Earticle

본 연구는 항공 산업의 전례 없는 패러다임 전환기에 직면하여, 인천국제공항이 미래항공교통(Advanced Air Mobility, 이하 AAM) 시대를 선도하는 스마트 모빌리티 서비스 허브’로 성공적으로 도약하기 위한 구체적인 정책 및 전략 방안을 제시하는 데 목적이 있다. 전기동력과 수직이착륙 기술을 활용하는 AAM은 2040년경 글로벌 시장 규모 1조 달러에 이를 것으로 전망되는 핵심 모빌리티 혁명이다. 본 연구는 AAM 상용화의 이론적 기틀로서, 고객 중심의 개인화된 서비스 통합 플랫폼인 AaaS(Airport as a Service)와 지능형 디지털 트윈을 활용한 데이터 기반 운영 혁신 체계인 TAM(Total Airport Management)을 제시하고 이들의 통합적 관계를 규명하였다. 나아가 글로벌 기술 동향, 각국의 정책, 그리고 K-UAM 실증사업 결과를 분석하고, 내ㆍ외부 전문가 인터뷰 및 대국민 설문조사를 통해 인천공항의 SWOT 및 10대 핵심 과제를 도출하였다. 이를 바탕으로 AaaS 기반 사업 모델 혁신, TAM 기반 운영 모델 혁신, 지속가능한 안전 및 사회적 수용성 확보라는 3대 핵심 추진 전략을 수립하였다. 마지막으로 법ㆍ제도 개선, 규제 샌드박스 특구 지정, 민관 협력 거버넌스 구축에 대한 실효성 있는 정책을 제언한다. 본 연구는 공항이 단순한 물리적 인프라 제공자를 넘어 데이터와 서비스가 융합된 플랫폼 비즈니스로 진화하기 위한 학술적, 실무적 방향성을 제공한다는 점에서 의의가 있다.

This study aims to propose specific policy and strategic measures for Incheon International Airport to successfully transform into a 'Smart Mobility Service Hub' leading the era of Advanced Air Mobility (AAM), in response to the unprecedented paradigm shift in the aviation industry. AAM, leveraging electric vertical takeoff and landing (eVTOL) and autonomous flight technologies, is projected to become a trillion-dollar global market by 2040. This research establishes theoretical frameworks for the commercialization of AAM by introducing AaaS (Airport as a Service), a customer-centric integrated platform for personalized journeys, and TAM (Total Airport Management), a data-driven operational innovation system utilizing intelligent digital twins, while clarifying their integrated relationship. Furthermore, by analyzing global technology trends, policies, and the results of K-UAM grand challenge projects, alongside conducting in-depth expert interviews and public surveys, this study identified Incheon Airport's SWOT and top 10 core challenges. Based on these findings, three core strategies were formulated: innovation of business models based on AaaS, innovation of operational models based on TAM, and securing a foundation for sustainability focusing on absolute safety and social acceptance. Finally, practical policy recommendations are suggested, including the revision of relevant laws and regulations (Aviation Safety Act, Aviation Business Act, etc.), the designation of an AAM regulatory sandbox special zone, and the establishment of cooperative public-private governance. This study holds academic and practical significance as it provides actionable guidelines for airports to evolve from mere physical infrastructure providers into platform-centric mobility ecosystems.

Edge computing has emerged as a promising paradigm for addressing the latency, bandwidth, and scalability challenges associated with traditional cloud-centric architectures. Computation offloading, the process of transferring computational tasks from edge devices to more powerful remote servers or cloud infrastructure, plays a crucial role in optimizing performance and resource utilization in edge computing systems. However, traditional computation offloading techniques often face limitations related to latency, network dependency, and scalability. In this survey, we explore the integration of digital twin (DT) technology into edge computing environments to empower intelligent computation offloading decisions. DTs, virtual representations of physical entities or systems that mirror their real-world counterparts, offer opportunities to enhance situational awareness, optimize resource allocation, and enable more informed decision-making at the edge. We provide a comprehensive overview of DTs empowered intelligent computation offloading, covering the fundamentals of DTs, traditional computation offloading techniques, and their limitations in edge computing. Additionally, we discuss how DTs can address these challenges and improve computation offloading strategies, along with practical applications and use cases across various domains. Finally, we identify open research challenges and opportunities for future exploration in this emerging field. Through this survey, we aim to provide researchers, practitioners, and stakeholders with insights into the potential of DTs to revolutionize computation offloading for edge computing and drive innovation in this rapidly evolving area.

Localization modules play an important role in ensuring the secure operation of intelligent vehicles (IV) and accelerating the development of driving technologies. To safely drive an intelligent vehicle, an exact data registration is necessary. Map drift, scan-to-map alignment, high computational load, sensor noise, and calibration are the most prominent issues that cause poor localization performance for intelligent vehicles. Therefore, by presenting a digital twin-assisted data registration (DT-ADR) technique, this research attempts to mitigate that gap. The purpose of this study is to show how to implement the DT-ADR technique to help improve the existing light detection and ranging (LiDAR) data registration technique for more accurate vehicle localization and driving capabilities. We first investigate traditional data registration techniques that address the challenges for scan matching localization. We then present the proposed DT-ADR technique and discuss a pose selection technique. Next, we demonstrate an implementation of the proposed DT-ADR technique using AWSIM cosimulation, Autoware universe, and ROS2 virtual environments. To verify the effectiveness of the description of the localization approach, a case study is also conducted to analyze the pose estimation of IV. Lastly, an initial result is evaluated for the proposed DT-ADR technique, which reduces the orientation root mean squared error by an average compared to the existing LiDAR data registration technique based on normal distribution transforms. This work could be utilized as a testbed for future research that attempts to include advanced localization features for IV driving.

