ETRI Journal最新文献

{"title":"Special issue on smart city technologies and services based on AI for digital twin applications","authors":"Byoung Chul Ko, Ming-Ching Chang, Jong Taek Lee, Jo Woon Chong, Jin Seek Choi","doi":"10.4218/etr2.70073","DOIUrl":"https://doi.org/10.4218/etr2.70073","url":null,"abstract":"<p>The rapid advancement of artificial intelligence (AI) technologies, along with the accelerated development of smart cities, has created unprecedented opportunities to enhance the efficiency and sustainability of urban environments. The 2022 special issue on smart cities focused on foundational machine learning (ML), Internet of Things (IoT)-driven analytics, and optimization techniques to improve traffic management, public safety, and urban infrastructure sharing [<span>1</span>]. Smart cities go beyond basic digitalization by incorporating data-driven decision-making and advanced automation to improve citizens' quality of life, reduce energy consumption, and address urban challenges such as traffic congestion and environmental degradation. Since then, emerging trends such as metaverse integration, privacy-preserving AI, edge AI, and LiDAR-based autonomous navigation have reshaped smart city applications. At the same time, these advancements pose complex challenges that require integrated technological and governance strategies.</p><p>Recent research on smart city development has focused on integrating high-level intelligence into urban systems and analyzing the economic ripple effects of these technologies on wider industrial ecosystems. Massive datasets generated in real time through sensors and IoT devices provide critical insights into traffic flows, environmental conditions, energy usage, and patterns of human activity. However, the transformation of these large-scale datasets into actionable intelligence remains a significant technical and managerial challenge.</p><p>To address these challenges, the convergence of AI and digital twin technologies has emerged as a promising solution. This convergence enables the integration and analysis of heterogeneous data sources, offering predictive insights and real-time decision-making capabilities that enhance operational efficiency, optimize resource utilization, and strengthen the sustainability and resilience of urban systems. The applications of AI in smart cities span a wide range of domains, including anomaly detection, traffic flow analysis, predictive maintenance, energy optimization, and public safety. When combined with robust data privacy and security frameworks, AI can support transparent and accountable governance and safeguard personal information.</p><p>Digital twins are dynamic virtual models of physical urban environments that enable simulation-based policy testing and proactive problem resolution. These models allow city administrators to simulate infrastructure scenarios, forecast outcomes, and manage assets. When augmented with AI, digital twins achieve more precise feature extraction, automated fault detection, and scalable predictive analysis, which in turn yield cost savings and operational improvements. Furthermore, the fusion of digital twin technologies with metaverse platforms creates immersive and interactive environments to enable citizens to engage and contribute to ur","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"793-796"},"PeriodicalIF":1.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ELiOT: End-to-end LiDAR odometry with transformers harnessing real-world, simulated, and digital twin","authors":"Daegyu Lee,&nbsp;Hyunwoo Nam,&nbsp;Insung Jang,&nbsp;David Hyunchul Shim","doi":"10.4218/etrij.2025-0011","DOIUrl":"https://doi.org/10.4218/etrij.2025-0011","url":null,"abstract":"<p>The development of smart cities depends on intelligent systems that integrate data from diverse environments. In this work, we present <b>ELiOT</b>, an end-to-end LiDAR odometry framework with transformer architecture designed to utilize real-world data, simulations, and digital twins. ELiOT leverages high-fidelity simulators and digital twin environments to enable sim-to-real applications, training on the real-world KITTI odometry dataset while benefiting from simulated data for improved generalization. Our self-attention-based flow embedding network eliminates the need for traditional 3D-2D projections by implicitly modeling motion from sequential LiDAR scans. The framework incorporates a 3D transformer encoder-decoder to extract rich geometric and semantic features. By integrating digital twin environments and simulated data into the training process, ELiOT bridges the gap between simulation and real-world applications, offering robust and scalable solutions for urban navigation challenges. This work underscores the potential of combining real-world and virtual data to advance LiDAR odometry and highlights its role for the future smart cities.