, we propose a crowd counting network which is adaptive to the change in the density of people in the scene, addressing the performance degradation issue observed in the existing CSRNet(Congested Scene Recognition Network) when there are changes in density. To overcome the weakness of the CSRNet, we introduce a system that takes input from the image's information and adjusts the output of CSRNet based on the features extracted from the image. This aims to improve the algorithm's adaptability to changes in density, supplementing the shortcomings identified in the original CSRNet.

With the rapid development of AI technology, ChatGPT and other AI content creation tools are becoming common, and users are becoming curious and adopting them. These tools, unlike search engines, generate results based on user prompts, which puts them at risk of inaccuracy or plagiarism. This allows unethical users to create inappropriate content and poses greater educational and corporate data security concerns. AI content detection is needed and AI-generated text needs to be identified to address misinformation and trust issues. Along with the positive use of AI tools, monitoring and regulation of their ethical use is essential. When detecting content created by AI with an AI content detection tool, it can be used efficiently by using the appropriate tool depending on the usage environment and purpose. In this paper, we collect data on AI content detection tools and compare and analyze the functions and characteristics of AI content detection tools to help meet these needs.

With the recent advances in AI (artificial intelligence) and HPC (high-performance computing) technologies, deep learning is proliferated in various domains of the 4th industrial revolution. As the workload volume of deep learning increasingly grows, analyzing the memory reference characteristics becomes important. In this article, we analyze the memory reference traces of deep learning workloads in comparison with traditional workloads specially focusing on read and write operations. Based on our analysis, we observe some unique characteristics of deep learning memory references that are quite different from traditional workloads. First, when comparing instruction and data references, instruction reference accounts for a little portion in deep learning workloads. Second, when comparing read and write, write reference accounts for a majority of memory references, which is also different from traditional workloads. Third, although write references are dominant, it exhibits low reference skewness compared to traditional workloads. Specifically, the skew factor of write references is small compared to traditional workloads. We expect that the analysis performed in this article will be helpful in efficiently designing memory management systems for deep learning workloads.

This study is about a speech recognition error correction system designed to detect and correct speech recognition errors before natural language processing to increase the success rate of intent analysis in natural language processing with optimal efficiency in various service domains. An encoder is constructed to embedded the correct speech token and one or more error speech tokens corresponding to the correct speech token so that they are all located in a dense vector space for each correct token with similar vector values. One or more utterance tokens within a preset Manhattan distance based on the correct utterance token in the dense vector space for each embedded correct utterance token are detected through an error detector, and the correct answer closest to the detected error utterance token is based on the Manhattan distance. Errors are corrected by extracting the utterance token as the correct answer.

User reviews posted in the application market show high relevance with the satisfaction of application users and its significance has been proven from numerous studies. User reviews are also crucial data as they are essential for improving applications after its release. However, as infinite amounts of user reviews are posted per day, application developers are unable to examine every user review and address them. Simply addressing the reviews in a chronological order will not be enough for an adequate user satisfaction given the limited resources of the developers. As such, the following research suggests a systematical method of analyzing user reviews with a cost-benefit analysis, in which the benefit of each user review is quantified based on the number of positive/negative words and the cost of each user review is quantified by using function point, a technique that measures software size.

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

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

In this study, we compared the VacCAD and VacTran, commercial vacuum simulators, to investigate the simulation applicability and efficiency as vacuum simulation software. It was verified on reliability and simplicity of simulation modelling, and characteristics of the pump combinations, pumping down curves, and employed vacuum materials. First, usability of simulation schematics was estimated through the modeling tools and the overall simulation characteristics of each simulator were compared to evaluate the applicability in practice. Simulation reliability of each simulator was also probed by comparing the pumping performance characteristics of commercial high vacuum system models. In addition, the degree of tolerances on both simulators was also evaluated through pumping down analysis considering outgassing effect due to chamber material variations. The higher effectiveness and expediency of VacCAD than VacTran has been presented, and it was also expected that the utilization of VacTan in vacuum applications to be increased due to the higher availability of modelling variations.

