Earticle

스마트건설을 위한 업무의 디지털화는 상호 간의 디지털데이터를 원활하게 교류할 수 있어 야 효과가 있으나, 디지털데이터의 표준화 및 활용방안이 부족하다. 본 연구는 도로 건설현장 의 정보를 효과적으로 디지털로 변환할 수 있는 디지털 건설정보구조를 제안한다. 연구대상은 도로건설업무에서 중요도가 높은 작업계획, 일정관리, 안전관리, 품질관리이다. 건설정보구조 의 핵심은 건설정보를 객체와 작업으로 분리하고, 이 두 정보의 결합으로 단위업무를 정의함 으로써 건설정보의 표현 및 수정의 유연성을 확보하는 것이다. 객체와 작업은 각자의 계층구 조를 가지며, 이 계층구조는 실제 내용에 맞추어 가변형으로 정의하였다. 이는 효율성과 상세 성을 동시에 만족하는 구조다. 이 구조를 고속도로 건설공사에 시범적용 하였고, 범용 포맷을 이용해 구조를 디지털로 구현하였다. 본 연구는 현실에 가깝게 도로 건설공사 과정을 디지털화할 수 있으며, 도로건설 전주기의 디지털트윈의 토대가 되어 토목산업의 디지털전환을 가속화 할 것이다.

The digitalization of tasks for smart construction requires the smooth exchange of digital data among stakeholders to be effective, but there is a lack of digital data standardization and utilization methods. This paper proposes a digital construction information structure to transform information from road construction sites into digital formats. The study targets include significant tasks, such as work planning, scheduling, safety management, and quality control. The key to the construction information structure is separating construction information into objects and activities, defining unit works by combining these two types of information to ensure flexibility in representing and modifying construction information. The objects and activities have their respective hierarchical structures, which are defined flexibly to match the actual content. This structure achieves both efficiency and detail. The pilot structure was applied to highway construction projects and implemented digitally using general formats. This study enables the digitalization of road construction processes that closely resemble reality, accelerating the digital transformation of the civil engineering industry by developing a digital twin of the entire road construction lifecycle.

본 연구는 디지털 전환의 핵심 기술인 디지털 트윈 관련 연구 동향을 탐색하는 것을 목적으로 한다. 이를 위해 Web of Science 데이터베이 스에서 총 7,957편의 학술 논문을 수집하였으며, 수집된 데이터를 체계적으로 검토하기 위해 계량적 문헌 분석 기법인 토픽 모델링을 활용하 였다. 수집된 논문의 초록 데이터를 바탕으로 LDA (latent Dirichlet allocation) 모델을 적용한 결과, 총 14개의 연구 주제가 도출되었다. 분석 결과에 따르면 ‘Digital Manufacturing and Production’, ‘Simulation and System Modeling’과 같은 디지털 트윈의 출발점이 되는 주제뿐만 아 니라, ‘Digital Technologies in Urban and Industrial Applications’, ‘BIM and Construction Management’, ‘Energy Systems and Renewable Power Grid Management’, ‘Human-Robot Interaction and Virtual Environments’, ‘Clinical Applications and Patient Modeling’ 등의 세부 주 제가 확인되었다. 특히, ‘Predictive Modeling and Performance Optimization’과 같은 주제는 최근 높은 성장률을 보이며 중요한 연구 분야로 부상하고 있으며, 디지털 트윈 기술이 제조업을 넘어 다양한 산업 분야로 확장되고 있음을 확인하였다. 이러한 발견은 디지털 트윈 기술이 산 업 전반의 디지털 전환을 촉진하고 있음을 시사하며, 연구자들에게 향후 연구 방향에 대한 중요한 기초 자료를 제공할 뿐만 아니라, 기업들이 디지털 트윈을 도입할 때 해당 기술이 제공할 수 있는 실제적 가치를 명확히 인식하고 이를 효과적으로 활용할 수 있는 지침을 제공하는 데 기 여할 것이다.