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"815-829"},"PeriodicalIF":1.6,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2025-0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DOROS: A multilevel traffic dataset for dynamic urban scene understanding","authors":"Jungyu Kang,&nbsp;Kyoung-Wook Min,&nbsp;Sangyoun Lee","doi":"10.4218/etrij.2025-0063","DOIUrl":"https://doi.org/10.4218/etrij.2025-0063","url":null,"abstract":"<p>Advancements in autonomous vehicles and smart traffic systems require vision datasets capable of capturing complex interactions and dynamic behaviors in real-world urban environments. Although datasets such as COCO, Cityscapes, and ROAD have advanced object detection, segmentation, and action recognition, they often treat scene elements in isolation, thereby limiting their use for comprehensive understanding. This paper presents DOROS, a dataset with multilevel annotations across <i>Agent</i>, <i>Location</i>, and <i>Behavior</i> categories. DOROS is designed to support compositional reasoning under diverse traffic conditions. An annotation pipeline combining foundation models with structured human refinement ensures consistent, high-quality supervision. To support structured evaluation, we introduce the <i>Combined mAP</i>(<i>mask</i>) metric, which assesses instance segmentation under strict category-level label matching while mitigating the effects of class imbalance. Extensive experiments, including ablation studies and transformer-based baselines, validate DOROS as a resource for structured scene understanding in complex traffic scenarios. The dataset and code will be released upon publication.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"830-840"},"PeriodicalIF":1.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2025-0063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-speed and precise virtual try-on with two-stage semantic segmentation and a latent consistency model for optimized diffusion processes","authors":"Sangyeop Baek,&nbsp;Jong Taek Lee","doi":"10.4218/etrij.2024-0592","DOIUrl":"https://doi.org/10.4218/etrij.2024-0592","url":null,"abstract":"<p>This work tests the hypothesis that the primary bottleneck for visual quality in virtual try-on (VTON) systems is the precision of input segmentation masks, rather than generative capability. VTON technology empowers users to dress digital models in desired clothing items virtually. Conventional VTON models rely on segmentation models to isolate clothing regions and diffusion models to synthesize complete VTON images. This paper introduces high-speed and precise VTON (HSP-VTON) as a framework that uniquely combines refined two-stage semantic segmentation for enhanced accuracy with a latent consistency model to accelerate the diffusion-based image generation process. The synergistic integration of these components for VTON addresses critical challenges in both precision and speed. Experimental results on the ATR dataset demonstrate a 2.8% improvement in mean intersection over union compared with existing methods. Furthermore, HSP-VTON achieves superior performance on the VITON-HD dataset, outperforming state-of-the-art VTON models. The latent consistency model also reduces the number of inference steps, leading to substantial time savings without compromising image quality.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"881-892"},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring GPU sharing techniques for edge AI smart city applications","authors":"Sooyeon Woo,&nbsp;Jihwan Yeo,&nbsp;Jinhong Kim,&nbsp;Kyungwoon Lee","doi":"10.4218/etrij.2025-0065","DOIUrl":"https://doi.org/10.4218/etrij.2025-0065","url":null,"abstract":"<p>The growing adoption of edge AI in smart city applications such as traffic management, surveillance, and environmental monitoring necessitates efficient computational strategies to satisfy the requirements for low latency and high accuracy. This study investigated GPU sharing techniques to improve resource utilization and throughput when running multiple AI applications simultaneously on edge devices. Using the NVIDIA Jetson AGX Orin platform and object detection workloads with the YOLOv8 model, we explored the performance tradeoffs of the threading and multiprocessing approaches. Our findings reveal distinct advantages and limitations. Threading minimizes memory usage by sharing CUDA contexts, whereas multiprocessing achieves higher GPU utilization and shorter inference times by leveraging independent CUDA contexts. However, scalability challenges arise from resource contention and synchronization overheads. This study provides insights into optimizing GPU sharing for edge AI applications, highlighting key tradeoffs and opportunities for enhancing performance in resource-constrained environments.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"855-864"},"PeriodicalIF":1.