This study aims to investigate the effects of telepresence on young moviegoers' flow experiences and social interactions, and the impact on consumer delight, trust, and experience sharing behavior on cinema mobile social network site pages. Given the scarcity of telepresence research, indirect telepresence on experience sharing via two experiences and social interactions is also included. The study used pages from Korean cinema mobile social network sites, and 175 Chinese moviegoers residing in Korea participated. We found that telepresence positively impacts the activity in both human-human and human-computer interactions. We further contend that telepresence positively affects perceived enjoyment and attentional focus. However, perceived enjoyment does not significantly affect consumer delight. We found that consumer delight positively influences consumer trust and movie experience sharing. Moreover, we illustrated that telepresence significantly and indirectly influences consumer movie experience-sharing behavior through attention focus and consumer delight. Our results provide crucial insights for future study and practical managerial.

In the cloud service, system resource such as CPU, memory, I/O bandwidth are shared among multiple users. Particularly, in Linux containers environment, I/O bandwidth is distributed in proportion to the weight of each container through the BFQ I/O scheduler. However, since the I/O scheduler can only be applied to conventional block storage devices, it cannot be applied to Zoned Namespace(ZNS) SSD, a new storage interface that has been recently studied. To overcome this limitation, in this paper, we implemented a weighted proportional I/O bandwidth sharing scheme for ZNS SSDs in dm-zoned, which emulates conventional block storage using ZNS SSDs. Each user receives a different amount of budget, which is required to process the user's I/O requests based on the user's weight. If the budget is exhausted I/O requests cannot be processed and requests are queued until the budget replenished. Each budget refill period, the budget is replenished based on the user's weight. In the experiment, as a result, we can confirm that the I/O bandwidth can be distributed on their weight as we expected.

This study explores the evolving landscape of consumer experiences in the context of pop-up stores, considering the shifts from product economy to service economy and now the experience economy. It investigates the factors influencing consumer word-of-mouth intentions by examining the interplay of pop-up store experiences, brand equity, brand charisma, and verbal intent. Using Schmitt's strategic experience modules and the Aaker brand equity model, the study employs quantitative methods and data analysis to uncover the relationships among these variables. Surprisingly, it finds limited associations between the aspects of the pop-up store experience and brand equity. However, it highlights the direct impact of brand equity on brand charisma, which subsequently influences consumers' intentions to share brand-related information. This research contributes to our understanding of word-of-mouth marketing for pop-up stores, filling a knowledge gap and offering valuable insights for academics and businesses navigating the evolving marketing landscape. It also emphasizes the significance of brand charisma in the context of transient in-store experiences and evolving consumer preferences.

Due to the development of science and technology in the 4th Industrial Revolution, a variety of content is being developed and utilized through educational courses linked to digital textbooks. Students use smart devices to engage in realistic virtual learning experiences, interacting with the content in digital textbooks. However, while many realistic contents offer visual and auditory effects like 3D VR, AR, and holograms, olfactory content that evokes actual sensations has not yet been introduced. Therefore, in this paper, we designed and implemented 4D educational content by adding the sense of smell to existing content. This implemented content was tested in classrooms through a curriculum-based evaluation. Classes taught with olfactory-enhanced content showed a higher percentage of correct answers compared to those using traditional audio-visual materials, indicating improved understanding.

The representative branch of ICT education is Arduino. However, there are various problems when teaching using Arduino. Arduino requires a complex understanding of hardware and software, and this can be perceived as a difficult course, especially for beginners who are not familiar with programming or electronics. Additionally, the process of connecting the pins of the Arduino board and components must be accurate, and even small mistakes can lead to project failure, which can reduce the learner's concentration and interest in learning Arduino. Existing Arduino learning content consists of text and images in 2D format, which has limitations in increasing student understanding and immersion. Therefore, in this paper analyzes the necessary conditions for sprouting 'growing kidney beans' in the first semester of the fourth grade of elementary school, and builds an automated experimental environment using Arduino. Augmented reality of the pin connection process was designed and produced to solve the difficulties when building an automation system using Arduino. After 3D modeling Arduino and components using 3D Max, animation was set, and augmented reality (AR) content was produced using Unity to provide learners with more intuitive and immersive learning content when learning Arduino. Augmented reality (AR)-based Arduino learning content production is expected to increase educational effects by improving the understanding and immersion of classes in ICT education using Arduino and inducing fun and interest in physical computing coding education.

