• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2772-2786
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• 2022

We had researched an automatic authentication-supported medical information platform[6]. The proposed automatic authentication consists of user authentication and mobile terminal authentication, and the authentications are performed simultaneously in patients' emergency conditions. In this paper, we studied on finding emergency conditions for the automatic authentication by applying big data processing and AI mechanism on the extended medical information platform with an added edge computing system. We used big data processing, SVM, and 1-Dimension CNN of AI mechanism to find emergency conditions as authentication means considering patients' underlying diseases such as hypertension, diabetes mellitus, and arrhythmia. To quickly determine a patient's emergency conditions, we placed edge computing at the end of the platform. The medical information server derives patients' emergency conditions decision values using big data processing and AI mechanism and transmits the values to an edge node. If the edge node determines the patient emergency conditions, the edge node notifies the emergency conditions to the medical information server. The medical server transmits an emergency message to the patient's charge medical staff. The medical staff performs the automatic authentication using a mobile terminal. After the automatic authentication is completed, the medical staff can access the patient's upper medical information that was not seen in the normal condition.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.231-243
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• 2024

As generative artificial intelligence (AI) services become more diversified and widely used, attempts and discussions on their application in education have become active. The purpose of this study is to investigate and analyze general and work-related perceptions, utilization, and needs regarding generative AI in elementary and secondary education. A survey was conducted among teachers and staff in Chungcheongbuk-do, and 934 responses were analyzed. The main research results are as follows: First, their work-related use of generative AI was lower than their general use, and considering the periodic frequency of more than once a month, the rate was much lower. Second, the main expectation when using generative AI in work appears to be improved work efficiency. Third, regarding the use of generative AI for each task, differences in perception of its usefulness were noticeable depending on position and occupation. They generally responded positively to the usefulness of generative AI in processing documents. To facilitate the use of generative AI for work by elementary and secondary teachers and staff, it is necessary to create an environment that promotes its use while ensuring safety against potential side effects. Additionally, requirements and needs should be considered depending on the position and occupation.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.513-521
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• 2022

This study deals with an AI architecture model for collecting battlefield data using the tactical C4I system. Based on this model, the artificial staff can be utilized in tactical echelon. In the current structure of the Army's tactical C4I system, Servers are operated by brigade level and above and divided into an active and a standby server. In this C4I system structure, the AI server must also be installed in each unit and must be switched when the C4I server is switched. The tactical C4I system operates a server(DB) for each unit, so data matching is partially delayed or some data is not matched in the inter-working process between servers. To solve these issues, this study presents an operation concept so that all of alternate server can be integrated based on virtualization technology, which is used as an source data for AI Meta DB. In doing so, this study can provide criteria for the AI architectural model of the ground tactical echelon.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.357-366
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• 2022

Artificial intelligence (AI) is the replication of human intelligence by computer systems and machines using tools like machine learning, deep learning, expert systems, and natural language processing. AI can be applied in administrative settings to automate repetitive processes, analyze and forecast data, foster social communication skills among staff, reduce costs, and boost overall operational effectiveness. In order to understand how AI is being used for administrative duties in various organizations, this paper gives a critical dialogue on the topic and proposed a framework for using artificial intelligence in organizations. Additionally, it offers a list of specifications, attributes, and requirements that organizations planning to use AI should consider.

• Convergence Security Journal
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• v.23 no.4
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• pp.122-129
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• 2023

The paradigm of warfare is undergoing a revolutionary transformation due to the advancements in technology brought forth by the Fourth Industrial Revolution. Specifically, the U.S. military has introduced the concept of mosaic warfare as a means of military innovation, aiming to integrate diverse resources and capabilities, including various weapons, platforms, information systems, and artificial intelligence. This integration enhances the ability to conduct agile operations and respond effectively to dynamic situations. The incorporation of mosaic warfare could facilitate efficient and rapid command and control by integrating AI staff with human commanders. Ukrainian military operations have already employed mosaic warfare in response to Russian aggression. This paper focuses on the mosaic war fare concept, which is being proposed as a model for future warfare, and suggests the strategy for introducing the Korean mosaic warfare concept in light of the changing battlefield paradigm.

• Journal of Korean Critical Care Nursing
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• v.12 no.3
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• pp.13-23
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• 2019

Purpose : The purpose of this study was to determine whether appreciative inquiry (AI) is an effective intervention for increasing the positive psychological capital and organizational commitment of new nurses. Method : The study used a nonequivalent control group pretest-posttest design. The participants were 60 new nurses in a tertiary hospital in Seoul. The experimental group received 2 classes of AI education and in-unit AI activities. The control group received the existing education program. Results : There was no statistically significant difference in the positive psychological capital and organizational commitment between the experimental group and the control group over time. Satisfaction with the AI education scored 3.69, which was higher than the average. The reason why the experimental group members were satisfied with the program was that AI education helped them to adapt and the in-unit AI activities made staff more cooperative and the atmosphere of the unit more positive. Conclusion : When applying AI activities to new nurses to promote positive psychological capital and organizational commitment, it is necessary to provide a workshop in which the participants can fully concentrate on education and to extend the period of use to one year in order to maintain the effect of AI activities.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Korean Journal of Applied Biomechanics
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• v.32 no.3
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• pp.69-79
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• 2022

Objective: The purpose of this study is to analyze trends related to sports and artificial intelligence (AI) to understand the trends and how they change according to time, and to establish methods to apply AI in sports. Both macro and micro perspectives related to sports utilization of AI were analyzed. Method: In this study, after analyzing and discussing various information related to the use of artificial intelligence in the sports through a search of academic journals, papers, books, and websites published recently at nationally and internationally, the application plan of artificial intelligence in the sports field was presented. Results: 1) Motion analysis technology using artificial intelligence is effective in sports where posture is important, and if it provides systematic feedback and training methods, it can help improve performance. 2) The introduction of a sports referee judgment system using artificial intelligence is expected to improve performance by restoring factual judgment and objective fairness in sports games. 3) Artificial intelligence will provide coaching staff and players with a variety of information to help improve performance through systematic coaching and improving feedback and enhanced training methods. 4) It is judged that artificial intelligence-related to sports ethics, sports ICT, sports marketing, sports prediction, etc. We think that based on the current AI research trends will have a positive impact on all sports-related areas, helping to revitalize sports. Conclusion: Motion analysis technology using artificial intelligence, sports referee judgment system, coaching using artificial intelligence, and artificial intelligence are judged to have a positive effect on all sports-related areas and help revitalize sports.