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2025-0065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Mahalanobis distance-based lazy learning method for fault detection in high-dimensional processes","authors":"Jungwon Yu,&nbsp;Kwang-Ju Kim,&nbsp;In-Su Jang","doi":"10.4218/etrij.2024-0253","DOIUrl":"https://doi.org/10.4218/etrij.2024-0253","url":null,"abstract":"<p>When using lazy learners based on the Mahalanobis distance (MD) function for process fault detection (FD), due to the curse of dimensionality, type I errors can increase significantly as the number of process variables increases. In high-dimensional data spaces, certain regions exist in which data samples are sparsely distributed. From the perspective of dense regions, the outlierness (i.e., degree of being statistical outliers) of samples in sparse regions increases as the data dimensions increase, leading to unstable estimations of classical covariance matrices for calculating MD function values. To solve this problem, a lazy learning method is proposed based on a robust MD function, where robust covariance matrices are estimated using a minimum covariance determinant method. Here, <i>k</i>-nearest neighbors and local outlier factor are employed as baseline learners. The proposed method can be applied to all types of lazy learning techniques. To verify FD performance, the proposed method is applied to two benchmark processes. The experimental results show that the proposed method can perform FD on very high-dimensional processes successfully without rapid increases in type I errors.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"865-880"},"PeriodicalIF":1.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in intelligent sensor-based customized management technologies for sewer infrastructures","authors":"Mi-Seon Kang,&nbsp;Hyan-Su Bae,&nbsp;Kyoungoh Lee,&nbsp;Ki-Young Moon,&nbsp;Jung-Won Yu,&nbsp;Jin-Hong Kim,&nbsp;Doo-Sik Kim,&nbsp;Yun-Jeong Song,&nbsp;Je-Youn Dong,&nbsp;Kwang-Ju Kim,&nbsp;Sang-Soo Baek","doi":"10.4218/etrij.2024-0601","DOIUrl":"https://doi.org/10.4218/etrij.2024-0601","url":null,"abstract":"<p>Sewer infrastructure management is essential for public health, environmental protection, and urban stability. Aging networks and the impacts of climate change emphasize the need for advanced management solutions. Traditional methods, such as periodic inspections and reactive maintenance, are insufficient to address the complexities of modern sewer systems. This study surveys intelligent-sensor-based management technologies aimed at improving sewer infrastructure. Key technologies include Internet-of-Things-driven data collection, machine learning and deep learning analytics, cloud and edge computing, and autonomous robotics. Based on case studies from South Korea, Germany, Japan, and the United States, the practical benefits of these technologies were explored, including real-time monitoring and predictive maintenance, as well as challenges such as sensor durability, robotic mobility, and data analysis limitations. Rather than proposing solutions, this study evaluates the current state of these technologies and identifies gaps that require further research and innovation. It provides a comprehensive overview that serves as a valuable resource for researchers and practitioners and contributes to the advancement of sustainable and efficient sewer management systems.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"797-814"},"PeriodicalIF":1.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving labeling-free occupancy counting sensor based on ToF camera and clustering","authors":"Jaeik Jeong,&nbsp;Wan-Ki Park","doi":"10.4218/etrij.2025-0022","DOIUrl":"https://doi.org/10.4218/etrij.2025-0022","url":null,"abstract":"<p>Occupancy detection systems are crucial for optimizing energy efficiency in smart cities and buildings but often face privacy and data dependency challenges. YOLO (you only look once), a widely used real-time detection framework, relies on identifiable image data and labeled datasets. This study proposes a privacy-preserving, labeling-free occupancy sensor using a time-of-flight (ToF) camera, and a clustering algorithm. Positioned above doorways, the ToF camera captures depth data that inherently protect privacy by avoiding identifiable information. Using the mean shift clustering algorithm, it performs real-time detection and tracking without labeled data, generating bounding boxes for movement analysis. Unlike traditional ToF-based or unsupervised methods, the proposed system adapts dynamically to varying occupant behaviors and environmental conditions for robust real-time detection. Experimental results show that the proposed method achieves over 90% accuracy in standard single-entry and exit scenarios. By addressing existing limitations, it offers a data-efficient, privacy-sensitive solution for building digital twins in energy optimization and resource management.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"841-854"},"PeriodicalIF":1.