ETRI Journal最新文献

{"title":"Correction to “CRFNet: Context ReFinement Network used for semantic segmentation”","authors":"","doi":"10.4218/etr2.70084","DOIUrl":"https://doi.org/10.4218/etr2.70084","url":null,"abstract":"<p>\u0000 <span>T. An</span>, <span>J. Kang</span>, <span>D. Choi</span>, and <span>K.-W. Min</span>, <span>CRFNet: Context ReFinement Network used for semantic segmentation</span>, <i>ETRI Journal</i> <span>45</span> (<span>2023</span>), <span>822</span>–<span>835</span>. DOI 10.4218/etrij.2023-0017</p><p>In the article entitled “CRFNet: Context ReFinement Network used for semantic segmentation,” the authors would like to correct the author name of their article. “Taeghyun An” should be corrected to “Taeg-Hyun An.” The author's name has also been corrected in the online article.</p><p>The authors would like to apologize for the inconvenience caused.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216869","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":"RETRACTION: TransField: Improving Transformer Efficiency and Performance Through Conditional Random Fields","authors":"","doi":"10.4218/etr2.70085","DOIUrl":"https://doi.org/10.4218/etr2.70085","url":null,"abstract":"<p><b>RETRACTION</b>: H. Nguyen. Van, N. Phan Minh, A. Ho Quoc Thien, and N. Nguyen Bao Thuc, “TransField: Improving Transformer Efficiency and Performance Through Conditional Random Fields,” <i>ETRI Journal</i> (Early View): https://doi.org/10.4218/etrij.2024-0491.</p><p>The above article, published online on 11 December 2024 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors; the journal Editor-in-Chief, Sungwon Yi, and John Wiley &amp; Sons Australia, Ltd. The retraction has been agreed due to significant unattributed overlap between this article and a previously published article.[1]</p><p><b>REFERENCE</b></p><p>[1] H. Wu and K. Tu, “Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation,” Findings of the Association for Computational Linguistics: ACL 2023 (2023): 7613–7636, https://doi.org/10.18653/v1/2023.findings-acl.482.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217512","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":"GL-WF: A lightweight graph learning model for website fingerprinting attacks","authors":"Bo Gao,&nbsp;Weiwei Liu,&nbsp;Guangjie Liu,&nbsp;Fengyuan Nie,&nbsp;Jianan Huang,&nbsp;Junzhe Zhang","doi":"10.4218/etrij.2024-0197","DOIUrl":"https://doi.org/10.4218/etrij.2024-0197","url":null,"abstract":"<p>Website fingerprinting attacks that analyze clients' browsing preferences are an important information source for maintaining cybersecurity and improving big data utilization in terms of service quality. Many scholars have studied graph learning because of its powerful learning and inferential capabilities on unstructured network traffic. However, the extensive computing resource requirements of graph neural networks limit their application to large-scale and automated scenarios. In this paper, we propose a lightweight graph learning method for website fingerprinting attacks called GL-WF. We designed a four-level heterogeneous spatiotemporal graph using a multimodal representation of website browsing traffic. We then utilized a graph sampling algorithm to refine the structure of the graph. Finally, a well-designed graph isomorphism network was used to extract the graph topology for the traffic classifiers. The proposed GL-WF model, which was evaluated on over 3.6 million website flows, was found to provide high accuracy and efficiency.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 1","pages":"56-68"},"PeriodicalIF":1.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224521","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":"Noncoherent massive SIMO system based on Array partitioning","authors":"Lin Zheng,&nbsp;Huan Meng,&nbsp;Jian-Mei Chen,&nbsp;Guang-Wei Li,&nbsp;Chao Yang","doi":"10.4218/etrij.2024-0435","DOIUrl":"https://doi.org/10.4218/etrij.2024-0435","url":null,"abstract":"<p>This study investigated a noncoherent multiuser single-input multiple-output (SIMO) uplink transmission system based on large-scale array partitioning. In practical cases, limitations on the number of array elements and noncoherent detection make it difficult to resist nonlinear mutual interference and implement space division multiple access. In this study, based on energy difference detection (EDD), an array partitioning scheme and amplitude constellation were combined to improve the capacity of a massive SIMO system. EDD can suppress nonlinear mutual interference in energy-based SIMO, while the joint partitioning and amplitude constellation achieve both large-scale array diversity gain and spatial multi-access effects. Multi-partition detection can also be applied to cell-free SIMO scenarios. The complexity and overhead of the proposed scheme are tractable. Theoretical analysis demonstrates the feasibility of our scheme, and simulations reveal that the array partitioning scheme offers higher system capacity compared with non-partitioning schemes.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 1","pages":"42-55"},"PeriodicalIF":1.6,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216721","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 smart city technologies and services based on AI for digital twin applications","authors":"Byoung Chul Ko,&nbsp;Ming-Ching Chang,&nbsp;Jong Taek Lee,&nbsp;Jo Woon Chong,&nbsp;Jin Seek Choi","doi":"10.4218/etr2.70073","DOIUrl":"https://doi.org/10.4218/etr2.70073","url":null,"abstract":"&lt;p&gt;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 [&lt;span&gt;1&lt;/span&gt;]. 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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 2","pages":"272-286"},"PeriodicalIF":1.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148083455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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}