The purpose of this paper is to understand and express emotions and experiences from the perspective of others through empathy, which is crucial for maintaining social relationships. The smooth formation of interpersonal relationships through the physical activity of children with disabilities holds significant meaning. Children with disabilities often lack opportunities for interaction with their peers compared to typical children, and the absence of effective communication methods poses difficulties in forming relationships. Therefore, this study aimed to investigate the effects of a movement education program on enhancing empathy in children with disabilities. The program was implemented for 12 weeks from April to June 2023, involving five children with disabilities. The movement education program comprised 12 topics, encompassing physical, emotional, and cognitive domains. Empathy was measured in two areas: cognitive empathy and emotional empathy. The results indicated improvement in both cognitive and emotional empathy after the program compared to the pre-assessment. The rate of progress varied depending on the type and severity of the disability, but overall, positive changes in the development of empathy were observed. Through this research, it is hoped that movement programs can be practically utilized as a valuable resource.

This article is set against the backdrop of the rapid development of the metaverse and artificial intelligence technologies, and aims to explore the possibility and potential impact of integrating AI technology into the traditional 3D animation production process. Through an in-depth analysis of the differences when merging traditional production processes with AI technology, it aims to summarize a new innovative workflow for 3D animation production. This new process takes full advantage of the efficiency and intelligent features of AI technology, significantly improving the efficiency of animation production and enhancing the overall quality of the animations. Furthermore, the paper delves into the creative methods and developmental implications of artificial intelligence technology in real-time rendering engines for 3D animation. It highlights the importance of these technologies in driving innovation and optimizing workflows in the field of animation production, showcasing how they provide new perspectives and possibilities for the future development of the animation industry.

In recent years, with the rapid development of real-time rendering technology, the quality of the images produced by real-time rendering has been improving, and its application scope has been expanded from games to animation and advertising and other fields. This paper analyses the development status of real-time rendering technology in 3D animation by investigating the 3D animation market in China, which concludes that the number of 3D animations in China has been increasing over the past 20 years, and the number of 3D animations using real-time rendering has been increasing year by year and exceeds that of 3D animations using offline rendering. In this study, a real-time rendering and offline rendering 3D animation are selected respectively to observe the screen effect of characters, special effects and environment props, and analyse the advantages and disadvantages of the two rendering technologies, and finally conclude that there is not much difference between real-time rendering 3D animation and offline rendering 3D animation in terms of quality and the overall sense of view, and due to the real-time rendering of the characteristics of the WYSIWYG, the animation designers can better focus on the creation of art performance. Real-time rendering technology has a good development prospect and potential in 3D animation, which paves the way for designers to create 3D content more efficiently.

This thesis is a study of content development utilizing media outlets to date through digital humans. The trend of global content is that the video content industry, including the character business, is growing. Lil Michela, who was selected as one of the 25 most influential people on the Internet by Time magazine in 2018, Nasua, who appeared in a SK Telecom commercial, and Rosie, who appeared in a Shinhan Bank commercial, are representative. Digital humans, which are driving new content, are computer-generated human characters with various characteristics and are referred to as virtual humans, metahumans, and cyber humans. With the rise of the metaverse after COVID-19, digital humans are being utilized in various forms such as media and marketing as an element of visual content. In the form of media, we can see that the boundaries between the offline and digital worlds are converging, and in the form of marketing, we can see that digital humans connect consumers and products more naturally. In the form of interaction, it is possible to achieve two-way communication through various methods of operation, and through these factors, it is possible to go beyond behavioral communication in the form of memorialization to emotional communication through AI technology. What can be seen through these processes is that through the currently developing digital human production methods and AI functions, not only experts but also non-experts can create quality contents, and new directions of contents will appear, and contents that can provide immediate feedback by bringing consumers and creators closer together have been studied.