This study aims to explore research trends related to digital twins, a core technology in digital transformation. A total of 7,957 academic papers were collected from the Web of Science database, and topic modeling, a quantitative bibliometric analysis method, was employed to systematically review the collected data. Specifically, the LDA (latent Dirichlet allocation) model was applied to the abstracts of these papers, resulting in the identification of 14 research topics. The findings revealed not only foundational topics such as ‘Digital Manufacturing and Production’ and ‘Simulation and System Modeling,’ but also more specific topics like ‘Digital Technologies in Urban and Industrial Applications,’ ‘BIM and Construction Management,’ ‘Energy Systems and Renewable Power Grid Management,’ ‘Human-Robot Interaction and Virtual Environments,’ and ‘Clinical Applications and Patient Modeling.’ Notably, the topic ‘Predictive Modeling and Performance Optimization’ exhibited high growth, emerging as a significant area of research, while the application of digital twin technology was found to be expanding beyond manufacturing into various industrial sectors. These findings suggest that digital twin technology is accelerating digital transformation across industries, providing critical foundational insights for future research directions. Additionally, they offer practical guidance for companies in recognizing and effectively leveraging the tangible value that digital twins can deliver.

디지털시대의 일환으로 현실세계를 가상으로 정확하게 옮겨서 현실에서 할 수 없는 어떤 고가의 장비 수명을 저하시키는 스트레스 테스트를 실시하여 제품의 취약부분을 시뮬레이션 하는 디지털 트윈이 제조업에서 활발히 사용되고 있다. IoT의 발전으로 인해 현실세계에서 수집되는 데이터를 정확하게 수집하여 가상의 공간에 동일하게 만드는 디지털 트윈은 교통, 재난, 주거, 방역, 에너지, 환경, 고령화 같은 도시 생활의 문제에 대한 정확 한 예측을 할 수 있기 때문에 스마트 시티 건설에 필요한 도구로 자리매김하고 있다. 디지털 트윈은 제조업분야에 적용이 많이 되고 있지만 본 연구에서는 스마트 시티에 적용하여 4차산업혁명시대에 걸 맞는 스마트 시티 모델을 제안하고 제안된 모델을 통한 시민의 안전, 복지와 편리성을 증가시켜 기술의 발전이 삶의 질을 향상시키는 데 기여하고 한다. 또한, 디지털 트윈을 스마트 시티에 적용할 경우 실시간 상호작용을 통하여 현실성과 즉시성을 유지함으로써 현실과 가상간의 실시간 동기화를 통화여 보다 정확한 예측 및 분석을 할 수 있을 것으로 기대한다.

As part of the digital era, a digital twin that simulates the weak part of a product by performing a stress test that reduces the lifespan of some expensive equipment that cannot be done in reality by accurately moving the real world to virtual reality is being actively used in the manufacturing industry. Due to the development of IoT, the digital twin, which accurately collects data collected from the real world and makes it the same in the virtual space, is mutually beneficial through accurate prediction of urban life problems such as traffic, disaster, housing, quarantine, energy, environment, and aging. Based on its action, it is positioned as a necessary tool for smart city construction. Although digital twin is widely applied to the manufacturing field, this study proposes a smart city model suitable for the 4th industrial revolution era by using it to smart cities and increasing citizens' safety, welfare, and convenience through the proposed model. In addition, when a digital twin is applied to a smart city, it is expected that more accurate prediction and analysis will be possible by real-time synchronization between the real and virtual by maintaining realism and immediacy through real-time interaction.

21세기 디지털 기술의 급격한 발전은 미술관 운영 방식에도 근본적인 변화를 요구하고 있다. 특히, 디지털 트윈 기술은 물리적 공간과 자산을 가상 환경 에서 실시간으로 재현하여 데이터 기반 분석과 운영 최적화를 가능하게 하는 혁신적 도구로 주목받고 있다. 이를 통해 미술관은 소장품 보존, 관람객 맞춤 형 서비스 제공, 운영 효율성 증대 등 다양한 가치를 실현할 수 있다. 그러나 대부분의 미술관, 특히 중소규모 기관은 초기 투자 비용, 전문 인력 부족, 데이 터 보안 문제, 기술 표준화 부족 등 디지털 트윈 도입과 운영에서 여러 한계에 직면하고 있다. 본 연구는 디지털 트윈 기반 스마트 미술관의 개념과 특성을 분석하고, 국내외 주요 미술관 사례를 통해 기술 도입의 장단점을 평가하며, 이를 해결하기 위한 방안을 제시한다. 디지털 트윈 플랫폼은 IoT, AI, 클라우드 컴퓨팅, 빅데이터를 통합하여 물리적 자산과 디지털 자산을 연결하고, 실시간 데이터를 통해 문제를 예측하고 조정하는 기능을 제공한다. 이를 바탕으로 전 시 환경 최적화, 관람객 맞춤형 서비스, 유지보수 비용 절감 등 운영 혁신이 가능하다. 물론 해결해야 하는 문제점도 있다. 이를 해결하기 위해 연구는 초기 비용 부담을 낮추기 위한 단계적 도입, 데이터 보안 시스템 강화, 기술 표 준화 촉진, 전문 인력 양성, 관람객 접근성 개선 방안을 제안한다. 이를 통해 디지털 트윈 플랫폼 기반 스마트 미술관은 운영 효율성과 관람객 경험을 혁신 하며, 문화유산의 지속 가능한 보존과 활용을 가능하게 하는 미래 지향적 모델 로 자리 잡을 것이다.

