A public address (PA) system installed in a building is a system that delivers alerts, announcements, instructions, etc. in an emergency or disaster situation. As for the products used in PA systems, with the development of information and communication technology, PA products with various functions have been introduced to the market. PA systems recently launched in the market may be connected through a single network to enable efficient management and operation, or use voice recognition technology to deliver quick information in case of an emergency. In addition, a system capable of locating a user inside a building using a location-based service and guiding or responding to a safe area in the event of an emergency is being launched on the market. However, the new PA systems currently on the market add some functions to the existing PA system configuration to make system operation more convenient, but they do not change the complex PA system configuration to reduce facility costs, maintenance, and management costs. In this paper, we propose a novel PA system configuration for buildings using audio networks and control hierarchy over peer-to-peer (Anchor) technology based on audio over IP (AoIP), which simplifies the complex PA system configuration and enables convenient operation and management. As a result of the study, through the emergency signal processing algorithm, fire broadcasting was made possible according to the detection of the existence of a fire signal in the Anchor system. In addition, the control device of the PA system was replaced with software to reduce the equipment installation cost, and the PA system configuration was simplified. In the future, it is expected that the PA system using Anchor technology will become the standard for PA facilities.

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

Recently, fifth generation (5G) networks are being deployed in phases all over the world, the paradigm has shifted to developing the next generation wireless technologies, which have grown exponentially in last few decades, wireless networks are promising for the demand to enormous connections. Non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) are considered as the key technoloies for next-generation beyond 5G (B5G) and sixth generation (6G) networks, in which IRS can play an important advance in the wireless propagation environment, and NOMA can effectively increase massive connectivity to improve user fairness. In this paper, we analyze a performance on the strongest channel user in terms of achievable data rates numerically. Then, with the achievable data rates, the signal-to-noise ratio (SNR) gain is calculated for the IRS-NOMA network over the conventional NOMA network. As a consequence, IRS-NOMA schemes have been considered as some key technologies.

This paper designs a real-time video playback system using a web camera in the RTSP server. It designs a function to play the video data of the web camera in the client in real time using the web camera in the server and using the RTSP protocol. It consists of a server module function that produces real-time video information using a web camera and a client module function that plays video received from the server in real time. The experiment was conducted by establishing an environment for designing a real-time video playback system using a web camera. As a result of the experiment, it was confirmed that real-time video playback from the server's web camera worked well.

This paper provides a comprehensive overview of UAV classification, tracking, and detection, offering researchers a clear understanding of these fundamental concepts. It elucidates how classification categorizes UAVs based on attributes, how tracking monitors real-time positions, and how detection identifies UAV presence. The interconnectedness of these aspects is highlighted, with detection enhancing tracking and classification aiding in anomaly identification. Moreover, the paper emphasizes the relevance of simulations in the context of drones and UAVs, underscoring their pivotal role in training, testing, and research. By succinctly presenting these core concepts and their practical implications, the paper equips researchers with a solid foundation to comprehend and explore the complexities of UAV operations and the role of simulations in advancing this dynamic field.

Blockchain is a technology designed to prevent tampering with digital documents or information, safeguarding transaction data and managing it in a structured manner. This proves beneficial in addressing issues of trust and data protection in B2B, B2C, and C2B transactions. Blockchain finds utility not only in financial transactions but also across diverse industrial sectors. This study outlines significant cases and responses that jeopardize the security of blockchain networks and cryptocurrency technology. Additionally, it analyzes safety and risk factors related to blockchain and proposes effective testing methods to preemptively counter these challenges. Furthermore, this study presents key security evaluation metrics for blockchain to ensure a balanced assessment. Additionally, it provides evaluation methods and various test case models for validating the security of blockchain and cryptocurrency transaction services, making them easily applicable to the testing process.

This study focuses on the design of a GPT-based system for relatively rapid technology credit assessment of SMEs. This system addresses the limitations of traditional time-consuming evaluation methods and proposes a GPT-based model to comprehensively evaluate the technological capabilities of SMEs. This model fine-tunes the GPT model to perform fast technical credit assessment on SME-specific text data. Also, It presents a system that automates technical credit evaluation of SMEs using GPT and LLM-based chatbot technology. This system relatively shortens the time required for technology credit evaluation of small and medium-sized enterprises compared to existing methods. This model quickly assesses the reliability of the technology in terms of usability of the base model.