연구목적: 본 연구는 작업자 안전을 고려하고 제조기업의 생산성 향상, 품질 향상을 위해 디지털 트윈 기반의 AI 비전, 제조 실행 시스템, 위치 측위 기술을 활용한 지능형 제조공정 통합 모니터링 시스템을 개발하는데 목적이 있다. 연구방법: 제조공정 특성상 사출성형기 기반의 생산 작업을 고려하여 생산라 인 구조에 맞는 설계 및 적용을 통해 불량품 검출에 특화되도록 실시간 영상 기반의 AI 감지 기술을 적 용하였다. 연구결과: 제안된 방법을 통해 시스템 개발 및 적용하였고, 시스템 도입 전/후 생산 PPM 비교 하여 불량률 감소에 따른 생산성 향상을 확인하였다. 또한, 시스템 응답 속도 측정을 통해 현장 사고 발생 시 신속한 초기대응 및 상황전파가 가능한 것을 확인하였다. 결론: 본 연구의 시스템은 다양한 제조기업 이 제조공정 시스템 도입 시 의사결정을 지원하는 기초자료로 활용될 수 있을 것으로 기대된다.

Purpose: This study aims to enhance productivity in manufacturing companies while prioritizing worker safety and developing a digital twin-based AI vision to improve quality, creating manufacturing execution systems, and implementing an integrated monitoring system for intelligent manufacturing processes that leverages location positioning technologies. Method: Considering the characteristics of the manufacturing process, particularly for tasks involving injection molding machines, real-time video-based AI detection technology was applied to tailor its design and application to the production line structure, enhancing the specialized detection of defective products. Result: Using the proposed method, the system was developed and implemented, and a comparison of production PPM before and after its adoption demonstrated increased productivity due to reduced defect rates. Additionally, measurements of system response speed confirmed its ability to provide a swift initial response and rapidly disseminate information in the event of an on-site accident. Conclusion: The system developed in this study is expected to serve as a foundation for supporting decision-making in the implementation of manufacturing process systems across various companies.

많은 행정학･정책학 연구자들은 디지털 트윈 기술이 공공 정책 과정을 크게 변화시킬 것으로 전망하고 있으나 정책 의사결정을 위해 디지털 트윈을 어떻게 활용할 것인지에 대해서는 많은 것이 알려지지 않았다. 디지털 트윈 활용은 첫째, 행위자의 행태를 타당하게 설명하는 시뮬레이션 모델링의 구축과 둘째, 시뮬레이션 시나리오를 통한 예측 등의 두 단계로 진행된다. 특히 본 연구는 이 중에서 전통적인 연구 방법을 통해 어떻게 과학적 증거를 가진 시뮬레이션 시나리오를 개발할 수 있는지에 초점을 맞추었다. 이를 위해 디지털 트윈 기반 시뮬레이션 모델링을 적용한 세종시 공공자전거 어울링의 관리 및 운영을 위한 시뮬레이션 시나리오를 어떻게 전통적인 연구 방법 중 하나인 조건부 가치측정법을 활용해 만들 수 있는지 살펴보았다. 이를 통한 분석 결과에 따르면, 세종시 공공자전거 이용자들은 간헐적인 단거리 이동 수요를 중요하게 여기고 현재 공공자전거를 이용하지 않는 잠재적 이용자들은 건강 증진 등 다른 이용 가치를 중요하게 여긴다. 이를 고려하여 본 연구는 공공자전거 대여소 재배치 시나리오를 예시적으로 두 개로 정리하였다. 이상의 논의를 해본 연구는 디지털 트윈 기반 시뮬레이션 모델링이 지능적인 공공 정책 의사결정을 위하여 어떻게 활용될 수 있는지에 대한 함의를 제공하였다.

Researchers expect that digital twin technologies will transform the public policy cycle, but there is a lack of understanding on how digital twin technologies are applied to make policy decisions. Digital twin applications have two steps: 1) to develop a valid digital twin model to explain the behaviors of agents; 2) to predict changing behaviors of agents in various simulation scenarios. This paper focuses on how to develop scientific evidence-based simulation scenarios from traditional research methods in social science. For this purpose, we attempted to create scenarios from the contingent valuation method in estimating the value of the public bicycle-sharing system in Sejong City, where the digital twin model of the system has been developed to enhance its effective and efficient management. Findings suggest that public bicycle users in Sejong City pay attention to the intermittent short-distance movement demands. In contrast, potential users (or non-users) value a variety of usefulness (such as enhancing health). We terefore suggest two scenarios for redeploying public bicycle stations, as an example. This study provides implications for how governments use digital twin simulation modeling to make intelligent public policy decisions.