The rapid development of digital technology in the 21st century is also demanding fundamental changes in the way art museums operate. In particular, digital twin technology is attracting attention as an innovative tool that enables data-based analysis and operational optimization by reproducing physical spaces and assets in a virtual environment in real time. Through this, art museums can realize various values such as preserving collections, providing customized services to visitors, and increasing operational efficiency. However, most art museums, especially small and medium-sized institutions, face various limitations in the introduction and operation of digital twins, such as initial investment costs, lack of specialized personnel, data security issues, and lack of technology standardization. This study analyzes the concept and characteristics of a smart art museum based on digital twins, evaluates the pros and cons of technology introduction through cases of major art museums at home and abroad, and suggests measures to solve them. The digital twin platform integrates IoT, AI, cloud computing, and big data to connect physical and digital assets, and provides the ability to predict and adjust problems through real-time data. Based on this, operational innovations such as exhibition environment optimization, customized services for visitors, and reduced maintenance costs are possible. Of course, there are also issues that need to be resolved. To address this, the study proposes a phased introduction to reduce initial cost burden, strengthen data security systems, promote technology standardization, train professional personnel, and improve accessibility for visitors. Through this, the smart art museum based on the digital twin platform will innovate operational efficiency and visitor experience, and become a future-oriented model that enables sustainable preservation and utilization of cultural heritage.

국내 상하수도 시설들은 운전과 관리 인력의 노후화와 같은 유지관리의 어려움이 가속화되고 있으므로 디지털트윈 기술 이 유력한 관리 기술로 부각되고 있다. 국내 정수장의 디지털트윈 기술은 환경부의 지능형 하수처리, 일부 지자체에서 독자적으로 진행되는 사업 및 K-water 주관의 디지털 트윈 과제 등이 포함되지만, 적용 범위가 각 기관별로 상이하다. 이에 따라 정수공정에서는 시행착오를 줄이고, 미래 사업 활성화를 위해서는 기술표준화와 순차적 도입과정이 필요하다. 본 연구의 목적은 환경부 스마트하수처리사업, K-water 지능형정수공정 구현사업, 지자체 사업 등 각 기관별로 추진되고 있는 디지털트윈 관련 기술에 대한 효율적인 추진 전략을 제공하는데 있다.

Domestic water supply and sewage facilities are rapidly aging and maintenance difficulties such as aging of operation and management personnel are overlapping, so Digital Twin technology is attracting attention as an intelligent means of process management. Digital twin projects for domestic water treatment processes include the smart sewage treatment project promoted by the Ministry of Environment, projects independently promoted by some local governments, and digital twin purification plant projects promoted by K-water. However, the content of digital twin promotion is different for each institution. Therefore, in the water treatment process, technological standardization and step-by-step implementation methods for digital twins must be preceded to reduce trial and error in future business promotion. This study aims to provide an efficient promotion plan by prescribing the digital twin element technology and composition method in the water treatment process and reviewing the contents currently being promoted by the Ministry of Environment, local governments, and K-Water individually.

