• International conference on construction engineering and project management
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• 2022.06a
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• pp.57-66
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• 2022

Claims and disputes are major causes of cost and schedule overruns in the construction business. In order to manage claims and disputes effectively, it is necessary to analyze various types of contract documents punctually and accurately. Since volume of such documents is so vast, analyzing them in a timely manner is practically very challenging. Recently developed approaches such as artificial intelligence (AI), machine learning algorithms, and natural language processing (NLP) have been applied to various topics in the field of construction contract and claim management. Based on the systematic literature review, this paper analyzed the goals, methodologies, and application results of such approaches. AI methods applied to construction contract management are classified into several categories. This study identified possibilities and limitations of the application of such approaches. This study contributes to providing the directions for how such approaches should be applied to contract management for future studies, which will eventually lead to more effective management of claims and disputes.

• Annual Conference of KIPS
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• 2023.05a
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• pp.200-202
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• 2023

SDN (Software Defined Networking) is an emerging networking system which differs from traditional network architecture. Moreover SDN has many advantages and special capabilities that traditional networks do not have. SDN and P4 are related in that they can be combined to create more advanced and intelligent networking systems. Additionally, Al has emerged as a transformative force in various fields, including SDN. By applying Al and P4 to SDN, network administrators can leverage the power of them to make impact on SDN security. We offer an overview of recent trend of SDN security integrating P4 a nd Al in this study.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.75-83
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• 2023

Smart manufacturing in Industry 4.0 is developing toward autonomous manufacturing as a last-mile technology. We investigate development trends in manufacturing innovation technologies, review major industrial intelligence projects currently carried out at ETRI, and infer directions of future technology developments.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1063-1064
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• 2023

인류는 주가를 과학적으로 예측하기 위해 수많은 학문적 노력을 기울여왔지만, 아직까지도 풀지 못한 난제로 남아 있다. 이에 본 연구에서는 깊은 수학적 원리에 기반하고 알파고 등에서 인간을 능가하는 성능을 보여준 강화학습 기술을 주식 트레이딩에 적용한 RLTA 모델을 제안하고, 실험을 통해 그 유용성을 입증하였다.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4282-4286
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• 2023

Nuclear energy is estimated by the machine learning method as the mathematical quantifications where neural networking is the major algorithm of the data propagations from input to output. As the aspect of nuclear energy, the other energy sources of the traditional carbon emission-characterized oil and coal are compared. The artificial intelligence (AI) oriented algorithm like the intelligence of a robot is applied to the modeling in which the mimicking of biological neurons is utilized in the mathematical calculations. There are graphs for nuclear priority weighted by climate factor and for carbon dioxide mitigation weighted by climate factor in which the carbon dioxide quantities are divided by the weighting that produces some results. Nuclear Priority and CO2 Mitigation values give the dimensionless values that are the comparative quantities with the normalization in 2010. The values are 1.0 in 2010 of the graphs which are changed to 24.318 and 0.0657 in 2040, respectively. So, the carbon dioxide emissions could be reduced in this study.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.113-120
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• 2024

Recently, case presentations using AI functions such as ChatGPT are increasing in many industrial fields. As AI-based results emerge even in the areas of images and videos, traditional animation production tools are in need of significant changes. Unreal Engine is the tool that adapts most quickly to these changes, proposing a new animation production workflow by integrating tools such as Metahuman and Marvelous Designer. Working with realistic metahumans allows for the production of realistic and natural movements, such as those captured through motion capture data. Implementing this approach presents many challenges for production tools that adhere to traditional methods. In this study, we investigated the differences between the cell animation workflow and the computer graphics animation production workflow. We compared and analyzed whether these differences could be reduced by creating sample movements using character rigs in Maya and Cascadeur tools. Our results showed that a similar cell animation workflow could be constructed using the Cascadeur tool. To improve the accuracy of our conclusions, we created large, action-packed short animations to demonstrate and validate our findings.

• Electronics and Telecommunications Trends
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• v.39 no.5
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• pp.12-20
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• 2024

As more devices are used across various industries and their performance improves, artificial intelligence applications are being increasingly adopted. Hence, the rapid development of neural networks suitable for diverse devices can determine the competitiveness of companies. Machine learning operations (MLOps), which constitute a framework that supports neural network generation and its immediate application to devices, have become necessary for the development of artificial intelligence. Currently, most MLOps are provided by major companies such as Google, Amazon, and Microsoft, which provide cloud services supported by large-scale computing power. In addition, various services are provided by the open-source project Kubeflow. We examine basic concepts and technology trends in MLOps and unveil additional functions required in industry.

• Journal of Information Technology Applications and Management
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• v.31 no.4
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• pp.47-61
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• 2024

A manned and unmanned complex combat system refers to a combat system that performs various missions by operating manned and unmanned aircraft together. The combat system is rapidly becoming more advanced due to recent remarkable developments in information and communication technologies(ICT), including AI and 5G, and major countries are actively using it in actual battlefields. Furthermore, the importance of this combat system is increasing and it is emerging as the core of future warfare. Accordingly, this study analyzed the concept of the manned and unmanned complex combat system and the current status of its integration with ICT, presented an operational concept utilizing it, and then analyzed the actual current status of related combat systems at home and abroad. Lastly, five suggestions were presented for the development of domestic manned and unmanned complex combat systems.

• Annual Conference of KIPS
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• 2024.10a
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• pp.831-832
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• 2024

이 연구는 LLVM IR 을 활용한 보안 취약점 탐지의 새로운 접근 방식을 제시합니다. Juliet Test Suite 의 CWE-121 Stack-based Buffer Overflow 데이터를 사용하여 Word2Vec 으로 코드를 벡터화하고 LSTM 모델로 학습했습니다. 모델 성능은 정확도 90%, 정밀도 87%, 재현율 93%, F1 스코어 90%로 평가되었습니다. 향후 다양한 보안 취약점을 다룰 수 있는 다중 분류 모델로 확장 가능성을 제안합니다.