• Educational Technology International
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• v.24 no.2
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• pp.143-168
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• 2023

This study aims to analyze the perceptions and educational needs of pre-service teachers for the use of Artificial Intelligence (AI) in education. To this end, we collected survey data from 25 undergraduate students who were enrolled in a teacher education college in Seoul. The purpose of the survey was to measure the importance and current performance for instructional AI use based on the technological, pedagogical, and content knowledge (TPACK) framework, and to explore the priority of educational needs using Borich's needs analysis and the Locus for Focus model. The results of the study confirmed that Ethics and TPK competencies are prioritized. Additionally, the results indicated a high demand for practical knowledge that can be implemented in the practice of education. Based on the results, it is necessary to develop a teacher education program that focuses on ethical aspects and teaching strategy competencies in AI-based education.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.229-237
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• 2023

In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.

• Smart Media Journal
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• v.12 no.2
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• pp.42-55
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• 2023

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2361-2368
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• 2009

Recently AI(Artificial Intelligence) is one of the issues in the on-line game, a research that a game character seems to be realistic and is progressing using AI technique. Especially NPC is an important part of the AI researches of on-line game, and it is concerned by a game player and an architect. We proposed an intelligent agent framework to implement the NPC technique after studying the NPC technique using context awareness that reacts to the PC(Player Character) actively. Also, it can be developed gradually, and apply to various application because it has the capability to of adding an agent or deleting an agent easily.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.251-252
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• 2023

Compared to other industries the construction industry experiences more casualties and property damage due to safety accidents. One of the reasons is the increasing number of foreign workers. For this reason, past studies have found that foreign workers at construction sites are more exposed to safety accidents than non-foreign workers. Nevertheless the proportion of foreign workers involved in safety accidents at construction sites is increasing, and there has been a lack of research to predict the risk of safety accidents at construction sites. Additionally, realistic safety management is lacking due to a lack of safety accident risk prediction research. Therefore, in this study, we would like to propose basic research that proposes an AI-based safety accident prediction model framework for predicting safety accidents of foreign workers at construction sites. The framework and results of this study will contribute to reducing and preventing the risk of safety accidents for foreign workers through risk prediction for safety management of foreign workers at construction sites.

• Industry Promotion Research
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• v.9 no.3
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• pp.101-117
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• 2024

This study empirically analyzed the factors affecting the intention to utilize manufacturing AI in SM-sized manufacturers by applying the TOE framework. Independent variables that are expected to influence were applied, focusing on TOE factors and managerial characteristics that reflect the characteristics of SME manufacturers. In addition, the mediating effect of technology trust and the moderating effect of factory location were analyzed. The results are as follows. First, the relationship between the independent variables and the dependent variable was tested, and the direct effects of the independent variables(complexity, organizational innovation, IT ability, competitive pressure, partner support, and managerial innovation) on the dependent variable were all statistically significant, except for compatibility. Second, the mediation effect of technology trustness was verified to have a full mediation effect between compatibility and utilization intention, and a partial mediation effect between managerial innovation and utilization intention. Third, among the seven independent variables, the moderating effect of factory location(metropolitan and non-metro) between the three independent variables of IT ability, competitive pressure, and partner support and the utilization intention was found to be significant. To increase the intention to utilize manufacturing AI for SM-sized manufacturers, it is recommended that more diverse and broader studies are needed, not only the factors identified in this study, but also the understanding and awareness of manufacturing AI.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1403-1413
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• 2021

In this paper, we investigate automated data validation techniques for artificial intelligence training, and also disclose open-source frameworks, such as Google's TensorFlow Data Validation (TFDV), that support automated data validation in the AI model development process. We also introduce an experimental study using public data sets to demonstrate the effectiveness of the open-source data validation framework. In particular, we presents experimental results of the data validation functions for schema testing and discuss the limitations of the current open-source frameworks for semantic data. Last, we introduce the latest studies for the semantic data validation using machine learning techniques.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.233-234
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• 2021

본 논문에서는 Microsoft Bot Framework와 Microsoft Azure Service, LUIS AI를 활용하여 쇼핑몰 이용에 도움을 주는 쇼핑 챗봇을 설계하고 구현한다. 이 챗봇은 쇼핑몰을 이용하는 사용자들에게 대화형 인터페이스를 통한 편의성을 제공하고 접근성을 증가시킨다. 또한 직접 찾는 방식이 아닌 AI의 선택이 중심이 되어 검색 시간 감소로 인한 시간 절약 효과를 얻을 수 있다.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.388-402
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• 2021

This study utilizes the theoretical framework of Extended Technology Acceptance Model to understand the governmental factors that affect the people's intention to use AI services. With the result of the analysis, as the expected impact of AI on fields related to effectiveness and accountability becomes higher, the intention of using AI service also got higher. In addition, the easier usability of e-government, the more active disclosure of their personal information, and the higher expectations for a hyper-connected society, their intention to use AI services became higher as well.