본 연구의 목적은 해군 무기체계 전력발전업무 단계별 디지털 트윈 적용방안을 제시하는 것이다. 첫째, 소요기획단계에서 현재 브레인스토밍 및 자료조사의 형태로 수행되는 사전 개념연구를 디지털 트윈을 적용하여 가상환경에 개략적인 성능을 보 유한 디지털 모델을 제작하고 테스트하는 것을 제안하였다. 둘째, 획득단계에서는 이렇게 구축된 가상환경을 바탕으로 선행 연구에서 M&S체계를 구축한 뒤 탐색개발에서 세부 모델링과 센서가 부착되는 디지털 트윈을 구축하고 이후 체계개발에서 더욱 고도화된 디지털 트윈을 구축한다. 이렇게 구축된 디지털 트윈을 시험평가에 적용하여 함정 전력화 기간을 단축하는 방 안도 제시하였다. 마지막으로 운용유지단계에서는 기구축된 디지털 트윈체계를 이용하여 실제 운용 환경에서 획득한 데이터 를 분석한 뒤 고장을 진단 및 예측하는 방안을 제시하였다. 마지막으로 해군 무기체계 특성에 맞는 디지털 트윈 구축을 위한 아키텍처를 제시하였다. 총 3가지 모듈로 구성되어 있으며 가상-물리 상호작용 모듈에서 센서를 통한 데이터가 수신되고 가 상환경의 디지털 모델이 동작한다. 데이터 통합 모듈에서 각종 센서 신호 및 가상환경의 시뮬레이션 결과가 저장 및 분석을 위한 전처리 과정이 진행된다. 지식 계산 및 시각화 모듈에서 다양한 방법을 사용한 데이터 분석을 실시하고 분석 결과를 실 제 장비에 피드백하거나 운용자에게 알려주게 된다.

The purpose of this study is to propose a digital twin application plan according to the three stages of naval weapon system force development work. Firstly, in the planning stage, pre-conceptual research is currently performed by brainstorming and data research. Therefore, this study proposed to create and test a digital model in a virtual environment using digital twin technology. Secondly, in the acquisition stage, a plan was proposed to upgrade the digital twin system based on the previously established virtual environment. In the previous stage, M&S system is created and a digital twin modeling and sensors are created in the exploration and development stage. After that, advanced digital twin system will be built in the system development stage. Lastly, in the operation and maintenance stage, analyzing the data obtained from the real environment using the digital twin system, it predicts equipment failure and repair the broken equipment quickly. Next, a digital twin architecture suitable for the naval weapon system was proposed. The architecture consisted of a virtual-physical interaction module, a data integration module, and a knowledge calculation and visualization module.

최근 네트워크 환경에서는 사이버 위협에 대한 해결책을 찾는 노력이 높아지고 있다. 특히, 사이버 보안 측면에서 악의적인 행위를 막기 위해서 사이버 보안에 AI가 통합되고 있다. 본 논문에서는 네트워크 리소스를 다양 한 클라우드 서비스를 제공할 수 있도록 디지털 트인 기반의 전방 공격 탐지 모델의 성능평가를 제안한다. 제안 모델은 안전한 데이터 저장 및 검증을 위해 블록체인을 통합한 IoT 환경에 AI 기반 IDS 프레임워크을 사용한다. 제안 모델은 네트워크 내 모든 장비를 블록체인 블록으로 묶어 처리함으로써 클라우드 내 IoT 리소스의 특징별 블록들의 교차 분석을 통해 보안 점검이 이루어지지만, 최소 오류는 허용한다. 성능평가 결과, 제안 모델은 학습 중 모든 카테고리에서 고르게 리샘플링함으로써 생성기가 소수 클래스에서도 학습할 수 있도록 보장하여 프론트 러닝 공격 탐지 성능을 향상시켰다. 또한 제안 모델은 실제 데이터의 조건부 분포를 학습하여 실제 데이터와 합성 데이터를 더 잘 구분하였다.

In recent network environments, efforts to find solutions to cyber threats are increasing. In particular, AI is being integrated into cybersecurity to prevent malicious behavior in terms of cybersecurity. In this paper, we propose the performance evaluation of the forward attack detection model using a digital open-based blockchain so that network resources can provide various cloud services. The proposed model uses an AI-based IDS framework in the IoT environment that integrates blockchain for secure data storage and verification. The proposed model manages additional information that can handle the rights of IoT resources as blockchain blocks. The proposed model performs a security check through cross-analysis of blocks by IoT resource characteristics in the cloud by grouping all equipment in the cloud into blockchain blocks, but allows for minimum errors. As a result of the performance evaluation, the proposed model evenly resampled in all categories during learning, ensuring that the generator could learn even in a few classes, thereby improving the front learning attack detection performance. In addition, the proposed model better distinguished between actual data and synthetic data by learning the conditional distribution of actual data.

