ETRI Journal最新文献

The development of smart cities depends on intelligent systems that integrate data from diverse environments. In this work, we present ELiOT, 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.

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 Agent, Location, and Behavior 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 Combined mAP(mask) 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.

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.

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.

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, k-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.

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.

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.

Wireless power transfer (WPT) technology offers a promising solution for powering electronic devices without a physical connection. However, achieving high power-transfer efficiency (PTE) while minimizing electromagnetic interference (EMI) remains a critical challenge, especially for flexible and unrestricted device positioning. This study explores the use of coil rotation and phase-shift control to optimize the PTE by adjusting the transmitter (TX) coil orientation and phase shifts. Analytical expressions based on the Neumann formula are employed to derive the mutual inductance between two coaxially aligned coils with varying receiver (RX) coil orientations. A prototype magnetic resonance WPT (MR-WPT) system is developed to validate the feasibility of the proposed efficiency enhancement methods. The simulation and experimental results demonstrate that optimizing the TX coil phase-shift and coil-rotation angle can maximize the RX voltage and improve the PTE by approximately 30%, while also reducing EMI levels.

Under ubiquitous smart environments, the convergence of mobile ad hoc networks (MANET) and infrastructure networks enables new communication patterns. In this hybrid MANET (H-MANET) platform, gateways critically affect network performance. We address the gateway selection problem by proposing a novel decision mechanism that considers multiple metrics. Using a multi-criteria decision method and bargaining game theory, we develop a novel gateway selection algorithm. First, routing paths are discovered. Second, decision criteria—route distance, queue length, connectivity degree, and link complexity—are evaluated. Third, each gateway's adaptability is assessed through the combination of Kalai–Smorodinsky and Nash bargaining solutions. Finally, the most adaptable gateway is selected for data transmission. Our main contribution is integrating both bargaining solutions' concepts for multi-criteria-based gateway selection. Simulation results demonstrate the performance benefits of our proposed approach over existing methods. The proposed method can also address other real-world multi-criteria decision problems.