Gait analysis plays a key role in the research field of exploring and understanding human movement. By quantitatively analyzing the complexity of human movement and the various factors that influence it, it is possible to identify individual gait characteristics and abnormalities. This is especially true for people with walking difficulties or special circumstances, such as amputee, for example. This is because it can help us understand their gait characteristics and provide individualized rehabilitation plans. In this paper, we compare and analyze the differences in ankle joint motion and angles between normal and amputee. In particular, a filtering process was applied to the ankle joint angle data to obtain high accuracy results. The results of this study can contribute to a more accurate understanding and improvement of the gait patterns of normal and amputee.

The food industry has been hit hard since the first outbreak of COVID-19 in 2019. However, as of April 2022, social distancing has been resolved and the restaurant industry has gradually recovered, interest in restaurant start-ups is increasing. Therefore, in this paper, 'restaurant start-up' was cited as a key keyword through social media big data analysis using TexTom, and word frequency and cone analysis were conducted for big data analysis. The keyword collection period was selected from May 1, 2022, when social distancing due to COVID-19 was lifted, to May 23, 2023, and based on this, a plan to develop chatbot services for restaurant start-ups was proposed. This paper was prepared in consideration of what to consider when starting a restaurant and a chatbot service that allows prospective restaurant founders to receive information more conveniently. Based on these analysis results, we expected to contribute to the process of developing chatbots for prospective restaurant founders in the future

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

This study attempted to identify the experiences of nursing students' participation in cadaver practice and provide a basis for understanding students' experiences in cadaver practice, which can be reflected in the development of programs for them. By applying the content analysis method according to the guidelines by Krippendorff to analyze the meaning of the experience of participating in cadaver practice among 80 nursing students who participated in cadaver practice at K University in W-si, Gangwon-do, a total of 4 areas, 13 categories, and 25 meaningful statements were derived. The categories included "worried," "surprised," and "fear" in the anxiety domain; "interesting," "knowing," and "focused" in the immersion domain; "value of life," "gratitude and remembrance," "thinking about donation," "facing death," and "precious body" in the reflection domain; and "motivation" and "sense of accomplishment" in the growth domain. The results of this study will help to understand the physical and psychological reactions that nursing students may experience during cadaver practice, and will provide a basis for developing various strategies such as counseling, education, and reflection programs in conjunction with cadaver practice to help nursing students cope with stress, develop a sense of ethical responsibility, and develop a positive self-image as nursing students in order to be successful in cadaver practice. This study is also significant because it provides a basis for preventive program interventions for experiences related to the negative effects of cadaver practice.

This study explores the convergence of metaverse and short-form content, proposing a new approach for viewing short-form content with a vertical aspect ratio on a metaverse platform and within a virtual museum. Short-form content has gained popularity due to the snack culture and is proving to be advantageous in e-commerce. By studying the relationship between vertical ratio screens and gaze, we confirm the effectiveness of vertical ratio short-form content in providing immersive experiences and fostering sharing and communication. The proposed virtual museum offers opportunities for innovative businesses to market through fandom. This research highlights the value of vertical proportion short-form content and its significance in the convergence of metaverse and short-form content. It aims to contribute to industry development, provide new creative directions, enhance personal visual experiences, and expand applicability in related fields.

While tools exist for blind people to understand shapes, these are not commercially available nor affordable and often require the assistance of sighted people. Thus, we designed two low-cost grid-based tactile tools using toggle buttons (TogGrid) and cotton balls (CottonGrid). To assess the potential of these as an educational tool, we conducted a user study with 12 people with visual impairments where they were asked to understand and reproduce shapes under different conditions. Although CottonGrid is relatively cheap and easy to make, findings show that TogGrid was perceived to be better in terms of perceived easiness, task completion time, accuracy, and preference in general. Particularly, participants valued TogGrid for enabling them to identify and correct errors. Based on the findings, we provide implications for utilizing toggle buttons for designing educational instruments for learning and expressing shapes for blind people.

As the direction of education in the fourth industry in the 21st century, convergence talent education that emphasizes the connection and convergence between core competency-based education and academia is emerging to foster creative talent. The purpose of this paper is to present the criteria for evaluating the competency of learning outcomes in order to develop a strategic model for innovation in engineering teaching-learning. In this paper, as a study to establish the direction of implementation of convergence talent education, a creative innovation teaching method support system was established to improve the quality of convergence talent education. Firstly, a plan to develop a teaching-learning model based on computing thinking. Secondly, it presented the development of a teaching-learning model based on linkage and convergence learning. Thirdly, we would like to present educational appropriateness and ease based on convergence learning in connection with curriculum improvement strategies based on computing thinking skills. Finally, we would like to present a strategic model development plan for innovation in engineering teaching-learning that applies the convergence talent education program.