This study continues the entire process of researching how to build a pet digital twin, and continues to draw conclusions through cluster surveys and analysis of experimental results. First, with the advancement of AI models, the Transformer model achieved 98.5% accuracy in processing time-series sensor data, demonstrating superior performance compared to existing CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) (95.8%) or RF (Random Forest) (91.2%). Second, to overcome the differences in body shape between dog breeds in vision-based pose estimation using multimodal and shape-adaptive technologies, a family-aware model such as AniMer is essential, and accuracy is secured through fusion with an IMU (Inertial Measurement Unit) sensor. Third, the architecture, which applies its own framework and the MQTT (Message Queuing Telemetry Transport) protocol, a cloud-centric lightweight protocol, through platform standardization, enables real-time synchronization between physical sensor data and virtual engines (Unity/Unreal Engine), and provides the foundation for a bio-digital twin that visualizes even invisible information such as bioenergy through software sensors. Beyond a simple technological endeavor, companion animal digital twins will become established as a “digital companion” technology that expands communication channels with companion animals who cannot speak and dramatically improves their quality of life.

本研究全面探讨了面向宠物数字孪生构建的行为模式研究全流程,通过文献调研与实验结 果分析,得出如下结论.第一,随着人工智能模型的优化升级,在时间序列传感器数据处理任务 中,基于Transformer架构的模型达到了98.5%的准确率,相较传统的卷积长短期记忆网络 (95.8%) 和随机森林算法(91.2%) 展现出更优性能,这表明自注意力机制在捕捉宠物犬行为 的长期关联特征方面具有显著效果.第二,在基于计算机视觉的姿态识别任务中,借助多模态与 形态自适应技术,想要克服不同犬种之间的体型差异,AniMer这类犬种识别适配模型不可或缺 ; 同时,通过与惯性测量单元传感器的数据融合,进一步保障了识别精度.第三,在平台标准 化层面,集成自研框架与消息队列遥测传输协议的系统架构,实现了物理传感器数据与虚拟引擎 (Unity/虚幻引擎) 的实时同步,依托软件传感器完成了生物能量等非可视化信息的可视化呈 现,为构建宠物生理数字孪生奠定了技术基础.宠物数字孪生的价值远超单纯的技术尝试,它将 作为一种“数字伙伴”技术,拓宽与无法言语的宠物犬之间的沟通渠道,显著提升宠物的生活质 量.未来的研究方向,有望朝着能够实时学习个体特性并持续进化的“自适应数字孪生”方向发 展,同时推动其与低延迟通信􀕵边缘计算技术的深度融合.

본 연구는 기후위기와 디지털 전환 환경 속에서 대형 국립미술관의 시설운영을 데이터 기반 지능형 체계로 혁신 하기 위한 통합 운영모델을 제안한다. 이를 위해 루브르 박물관, 메트로폴리탄 미술관 등 해외 선도 사례와 국내 국 립미술관의 현황을 비교 분석하였다. 분석 결과, 해외 기관들은 에너지·환경·설비 데이터를 통합 관리하고 AI와 디 지털 트윈 기술을 통해 예지보전과 에너지 최적화를 실현하고 있는 반면, 국내 기관은 부서 간 데이터가 단절된 단순 모니터링 단계에 머물러 있는 것으로 나타났다. 이에 본 연구는 시설운영 데이터를 5대 핵심 범주(에너지, 설비상태, 환경, 안전, 공간점유)로 체계화하고, 이를 기 반으로 한 ‘데이터 수집-클라우드 통합 플랫폼-AI 분석 및 예측-운영 거버넌스’의 4계층 통합모델과 핵심성과지표 (KPI)를 제시하였다. 본 연구는 기존의 스마트 미술관 논의를 전시·관람 중심에서 시설 운영 인프라와 조직 혁신의 영역으로 확장함으로써, 국립미술관의 지속가능한 경영을 위한 실질적 가이드라인을 제공하는 데 의의가 있다.기여 하고자 한다.

