• International conference on construction engineering and project management
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• 2024.07a
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• pp.1145-1152
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• 2024

Digital transformation has become a pivotal focus in the Architecture, Engineering, and Construction (AEC) industry, driven by an urgent need to enhance productivity and optimize resource management. This transformation plays an essential role throughout the entire project lifecycle, from the early stages of conception to the final phases of completion. The paper underscores the critical importance of aligning digital transformation initiatives with the broader business strategies of AEC organizations. This alignment is key to gaining a competitive edge and fostering sustainable growth within the industry. The paper introduces a comprehensive and adaptable strategic framework for digital transformation. This framework is designed to be flexible, allowing AEC organizations to tailor digital transformation strategies to meet their specific needs and objectives. The framework not only addresses the technological aspects but also considers the cultural and operational shifts required for successful implementation. Moreover, the paper delves into various aspects of digital transformation, such as data management, workflow automation, and the integration of emerging technologies like AI and IoT in AEC processes. It discusses the potential barriers to digital adoption and offers strategies to overcome these challenges. This paper serves as an in-depth guide for AEC organizations looking to seamlessly integrate digital technologies into their business models. It provides valuable insights and methodologies that are crucial for any entity in the AEC industry striving to thrive in an increasingly digitalized world, making it a must-read for leaders and decision-makers within the industry.

• Korean Journal of Materials Research
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• v.7 no.7
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• pp.571-575
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• 1997

급냉온도에 따른 전기 저항 측정으로 Cu-17, 25Zn-15AI 및 Cu-17.25Zn-15AI-1Ag형상기억합금의 열처리에 의한 마르텐사이트 변태온도의 영향을 연구하였다. DSC 측정으로 고온 모상에서의 상전이 온도롸 종류를 구별하였고 XRD측정으로 구조 변화를 연구하였다. 그리고 열처리에 의한 온도 변화의 원인을 연구하였17.25Zn-15AI 합금에서 고온 모상의 규칙-불규칙 전이온도인 $T_{2}$, $T_{L21}$은 각각 809K와 610K였다. CuZnAI의 경우 $T_{2}$근방에서의 급냉은 마르텐사이트 변태온도를 높이지만 $T_{L21}$ 근방에서의 급냉은 마르텐사이트 변태온도를 낮춘다. 실험결과 열처리에 따른 상전이온도 변화의 원인은 석출물의 형성이라기 보다는 급냉전의 모상의 구조에 가장 큰 영향을 받는다.받는다.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.295-301
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• 2021

This study tried to examine the necessity of AI literacy education to increase the effectiveness of artificial intelligence education ahead of the revision of the next revised curriculum. To this end, AI modeling classes were conducted for high school students and the necessity, content, and training period of AI literacy perceived by students in AI education were investigated through a questionnaire. The results showed that they generally agreed on the need for data utilization and data preprocessing in the AI class, and in the course of the AI class, there were many cases of difficulties due to lack of basic competencies for database use. In particular, it was observed that the understanding of the file structure for data analysis was insufficient and the understanding of the data storage format for data analysis was low. In order to overcome this part, the necessity of prior education for data processing was recognized, and there were many opinions that it is generally appropriate to go to high school at that time. As for the content elements of AI literacy, it was found that there were high demands on the content of data visualization along with data transformation, including data creation and deletion.

• Fourth Industrial Review
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• v.4 no.1
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• pp.19-27
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• 2024

Purpose: This study is based on the results of the National Information Society Agency's the Report on the Digital Divide in 2022. This study sought to develop digital literacy education programs for senior citizens, a digitally disadvantaged group, and to utilize smart devices to enhance their digital capabilities. Research design, data and methodology: Based on Gagné's nine events of instruction, a total of 7-session educational programs using smart devices were developed, and teaching-learning goals were set at a level that older learners can realistically perform. In preparation for the era of digital transformation, AI utilization methods are introduced and utilized in some sessions of the educational program. Results: Among a total of 7 sessions of the educational program, 5 sessions using KakaoTalk and Naver App, and 2 sessions using other apps were developed. There are a total of three sessions using AI. Conclusions: This study presented a digital literacy education program that combined AI, addressing the insufficiency of AI-based education programs targeting senior citizens. It is expected that this educational program will be able to improve the digital literacy skills and provide a basis for fulfilling their responsibilities as digital citizens by suggesting a direction for AI utilization education for senior citizens.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• The Korean Journal of Food & Health Convergence
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• v.9 no.4
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.273-278
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• 2024

The meta-universe, as an innovative medium of digital technology that integrates the virtual and real worlds, is revolutionizing the traditional K-POP industry by leveraging advanced technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and motion capture. This transformation is gradually reshaping the entire entertainment sector. As K-POP continues its global expansion, the industry is actively exploring the application of virtual technologies, presenting viewers with a more diverse range of entertainment content. This thesis reviews the development history of virtual technology in K-POP, analyzes the practical applications of VR, AR, AI, and motion capture within the industry, and examines how these technologies enhance artist-fan interactions and immersion. The study demonstrates that the incorporation of virtual technology not only overcomes the limitations of traditional entertainment modes but also provides new directions for the future development of the K-POP industry.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.199-226
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• 2024

In response to the era of transformation necessitating the introduction of Artificial Intelligence (AI) and digital technologies, educational innovation is undertaken with the implementation of AI digital textbooks in Mathematics, English, and Information subjects by 2025 in Korea. Within this context, this study analyzed the perceptions and needs of elementary school teachers regarding mathematics AI digital textbook. Based on a survey conducted in November 2023, involving 132 elementary school teachers across the country, the analysis revealed that the majority of elementary school teachers had a low perception of the introduction and need for mathematics AI digital textbooks. However, some recognized the potential for personalized learning and effective teaching support. Furthermore, among the core technologies of the AI digital textbook, teachers highly valued the necessity of learning diagnostics and teacher reconfiguration functions and had the most positive perception of their usefulness in math lessons, while their perception of interactivity was relatively low. These findings suggest the need for changing teachers' perceptions through professional development and information provision to ensure the successful adoption and use of mathematics AI digital textbooks. Specifically, providing concrete and practical ways to use the AI digital textbook, exploring alternatives to digital overload, and continuing development and research on core technologies.