• Electronics and Telecommunications Trends
• /
• v.35 no.5
• /
• pp.1-13
• /
• 2020

Recently, network infrastructure has evolved into a BizTech agile autonomous network to cope with the dynamic changes in the service environment. This survey presents the expectations from two different perspectives of the harmonization of network and artificial intelligence (AI) technologies. First, the paper focuses on the possibilities of AI technology for the autonomous network industry. Subsequently, it discusses how networks can play a role in the evolution of distributed AI technologies.

• Electronics and Telecommunications Trends
• /
• v.35 no.7
• /
• pp.23-35
• /
• 2020

In this paper, we introduce ETRI AI Strategy #3, "Leading Future Technologies of Network, Media, and Content." Its first goal is "to innovate AI service technology to overcome the current limitations of AI technologies." Artificial intelligence (AI) services, such as self-driving cars and robots, are combinations of computing, network, AI algorithms, and other technologies. To develop AI services, we need to develop different types of network, media coding, and content creation technologies. Moreover, AI technologies are adopted in ICT technologies. Self-planning and self-managing networks and automatic content creation technologies using AI are being developed. This paper introduces the two directions of ETRI's ICT technology development plan for AI: ICT for AI and ICT by AI. The area of ICT for AI has only recently begun to develop. ETRI, the ICT leader, hopes to have opportunities for leadership in the second wave of AI services.

• International Journal of Advanced Culture Technology
• /
• v.10 no.2
• /
• pp.246-251
• /
• 2022

As the number of data and devices explodes, centralized data processing and AI analysis have limitations due to the load on the network and cloud. On-device AI technology can provide intelligent services without overloading the network and cloud because the device itself performs AI models. Accordingly, the need for on-device AI technology is emerging. Many smartphones are equipped with On-Device AI technology to support the use of related functions. In this paper, we compare software tools that implement On-Device AI.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.307-313
• /
• 2024

This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.655-656
• /
• 2021

April 2021, Disaster Safety Communication Network services have been launched, but security functions are weak at the beginning of the service. The current security method for Android-based APP is using Google Protect's technology to detect malware. Malware is difficult to detect directly because there are various types, so by applying malware detection technology that combines AI and Google Protect technology to Disaster Safety Communication Networks, research on how to implement 'AI-based Disaster Satety Communication Network Protect'.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.681-683
• /
• 2023

폐암은 전 세계적으로 사망률이 가장 높은 암 질환으로, 조기 발견 및 신속한 치료를 위해서는 흉부 X-ray 영상 내 악성 결절을 놓치지 않는 것이 중요하다. 그러나 흉부 X-ray 영상은 정밀도의 한계로 진단 결과에 대한 신뢰도가 낮아, 이를 보조하는 도구의 개발이 요구된다. 기존의 폐암 진단 보조 도구는 학습 기반의 기법으로, 진단 결과에 대한 설명성(explainability)이 없다는 위험성을 갖는다. 이에 본 논문에서는 통계 분석에 기반한 결절의 석회화 여부 진단 기법을 제안한다. 제안하는 기법은 결절과 해부학적 구조물의 밝기 차 분포로부터 석회화 여부를 판단하며, 그 결과 민감도 65.22%, 특이도 88.48%, 정확도 83.41%의 성능을 보였다.

• International journal of advanced smart convergence
• /
• v.11 no.4
• /
• pp.10-19
• /
• 2022

AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.644-646
• /
• 2023

스마트팩토리는 협동 로봇과 같은 프로그래머블한 설비의 유기적인 협업을 통해 최적화된 공정을 수행한다. 따라서 수집되는 센서 데이터의 특징과 환경 조건의 복잡도가 높아, 예지보전을 위한 AI 소프트웨어의 개발 시 요구사항 기반의 체계적인 개발 및 검증이 필수적이다. 본 논문에서는 AI 소프트웨어의 요구사항을 사용자와 시스템 관점에서 정의하고, AI 모델 개발 프로세스와 스마트팩토리 예지보전 측면에서 분석한다. 도출된 요구사항을 CNN 기반의 협동 로봇 기어 마모 예측 모델의 개발에 적용하여 데이터 관리와 모델 신뢰성 관점의 요구사항을 분석 및 검증하였다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.549-559
• /
• 2023

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.12A
• /
• pp.1181-1187
• /
• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.