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2025-0022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic tile-map generation for crack-free rendering of large-scale terrain data","authors":"Cheonin Oh,&nbsp;Ahyun Lee","doi":"10.4218/etrij.2024-0496","DOIUrl":"https://doi.org/10.4218/etrij.2024-0496","url":null,"abstract":"<p>Three-dimensional (3D) geospatial technologies are essential in urban digital twins, smart cities, and metaverse. Rendering large-scale terrain data, often exceeding tens of terabytes, presents challenges. While planetary-scale platforms, like Google Earth and Cesium stream data, the streaming of data and the use of regular grid-type digital elevation models lead to cracks among tiles with different levels of detail. This paper proposes a novel dynamic tile-map generation method to eliminate these cracks. Unlike existing methods, our approach leverages tile subindex information to efficiently construct a tile adjacency map, significant reducing the search space for neighboring tiles and eliminating the need for prior knowledge of the terrain tile structure. Furthermore, our approach is robust to data loss, mitigating cracks caused by missing or incomplete tiles. Compared with existing root-down search methods, our method reduces processing time by 1–5 ms per frame and decreases the number of tile-to-tile links by a factor of 3–5, as demonstrated by experimental results.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 5","pages":"970-982"},"PeriodicalIF":1.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on defense and disaster response technologies","authors":"Hea Sook Park,&nbsp;Jong-Moon Chung,&nbsp;Moosung Park,&nbsp;Youngok Kim,&nbsp;Ji-Bum Chung,&nbsp;Sangtae Ha,&nbsp;Yong-Yuk Won","doi":"10.4218/etr2.70040","DOIUrl":"https://doi.org/10.4218/etr2.70040","url":null,"abstract":"<p>In today's technological landscape, the rapid and widespread adoption of new technologies is crucial to enhance the capabilities, robustness, and efficiency of military defense and disaster response operations. Technologies such as artificial intelligence, mobile communication, and the Internet of Things have enriched battlefield communication, surveillance, tactical decision-making, and early warning systems. This trend is common across various fields, including disaster response technologies, and has led to considerable improvements in disaster prediction, mitigation, response, and recovery applications.</p><p>The emergence of new technologies has resulted in dramatic changes in the operational environment. For example, the increasing diversity of connections between combat/rescue equipment, weaponry, and operational headquarters imposes complex communication requirements related to availability, reliability, and latency, as well as the need for safe processing of unprecedented volumes of data. Conversely, responses to disasters must consider their potential impacts, including their high frequency, widespread damage, and global scale. Additionally, preemptive interventions that allow for accurate forecasting of disasters are essential for modern disaster response. Overall, myriad factors collectively contribute to the complexity of developing efficient solutions for military defense and disaster response applications.</p><p>The Electronics and Telecommunications Research Institute (ETRI) Journal is a peer-reviewed open-access journal launched in 1993 and published bimonthly by ETRI (Republic of Korea), aiming to promote worldwide academic exchange in the fields of information, telecommunications, and electronics. This special issue explores recent research trends in the technological advances driving the digital transformation of military defense and disaster response systems. It presents notable, cutting-edge studies aimed at improving the efficiency, safety, and real-time responsiveness of these critical domains. Given the central role of technologies such as virtual training, robotic navigation, drone countermeasures, and secure communications in the modernization of defense operations, the contributions in this special issue offer valuable insights into the future direction of digitalized military defense and disaster response strategies. Accordingly, we have selected eight critical papers on three aspects of military defense and disaster response technology for this special issue. A brief review regarding commitments for this special issue follows.</p><p>The first invited paper [<span>1</span>], entitled “Next-generation wireless communication technologies for improved disaster response and management” by Song et al., introduces next-generation wireless communication technologies that can improve disaster response and management. This study proposes an integrated disaster-response communication framework with the potential to achieve ul","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 3","pages":"371-374"},"PeriodicalIF":1.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}