Having pet is one of the activities people living in modern society do to relieve stress and find peace of mind. Currently, the object of companion animals has moved beyond being a real 'living entity' and has developed to a stage where the animal's upbringing process can be enjoyed in a virtual space by being programmed in digital content. This paper studies detailed elements such as character design, interaction, and realism of 'Tamagotchi (1996)', which can be said to be the beginning of digital training content, and 'Peridot (2023)', a recently introduced augmented reality-based training content. The point was that it was training content using portable electronic devices. However, while the environment in the electronic device in which Tamagotchi's character exists was a simple black and white screen, the environment in which Peridot's character operates has been changed to the real world projected on the screen based on augmented reality. Mutual communication with characters in Tamagotchi remained a response to pressing buttons, but in Peridot, it has advanced to the point where you can pet the characters by touching the smartphone screen. In addition, through object and step recognition, it was confirmed that the sense of reality had become more realistic, with toys thrown by users on the screen bouncing off real objects. We hope that this research material will serve as a useful reference for the development of digital training content to be developed in the near future.

This paper examines how the design of female characters in Disney animations is evolving over time, and explores whether these changes are related to the social status of women in modern society. We analyze in detail how Disney's female character design has undergone changes in form, characteristics, and personality with the transition from 2D animation to 3D animation, and show that the change in perception of women in modern society is behind this change. It shows. It deals with changes in the design and personality of female characters, focusing on major Disney animation works before and after 2010. Starting with the movie <Rapunzel>, released in 2010, female characters showed stronger and more active characteristics and changed from traditional Disney princesses. Disney is bringing about this social change by breaking away from the image of an independent woman and showing the growth process of overcoming hardships based on one's abilities and the support of one's family, as well as the increasing number of female characters of various races and appearances. The conclusion was reached that it shows a conscious and active willingness to accept it.

The current rapid development of artificial intelligence technology has involved all aspects of the production field. The development of various algorithms and programs has pushed artificial intelligence to a new peak. Due to its complexity and diversity in the field of architectural design, the positive impact of artificial intelligence technology on architectural design is discussed from the perspective of conceptual design. For museums, which are one of the increasingly popular public facilities, the introduction of artificial intelligence technology has provided certain help in assisting the conceptual design of the museum. This article analyzes the theoretical and practical support of artificial intelligence technology in improving conceptual design, analyzing the architectural appearance, structural layout, materials, etc., to increase the feasibility and practicality of assisting conceptual design. It has certain reference significance for building a modern, advanced, international and interactive modern museum.

Breaking the fourth wall is a very popular concept right now, and depictions of breaking the barrier between virtuality and reality are often used in game advertising. In VR games, game manufacturers describe the experience after breaking the fourth wall as an experiencer who will be completely immersed in the virtual world, as if they are actually living in the virtual world. At the same time, research in the field of traditional drama also shows that breaking the fourth wall can also bring a sense of alienation to the player, allowing the experiencer to clearly realize that he and the character are in a completely different world, and to conduct aesthetic criticism of related works of art.So why there are two completely different feelings after breaking the fourth wall will be the content of this article. This article will focus on the theoretical analysis of the relationship between two different cognitions and two completely different cognitions after breaking the fourth wall. Finally, it will be analyzed from three directions: game perspective, game art style, and different world views of the game. Finally, it was concluded that when players break the fourth wall in the game, these three factors will cause the experiencer to have two completely different cognitions.

A research model was developed in this study from previous research to illustrate the relationship among perceived scarcity, assumed expensiveness of art products, perceived functional value, perceived symbolic value as well as purchase for self and others. 639 valid cases who have experienced NFT art products have been collected for data analyses. The R software has been chosen for analysis. The results have revealed that perceived scarcity was positively related to perceived functional value and perceived symbolic value, but it was assumed that the expensiveness of art products was not significantly related to perceived functional value or perceived symbolic value. Moreover, functional value was significantly related to the purchase intention of both self and others, while symbolic value only was significantly affected by the purchase intention of others. The results of this study have provided managerial implications for future studies.