Joint research on software, electronic engineering, computer engineering, and financial engineering and the use of ICT knowledge through network formation play an important role in strengthening science and technology-based innovation capabilities and facilitating the development and production process of products using new technologies. For the purpose of this study, I would like to strategically propose ICT specialized education in the 4th industrial revolution. To this end, the ICT specialization model, ICT specialization strategy analysis, and ICT specialization operation and effect were explored to establish ICT specialization strategies centered on software, electronic engineering, computer engineering, and financial engineering in the era of super-intelligence, hyper-connected, and hyper-convergence. Secondly, a roadmap for detailed promotion tasks related to efficient ICT characterization based on core strategies, detailed promotion tasks, and programs was proposed, focusing on talent related to ICT characterization. Thirdly, we would like to propose a reorganization of the academic structure and organization related to ICT characterization. Finally, we would like to propose the establishment of a future-oriented education system related to ICT specialization based on the advanced education and research environment.

This study analyzed the relationship between ESG performance and corporate value using panel data from Chinese equipment manufacturing companies spanning from 2012 to 2021, and it also examined whether ownership structure moderates this relationship. We have contributed to filling the gap in existing research. The main conclusions of this study are as follows: Firstly, similar to previous researches, ESG performance was found to have a positive and statistically significant impact on corporate value. Secondly, when the three dimensions of ESG - Environmental (E), Social (S), and Governance (G) - were analyzed separately, it was observed that E and S have a positive and statistically significant impact on corporate value, while G has a negative and statistically significant impact. Thirdly, ownership concentration emerged as a significant moderating factor in explaining the connection between ESG performance and corporate value. Lastly, when the three dimensions of ESG were analyzed separately, ownership concentration was found to serve as a positive moderating factor in the relationship between corporate value and E and S, but it did not play a statistically significant role for G.

The restaurant industry is an industry with low entry barriers, and furthermore, it is an indispensable industry in life. However, for the restaurant industry, it is necessary to start a business considering many factors. In particular, the comparative group for each restaurant industry is different, and the commercial area analysis should be analyzed differently. Moreover, counseling for restaurant start-ups is still sticking to how to start a restaurant by meeting with each franchise supervisor or counselor. Therefore, a restaurant start-up chatbot is needed for prospective restaurant founders, and a food tech chatbot is needed to collect basic data. Therefore, in this study, factors for restaurant start-ups were divided into youth, preliminary start-ups, menus, taste, and food. In the case of restaurant start-ups with low entry barriers, it was confirmed as the most preferred start-up by young people. However, indiscriminate restaurant start-ups not only increase the closing rate but also have a significant impact on household debt, so accurate consulting should be used to lower the closing rate and increase the success rate. Furthermore, theories and measures for food technologies such as chatbots should be further developed to obtain accurate information on franchise start-ups.

The purpose of this paper is to analyze entrepreneurship and to find out the impact of CEOs in the restaurant industry on corporate performance when they have entrepreneurship. Entrepreneurs need entrepreneurship to take risks and jump into the market to generate profits. Entrepreneurship is not limited to the abilities or resources held, but it is not limited to the ability or resources held, and entrepreneurship to act means the spirit to take uncertainty and preempt opportunities through innovative activities [1]. In this study, the CEO's entrepreneurship was set as an independent variable and corporate performance as a dependent variable. By applying and analyzing how the CEO's entrepreneurship affects corporate performance in the restaurant industry, the importance of entrepreneurship in the restaurant industry and the impact relationship on corporate performance are analyzed. To this end, 100 CEOs working in the restaurant industry will be surveyed using the Likert 5-point scale[2]. And an empirical analysis will be conducted through the SPSS program[3]. Entrepreneurship is a spirit that can take risks and seize opportunities through bold challenges to generate profits. Therefore, it has been confirmed that it affects corporate performance as a key factor for improving corporate performance, and from related studies, the entrepreneurship of the CEO of the restaurant industry is expected to have a positive (+) effect on corporate performance.

