• Journal of information and communication convergence engineering
• /
• v.20 no.4
• /
• pp.273-279
• /
• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• Korean journal of aerospace and environmental medicine
• /
• v.31 no.2
• /
• pp.33-37
• /
• 2021

In the era of the 4th industrial revolution, the aviation industry is also growing remarkably with the development of artificial intelligence and networks, so it is necessary to study a new concept of crew resource management (CRM), which is required in the process of operating state-of-the-art equipment. The automation system, which has been treated only as a tool, is changing its role as a decision-making agent with the development of artificial intelligence, and it is necessary to set clear standards for the role and responsibility in the safety-critical field. We present a new perspective on the automation system in the CRM program through the understanding of the autonomous system. In the future, autonomous system will develop as an agent for human pilots to cooperate, and accordingly, changes in role division and reorganization of regulations are required.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
• /
• v.6 no.4
• /
• pp.262-266
• /
• 1977

A new computer system for the stress analysis and design of piping network has been devlo-ped in this study. For the stress analysis, the system utilizes the finite element technique in which the frontal technique is used as the equation solver. The element library of the system has (1) Pipe Element (2) Beam Element, (3) Hanger Element and (4) Spring Element which should be sufficient to model the entire piping system including flexible supports, joints, piping rack and hangers. Based on the element stresses, code check has been performed and the safety factor for each element is calculated.

• The Journal of Korea Robotics Society
• /
• v.18 no.3
• /
• pp.281-284
• /
• 2023

Real-time control communication network system is important for developing defense robots because it affects environmental interaction, performance, and safety. We propose a real-time control communication network using the Xenomai real-time operating system and the open-source EtherCAT master library, SOEM. EtherCAT is an Ethernet-based industrial communication method. It has low latency and many functions such as cable redundancy and distributed clock synchronization. We use Xenomai RTOS and Intel NUC to develop the system. Experimental tests demonstrate the Real-time EtherCAT master implementation, and communication with CiA301-based slave devices. The jitter measurement was conducted to validate the real-time performance of the system. The proposed system shows possibility for real-time robotics applications in various defense robots.

• Electronics and Telecommunications Trends
• /
• v.38 no.6
• /
• pp.95-106
• /
• 2023

As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.52-57
• /
• 2011

Bridges are essential and valuable elements in road and rail transportation networks. Bridge remediation is a top priority for asset managers, but identifying the nature of true defect deterioration and associated remediation treatments remains a complex task. Nowadays Decision Support Systems (DSS) are used extensively to assist in decision-making across a wide spectrum of unstructured decision environments. In this paper a requirements-driven framework is used to develop a risk based decision support model which has the ability to quantify the bridge condition and find the best remediation treatments using Multi Attribute Utility Theory (MAUT), with the aim of maintaining a bridge within acceptable limits of safety, serviceability and sustainability.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.11a
• /
• pp.1218-1221
• /
• 2007

센서 네트워크는 유비쿼터스 컴퓨팅 환경을 실현하기 위한 네트워크로 센싱 및 통신 능력으로 인간이 접근하기 어려운 다양한 곳에 설치되어 감시나 탐지 등을 통하여 데이터를 수집한다. 이러한 환경의 구현을 위하여 센서 네트워크에서 센서 노드가 수집한 데이터는 사용자에게 전달될 때 안전한 통신을 보장하기 위해 센서 노드간 키를 설정하는 것은 보안을 위한 기본적인 요구사항이 되고 있다. 따라서 초소형, 빈번한 데이터 이동, 제한적인 계산 능력 및 저장 능력 그리고 베터리 전력 사용이라는 특성을 갖는 센서 노드에 알맞은 암호화를 위한 키 관리 구조가 요구된다. 따라서 본 논문에서는 센서 네트워크에서의 효율적인 키 설정을 위해 클러스터에 기반한 구조와 다항식을 사용한 pair-wise key설정 방법을 제안 하였다.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.1-12
• /
• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2024.05a
• /
• pp.698-701
• /
• 2024

인공지능 기술이 발전함에 따라 인공지능은 인간이 하는 업무들을 대체하고 있다. 현재 인공지능 기술은 시각, 청각 분야로 초점이 맞춰져 있으나 최근 후각 분야에 관련된 연구도 활발히 진행 중이다. 후각 AI는 식품, 의료, 보안, 안전 등에 활용될 전망이며 본 논문에서는 우리 사회의 안전불감증 문제를 언급하고 오작동 비율이 높은 화재경보기에 후각 AI를 대입하여 화재경보기의 오작동 비율을 줄이고 화재경보기에 대한 인식을 해결되는 것을 기대한다.

• Nuclear Engineering and Technology
• /
• v.56 no.5
• /
• pp.1672-1678
• /
• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).