The purpose of this study is to empirically grasp the relationship between website immersion and website attitude by the attribute factors of the Taekwondo information website and provide it as basic data for effective operation of the Taekwondo information website. The subjects of this study were Taekwondo athletes enrolled in high schools and universities affiliated with the Korean Taekwondo Association, and the sampling method was sampled using the convient sampling method, a non-probability sampling method. Of the 820 questionnaires finally obtained, 789 were processed using PASW Statistics 20.0 and AMOS, except for 31 that were deemed to have poor respondents' contents or were not valuable as data. For data analysis, the statistical analysis techniques used in this study were frequency analysis, factor analysis, Cronbach's α test, correlation analysis, and structural equation model analysis (SEM), and the significance level of the research hypothesis was α=.It was verified at 05. The following conclusions were drawn through such research methods and procedures. First, information, entertainment, structure, cognition, searchability, and connectivity of Taekwondo information website attributes affect website immersion. Second, website immersion is affecting website attitudes.

This paper explores notable shifts in the restaurant startup market following the lifting of social distancing measures. Key trends identified include an escalated interest in startups, a heightened focus on the quality and diversity of food, a relative decline in the importance of delivery services, and a growing interest in specific industry sectors. The study's data collection spanned three years, from April 2021 to May 2023, encompassing the period before and after social distancing. Data were sourced from a range of online platforms, including blogs, news sites, cafes, web documents, and intellectual forums, provided by Naver, Daum, and Google. From this collected data, the top 50 words were identified through a refinement process. The analysis was structured around the social distancing application period, comparing data from April 2021 to April 2022 with data from May 2022 to May 2023. These observed trend changes provide founders with valuable insights to seize new market opportunities and formulate effective startup strategies. In summary, We offer crucial insights for founders, enabling them to comprehend the evolving dynamics in food service startups and to adapt their strategies to the current market environment.

Achieving realistic visual quality while maintaining optimal real-time rendering performance is a major challenge in evolving computer graphics and interactive 3D applications. Normal mapping, as a core technology in 3D, has matured through continuous optimization and iteration. Hybrid normal mapping as a new mapping model has also made significant progress and has been applied in the 3D asset production pipeline. This study comprehensively explores the hybrid normal techniques, analyzing Linear Blending, Overlay Blending, Whiteout Blending, UDN Blending, and Reoriented Normal Mapping, and focuses on how the various hybrid normal techniques can be used to achieve rendering performance and visual fidelity. performance and visual fidelity. Under the consideration of computational efficiency, visual coherence, and adaptability in different 3D production scenes, we design comparative experiments to explore the optimal solutions of the hybrid normal techniques by analyzing and researching the code, the performance of different hybrid normal mapping in the engine, and analyzing and comparing the data. The purpose of the research and summary of the hybrid normal technology is to find out the most suitable choice for the mainstream workflow based on the objective reality. Provide an understanding of the hybrid normal mapping technique, so that practitioners can choose how to apply different hybrid normal techniques to the corresponding projects. The purpose of our research and summary of mixed normal technology is to find the most suitable choice for mainstream workflows based on objective reality. We summarized the hybrid normal mapping technology and experimentally obtained the advantages and disadvantages of different technologies, so that practitioners can choose to apply different hybrid normal mapping technologies to corresponding projects in a reasonable manner.

Previous research has demonstrated that happy moods are known to promote feeling-based processing, whereas sad moods promote reason-based processing. The current research investigates a boundary condition for the effects of a happy mood on feeling-based decision making. This research proposes that the level of control (low vs. high) one exercises in a happy situation can promote a greater reliance on feelings (vs. reasons) in making judgments and decisions. Specifically, we hypothesize that (1) a happy individual in a situation where control level is low (vs. high) will be more likely to choose a cognitively (vs. affectively) superior option (hypothesis 1), and (2) a happy individual in a situation where control level is low (vs. high) will exert reason- (vs. feeling-) based processing (hypothesis 2). Consistent with the hypothesis 1, the results of two experiments show that happy individuals are more likely to choose cognitively versus affectively superior options in a situation where control level is low (vs. high). Moreover, the mediation analysis confirms that happy individuals are more likely to rely on cognitive, reason-based decision making when their control level is low, which supports the hypothesis 2.

