ETRI Journal最新文献

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.

Accurate GDP quarter-on-quarter (QoQ) nowcasting is crucial for timely economic decisions and policy formulation, requiring models that effectively capture complex economic dynamics. Traditional methods, like dynamic factor models, have been widely used but face two key limitations: (i) limited representation of latent factors, which inadequately capture economic dynamics, and (ii) modest nowcasting performance due to reliance on simple regression-based estimations. This paper introduces a hybrid approach that utilizes variational autoencoders to extract latent factors more effectively, enhancing factor representation. Simultaneously, a transformer encoder improves nowcasting accuracy by capturing intricate relationships among these factors. Our model is further augmented with uncertainty projection, auxiliary input, and cross-attention modules, enhancing both accuracy and interpretability. Experimental results show that our approach significantly outperforms traditional models across key metrics. This paper highlights the advantages of integrating advanced deep learning techniques into GDP QoQ economic forecasting, with the potential to influence future research and set a new standard for accuracy in GDP nowcasting.

Thrombocytopenia is a common complication among critically ill patients. To enable early prediction, we conducted a retrospective study using five machine learning (ML) models developed with a sequence embedding approach that integrates temporal medication and diagnostic data. Models were trained on the MIMIC-IV database and evaluated on the eICU database. We propose a novel sequence feature fusion method combining explicit and implicit features with embeddings for ICD codes and drug sequences to capture complex interactions. To our knowledge, this is the first study to make continuous predictions for ICU patients until thrombocytopenia onset. Model performance was assessed using AUC; t-SNE and SHAP were used to evaluate feature importance. XGBoost with sequence feature fusion performed best, achieving AUCs of 0.80, 0.85, and 0.92 at ICU admission, and 72 h and 24 h before onset, respectively. Platelet count, phosphate, and lactate were the top predictors. These findings demonstrate that ML models with sequence embeddings can effectively predict thrombocytopenia by capturing temporal patterns in patient data.

Texts are widely used in natural language processing. However, such applications are vulnerable to adversarial attacks. Existing research attempts to artificially add semantically meaningless word-, character-, or sentence-level perturbations, which compromise the syntax and consistency of texts. However, they fail to ensure high-quality outputs. Therefore, we propose an attack model for generating adversarial samples using policy gradients and a generative adversarial network. In our model, first, a Seq2Seq encoder is used to generate sentences, mapping discrete text data into continuous hidden space vectors and then transforming them into adversarial text samples. Second, to emphasize semantics, we compute the cosine similarity or BERT-based semantic similarity between the original and adversarial texts for reward calculation. Finally, a policy gradient is applied to optimize the parameters. Experiments show that, while maintaining a semantic similarity above 0.8, our BERT-based method reduces classification accuracy by 51.77% on the DBpedia dataset. Our cosine similarity-based method requires only one-third to one-half the runtime of the baseline approach.

Reliable and stable recording of ECG signals during dynamic movements is crucial for modern clinical cardiology and future healthcare applications. However, high electrical impedance and nonconformal interface between soft tissues and conventional ECG electrodes in dynamic environments continue to hinder their widespread use in portable ECG applications. This study presents the development and application of a wireless ECG monitoring device based on a soft and conductive graphene oxide (GO) hydrogel, designed to overcome these limitations. The GO hydrogel electrodes, consisting of chemically exfoliated GO flakes as a filler material and water-soluble polyvinyl alcohol (PVA) as the polymer backbone, demonstrate low electrical impedance and a reliable interface for dynamic ECG acquisition. We developed a limb-mounted ECG monitoring system integrating these soft and conductive GO/PVA hydrogel electrodes with communication modules. This system was designed to capture raw ECG signals during both resting and walking states. The results indicate that the ECG signals recorded with the GO/PVA hydrogel patch electrodes more accurately represent R-peaks and other ECG patterns compared with those obtained with commercial ECG monitoring electrodes, particularly under conditions involving significant movement.

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.

Coded caching reduces the communication load substantially, exploiting the caches of end devices to generate multicast opportunities during the transmission phase. To address user-request privacy, we propose a decentralized coding caching method that focuses on protecting user privacy. This method involves creating file subpackages for users to cache linear combinations of files. We also expand the key scheme for decentralized situations, ensuring that files shared among users do not exceed each user's cache size. We make sure that the unencoded part of each packet in the user cache is larger than the size of the cached file after being cut, determining the range of values for the file allocation coefficient, θ. With fixed N and M, we can calculate that the load is a convex function of θ. Through mathematical analysis, we can determine the worst case load scenario. Subsequent simulation results unequivocally demonstrate the capability of the proposed scheme to fulfill any file request from users, all while achieving a communication load comparable to that of an enhanced distributed nonprivate cache scheme.