This study proposes an integrated operational model to transform the facility management of large-scale national art museums into a data-driven intelligent system in the context of the climate crisis and digital transformation. To this end, the study conducts a comparative analysis of leading international cases, such as the Louvre Museum and the Metropolitan Museum of Art, alongside the current status of national art museums in Korea. The analysis reveals that while leading overseas institutions achieve predictive maintenance and energy optimization by integrating energy, environment, and equipment data through AI and digital twin technologies, domestic institutions remain at a stage of simple monitoring characterized by data fragmentation across departments. Accordingly, this study systematizes facility operation data into five core categories— energy, equipment condition, environment, safety, and space occupancy. Based on this, it proposes a four-layer integrated model consisting of data collection, a cloud-based integrated platform, AI analysis and prediction, and operational governance, along with Key Performance Indicators (KPIs). This study is significant in that it extends the discussion of smart museums from exhibition and visitor services to the realms of facility infrastructure and organizational innovation, thereby providing practical guidelines for the sustainable management of national art museums.

본 도시 교통 혼잡은 간선도로망 전반에서 장시간의 지연, 배출 증가, 그리고 안전성 저하를 초래한다. 본 논문 은 스마트 폴 엣지 컴퓨팅과 연합형 다중 에이전트 강화학습을 결합한 교차로 제어용 엣지-네이티브 프레임워크를 제안 한다. 각 교차로의 경량 에이전트는 폴 탑재 센서로부터 로컬 학습을 수행하고, 프라이버시를 보존하는 연합 절차가 네 트워크 전역에서 주기적으로 모델 업데이트를 집계한다. 통신 계층은 저지연 로컬 V2X 교환을 위한 IEEE 802.11p와 모니터링 및 모델 조정을 위한 MQTT 5.0을 통합한다. 평가는 802.11p와 NR-V2X 링크 동작을 반영하기 위해 VEINS(OMNeT++–SUMO)와 Simu5G를 결합한 디지털 트윈-인-더-루프 환경에서 수행된다. 배출량은 SUMO의 HBEFA 기반 모형으로 추정한다. 고정주기, 감응식, 비연합형 MARL 기준선과 비교할 때, 제안 방법은 네트워크 전역 평균 지연과 대기행렬 길이를 줄이는 동시에 CO₂와 NOx를 낮출 잠재력을 보인다. 본 논문은 아키텍처, 학습 및 통신 설계, 그리고 시나리오 구성·평가지표·통계 검정을 위한 재현 가능한 프로토콜을 상세히 기술한다.

Urban traffic congestion leads to prolonged delays, elevated emissions, and diminished safety across arterial networks. This paper proposes an edge‑native framework for intersection control that combines smart‑pole edge computing with federated multi‑agent reinforcement learning. Lightweight agents at each intersection learn locally from on‑pole sensors while a privacy‑preserving federated procedure periodically aggregates model updates across the network. The communication layer integrates IEEE 802.11p for low‑latency local V2X exchange and MQTT 5.0 for monitoring and model coordination. Evaluation is conducted in a digital‑twin‑in‑the‑loop environment that couples VEINS (OMNeT++–SUMO) with Simu5G to account for 802.11p and NR‑V2X link behavior. Emissions are estimated using SUMO’s HBEFA‑based model. Compared with fixed‑time, actuated, and non‑federated MARL baselines, the proposed approach shows the potential to reduce network‑wide average delay and queue length while simultaneously lowering CO₂ and NOx. The paper details the architecture, the learning and communication design, and a reproducible protocol for scenario construction, metrics, and statistical testing.

Many vehicles equipped with autonomous driving functions have been developed to increase driver convenience and secure safety, and are being driven on actual roads. Various sensors such as camera, radar and lidar to recognize surrounding objects are applied on the autonomous driving-based vehicles, and advanced technology to control the vehicles using data collected from the sensors is grafted on them. To identify the cause of an accident in autonomous vehicles, it is necessary to compare and analyze a large of scenarios. However, there are constraints of time and cost in the traditional field investigation, and as a result, it causes limitations in defect investigation. This paper will present the necessity of autonomous driving simulation and its methodology for analysis of the autonomous driving accident. The simulation software is Prescan which has been developed by SIEMENS.

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.