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

This study investigated changes in salivary cortisol, lactic acid, and heart rate along the route during walking exercise in a forest environment for the purpose of reducing stress. Walking exercise in a forest environment was conducted on a Hill Type (Distance: 800m, Average slope 25°, Altitude 112m) and Step Type (Distance: 800m, Average slope 25°, Altitude 114m) routes for 10 female college students in their 20s. The subjects were asked to walk at a speed of 60 bpm. The resulting changes in salivary cortisol, lactate, and average heart rate during exercise were compared and analyzed using Repeated Measurement two-way ANOVA, and the maximum heart rate during exercise and average heart rate at rest were compared and analyzed using paired t-test, and the following results were obtained. First, there was no significant difference in salivary cortisol depending on the type and period of the forest, but it tended to gradually decrease. Second, there was a significant difference in lactic acid depending on the type and period, and it was higher in Step Type. Third, there was a significant difference in the average heart rate during exercise, and it was higher in Step Type. Fourth, there was a significant difference in maximum heart rate during exercise, and it was higher in Step Type. Fifth, there was no significant difference in average heart rate during rest. In summary, walking exercise in a forest environment can be effective for stress reduction for female college students in their 20s, but it appears that forest routes should be selected according to physical strength level, and walking exercise in a forest environment for long periods of time is not recommended. For this purpose, it is suggested that it is appropriate to select the Hill Type route.

Ten women in their 20s and 10 women in their 50s were selected to investigate the effect of an elastic band exercise program for 8 weeks according to age on women's knee speed expression rate and balance. Knee isokinetic muscle strength measurement, single-legged standing with eyes closed, and YBT were performed 1 week before and after the exercise program. The measured data were analyzed through a mixed design two-way ANOVA, and if there was a significant difference, post hoc verification was performed using the bonferoni method. In our study, as a result of an 8-week elastic band exercise program, there was no difference in the speed development rate according to the measurement period, and the speed development rate according to age was found to be higher in people in their 50s than in their 20s. In our study, there was no difference in balance ability depending on the measurement period, and there was also no difference in balance ability depending on age. Discussing the results of our study, we found that 8 weeks of elastic band exercise cannot bring about significant changes in speed development ability and balance ability, which become more accurate with age.

The purpose of this study is to identify the level of safety perception, ethical awareness, and safety activities of nursing students for patients, and to identify the correlation and impact between them. The research design is a descriptive survey study, and the subject of the study were 197 nursing college students in G City. Safety perception, ethical awareness, and safety activity tools were used for, and the data collection period was from October 17 to 28 in 2022. T-test, one-way ANOVA, Pearson's correlation coefficient, Regression analysis were used to analyze data. The result of the study indicated that the average level of safety perception of nursing students was 3.72 points, the average ethical awareness of patients, professional work, and cooperators perceived by nursing students was 3.04 points, and the safety activities of nursing students were 4.20 points. In the case of safety awareness and ethics awareness, r=.327, a significant positive correlation, in the case of safety awareness and safety activities, r=.399, significant positive correlation, ethics awareness and safety activities as r=.296. And so on these results showed that high safety perception increases safety activities, and high ethical awareness increases safety activities. Therefore, we need practical and step-by-step convergence education to equip nursing students with patient safety nursing capabilities. To this end, a safer environment will be created if the social support network for the systematic application of safety education is well formed.

This study examined the importance of screening for changing bodybuilding and fitness category. The screening criteria for bodybuilding, the background and reason for the creation of new bodybuilding and fitness items, the screening criteria for new items, and the use of drugs were described. The current bodybuilding gives high marks to excessive muscles and excessive diet conditions, and new bodybuilding category have been newly established in line with the recent global trend of pursuing natural beauty over abnormally excessive muscles, and the screening criteria also prioritize the balance of ideal and overall muscles to fit your height and weight. In addition, fitness events such as physique and bikini are gaining popularity with the establishment because they focus on not excessive muscles and natural elements of the body that ordinary people can challenge. Since athletes as well as ordinary people are using drugs to increase muscles and suffer side effects, IFBB(International Federation of BodyBuilding) and KBBF(Korea Body Building Federation) should consider and improve the current bodybuilding screening standards that avoid excessive muscles, and it is believed that bodybuilding and fitness events will develop only when strict punishment and continuous anti-doping education are carried out.