As the era of ChatGPT and generative AI technologies unfolds, the marketing industry stands on the precipice of a paradigm shift. Innovations such as GPT-4, DALL-E 2, and Mid-journey Stable Diffusion possess the capacity to dramatically transform the methods by which advertisers reach and engage with customers. The potential applications of these advanced tools herald a new age for the marketing and advertising sectors, offering unprecedented opportunities for growth and optimization. Nevertheless, the rapid adoption of generative AI within these industries presents a unique set of challenges, particularly for organizations that lack the necessary technological infrastructure and human capital to effectively leverage these innovations. As a result, a competitive crisis may emerge, exacerbating existing disparities between well-equipped enterprises and their less technologically adept counterparts. In this article, we undertake a comprehensive exploration of the implications of generative AI for the future of marketing, examining both its potential benefits and drawbacks. We consider the possible impact of these developments on the advertising and marketing industries at large, as well as the ways in which professionals operating within these fields may need to adapt to remain competitive in an increasingly AI-driven landscape. By providing a holistic overview of the challenges and opportunities associated with generative AI, this study aims to elucidate the complex dynamics at play in the ongoing evolution of the marketing and advertising sectors.

This study would investigate the generative AIs currently in service in the era of hyperscale AIs and explore measures for the use of generative AIs, focusing on 'ChatGPT,' which has received attention as a leader of generative AIs. Among the various generative AIs, this study selected ChatGPT, which has rich application cases to conduct research, investigation, and use. This study investigated the concept, learning principle, and features of ChatGPT, identified the algorithm of conversational AI as one of the specific cases and checked how it is used. In addition, by comparing various cases of the application of conversational AIs such as Google's Bard and MS's NewBing, this study sought efficient ways to utilize them through the collected cases and conducted research on the limitations of conversational AI and precautions for its use. If connected to city-related databases, it can provide information on city infrastructure, transportation systems, and public services, so residents can easily get the information they need. We want to apply this research to enrich the lives of our citizens.

The purpose of this study was to investigate the structural relationship between eating habits, physical activity, and subjective health perception, which can affect the mental health status of adolescents, and to examine whether subjective health perception has a mediating effect in these relationships. In this study, raw data from the "17th 2021 Youth Health Behavior Online Survey" were used, and a total of 1,998 people were used for the analysis of Gangwon-do adolescents, except for data with missing values. For analysis, SPSS 25.0 and AMOS 25.0 programs were used to analyze descriptive statistics, t-test, and structural equation models(SEM). Physical activity was found to have a positive and significant effect on mental health status, and subjective health cognition showed the effect of physical activity mediating mental health status.

Until now, it is popular to use question-and-answer-based for human personality. The current inspection of representative personality types includes Myers-Briggs Type Indicator (MBTI) and job suitability evaluations. The problem of these inspection methods is influenced by the user's environment and psychological status during MBTI inspection. To solve this problem, we proposed MBTI Identification Model based on measuring bioelectricity patterns. We adapt traditional Korean medicine, the Eight Constitution, to this model. We develop an automatic MBTI identification algorithm that maps the Eight Constitution via biological current patterns to identify MBTI personality types. By utilizing the algorithm proposed in this research, it is anticipated that users will be able to measure MBTI more easily and accurately.

Surface electromyography (sEMG) is a noninvasive method used to capture electrically muscle activity, which can be easily measured even during exercise. The basic unit of muscle activity is the motor unit, and because an sEMG signal is a superposition of motor unit action potentials, analysis of muscle activity using sEMG should ideally be done from the perspective of motor unit activity. However, conventional techniques can only evaluate sEMG signals based on abstract signal features, such as root-mean-square (RMS) and mean-power-frequency (MPF), and cannot detect individual motor unit activities from an sEMG signal. On the other hand, needle EMG can only capture the activity of a few local motor units, making it extremely difficult to grasp the activity of the entire muscle. Therefore, in this study, a method to visualize the activities of motor units in a single-channel sEMG signal by relocating wavelet coefficients obtained by redundant discrete wavelet analysis is proposed. The information obtained through this method resides in between the information obtained through needle EMG and the information obtained through sEMG using conventional techniques.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

In modern society, since the expansion of the e-commerce market and the spread of the pandemic, face-to-face business are gradually changing to non-face-to-face. In the logistics industry, the demand for unmanned courier storage is increasing due to lack of loading space from urbanization and courier theft accidents. As the demand for unmanned parcel lockers increases, improved functions such as food storage and efficient space loading are required. This study develops an integrated model-based evaluation procedure of product based on performance factors according to the IEC 61508 standard for newly unmanned parcel storage devices with active loading technology, and derive Critical Technology Element based on the product's core functions and performance goals. As proposing these research, We expect improve the safety and reliability of development targets by identifying and evaluating elements.