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

Digital transformation is revolutionizing warehousing by integrating advanced technologies like Digital Twins, AI, and IoT. Digital Twins create virtual warehouse replicas for simulation and optimization, enhancing resource allocation and proactive problem-solving. AI-driven systems predict and optimize processes, while Automated Guided Vehicles (AGVs) streamline the movement of goods. IoT improves connectivity and data collection, supporting decision-making through machine learning and deep learning. Robots, utilizing reinforcement learning and computer vision, automate tasks like picking and packing, boosting efficiency and reducing errors. Additionally, anomaly and intrusion detection enhance security. This review highlights key advancements in path planning, task allocation, inventory management, and storage assignment, which improve operational effectiveness. It also addresses the lack of analysis on the economic impacts of digital transformation, particularly in cost reduction, return on investment (ROI), and customer satisfaction. The paper identifies research gaps, including the integration of sustainability, adaptation to dynamic environments, collaborative robots, and optimization of reverse logistics. This review provides a foundation for future research on the potential of digitalization to transform warehousing practices.

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.

This paper introduces a semantic-aware framework for communication and computation in Internet of Drones (IoD)-assisted digital twin (DT) networks within the context of smart agriculture. In our system, the IoD gathers environmental data and conducts onboard semantic analysis to identify task-relevant features before transmission. We propose a joint optimization scheme for offloading and semantic processing that utilizes federated deep reinforcement learning (FDRL) to address communication and computation constraints via context-adaptive semantic communication. The simulation results show that the proposed approach substantially enhances energy efficiency and communication delay, depending upon the configuration, compared to traditional offloading strategies.

Digital twins (DTs) are an emerging digitalization technology with a huge impact on today’s innovations in both industry and research. DTs can significantly enhance our society and quality of life through the virtualization of a real-world physical system, providing greater insights about their operations and assets, as well as enhancing their resilience through real-time monitoring and proactive maintenance. DTs also pose significant security risks, as intellectual property is encoded and more accessible, as well as their continued synchronization to their physical counterparts. The rapid proliferation and dynamism of cyber threats in today’s digital environments motivate the development of automated and intelligent cyber solutions. Today’s industrial transformation relies heavily on artificial intelligence (AI), including machine learning (ML) and data-driven technologies that allow machines to perform tasks such as self-monitoring, investigation, diagnosis, future prediction, and decision-making intelligently. However, to effectively employ AI-based models in the context of cybersecurity, human-understandable explanations, and their trustworthiness, are significant factors when making decisions in real-world scenarios. This article provides an extensive study of explainable AI (XAI) based cybersecurity modeling through a taxonomy of AI and XAI methods that can assist security analysts and professionals in comprehending system functions, identifying potential threats and anomalies, and ultimately addressing them in DT environments in an intelligent manner. We discuss how these methods can play a key role in solving contemporary cybersecurity issues in various real-world applications. We conclude this paper by identifying crucial challenges and avenues for further research, as well as directions on how professionals and researchers might approach and model future-generation cybersecurity in this emerging field.

본 논문은 고도화 되고 있는 적의 핵･WMD 능력에 대응하여 추진 중인 한국형 3축체계에 대해서 살펴보고, 한국형 3축체 계의 기반이 되는 정보감시정찰(ISR : Intelligence Surveillance and Reconnaissance) 능력을 향상시키기 위한 방안을 찾고자 작성되었다. 증강현실(AR : Augmented Reality), 가상현실(VR : Virtual Reality), 혼합현실(MR : Mixed Reality) 의 기술을 넘어서 최근 각광받고 있는 확장현실(XR : eXtended Reality)과 디지털 트윈(Digital Twin) 기술의 정의와 최신 동향에 대해 알아보고, 이러한 기술들을 정보감시정찰(ISR) 작전에 적용하여 정보감시정찰(ISR) 능력을 향상시킴으로써 적의 핵･WMD에 대응하는 한국형 3축체계가 효과적으로 수행될 수 있도록 발전방향을 제시 한다.

This paper examines thetriad system of the Republic of Korea (ROK), which defends against advanced nuclear and weapon of mass destruction (WMD) attacks. The ROK triad system consists of the Kill-Chain, Korea Air and Missile Defense (KAMD), and Korea Massive Punishment and Retaliation (KMPR), which relies on advanced Intelligence, urveillance, and econnaissance (ISR) capabilities. The triad system was created to be an advanced situational awareness system with reliable and robust counter attack capabilities, where new technologies like etended eality (XR) and igital win models could be added to significantly enhance the performance and automate the operation. In this paper, the definition of XR and digital twin technologies and applications are described. In addition, descriptions of how toapplythese technologies to ISR operations and the triad system are introduced.