The purpose of this study was to investigate the muscle activity of the lower extremity during driver swing in three-foot positions (Feet Open Stance, Feet Straight Stance, Lead Foot Open Stance). The electromyograms of gastrocnemius, tibialis anterior, and vastus lateralis during swing were measured and analyzed in three sections (take away - back swing, back swing - down swing, and down swing - follow swing). There was no significant difference in muscle activity according to foot position. Muscle activity according to phase was significantly higher in right gastrocnemius and tibialis anterior, and the left and right vastus lateralis in down swing - follow swing. In conclusion, the difference in muscle activity according to foot position is insignificant, and it is considered that the muscle activity to maintain the balance of the body increases toward the end of swing.

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

One of the basic tasks in the automobile manufacturing process is to design a bare chassis, which is the basic frame of a vehicle, and a bracket is a member connecting various devices to the frame. Bracket, which is a member connecting the engine, transmission, and suspension, which are the core devices of driving and operating the vehicle, to the frame, must maintain safety during vehicle operation. If the bracket connecting the various devices constituting the vehicle to the frame does not have durability, serious accidents may occur during operation of the vehicle. In this study, we performed stress analysis on the brackets installed in the bare chassis of the 25-passenger bus in the development stage. Based on the stress analysis performed, an improved bracket dimension was proposed.

Global warming is causing abnormal climates such as floods, droughts, and typhoons all over the world. According to some scientists, carbon dioxide emitted from automobiles is the main cause of global warming. To cope with this, each country is making efforts to replace the existing fossil fuel-powered engine-driven cars with electric vehicles. In order to commercialize small electric vehicles in Korea, it is necessary to solve many problems such as improvement of hill climbing capacity and improvement of power performance. In this study, we propose a proprietary model for a continuously variable transmission(CVT) of a small electric vehicle that can be operated on hills, in which a spring is mounted on a driving pulley and a driven pulley. A prototype of the CVT model using a spring was manufactured and attached to a small electric vehicle body.

This study presents a paper on the implementation of a Raspberry Pi-based educational smart farm system. It confirms that in a real smart farm environment, the control of temperature, humidity, soil moisture, and light intensity can be smoothly managed. It also includes remote monitoring and control of sensor information through a web service. Additionally, information about intruders collected by the Pi camera is transmitted to the administrator. Although the cost of existing smart farms varies depending on the location, material, and type of installation, it costs 400 million won for polytunnel and 1.5 billion won for glass greenhouses when constructing 0.5ha (1,500 pyeong) on average. Nevertheless, among the problems of smart farms, there are lax locks, malfunctions to automation, and errors in smart farm sensors (power problems, etc.). We believe that this study can protect crops at low cost if it is complementarily used to improve the security and reliability of expensive smart farms. The cost of using this study is about 100,000 won, so it can be used inexpensively even when applied to the area. In addition, in the case of plant cultivators, cultivators with remote control functions are sold for more than 1 million won, so they can be used as low-cost plant cultivators.

The government has set the mechanization of paddy agriculture as a national task, aiming to achieve over 70% by 2025. The main objective is to stabilize the farming costs of rural households due to the aging and feminization of rural areas, as well as the shortage of agricultural labor. In response to this, the Korea Rural Economic Institute operates a farm machinery rental business. However, there are challenges in selecting and managing rental machinery, including issues related to labor, costs, verification, and time. Additionally, there is a limit to upgrades, and overseas models are being imported and used for transplanters and rice planters, which do not conform to domestic standards and face maintenance difficulties. In order to solve the difficulties of the agricultural machine rental business, we intend to develop an application that shares domestic and foreign machines purchased and used by individuals at a low cost and use them in gun-level administrative districts.