ETRI Journal最新文献

Recognizing workers' locations and automatically assigning warnings are crucial for preventing industrial injuries. However, existing warning systems are unsuitable for industrial environments as they rely solely on images or insufficiently leverage multi-sensor inputs. Their distance- or plane-based warning assignment strategies are limited when managing 3D spatial environments. To address these issues, we propose DeepMonitor, a novel industrial automatic warning system that incorporates a prior knowledge-based 2D-to-3D conversion and a multi-sensor, space-based warning assignment strategy. We use a mature 2D object detector to avoid the need for 3D training datasets and apply prior knowledge with multi-sensors to reduce the search space for workers' locations. To manage 3D spatial environments, warnings are assigned based on the overlap ratios between workers and zones, defined as 3D bounding boxes. We have constructed a novel dataset for industrial safety and have tested our system against existing approaches. Results demonstrate our system's superiority, achieving an F1-score 16.7% and 24.7% higher than those of the image-only and camera-geometry systems, respectively.

This paper investigates securing nonorthogonal multiple access (NOMA) by leveraging receivers equipped with full-duplex (FD) communication and energy harvesting (EH) capabilities. These receivers decode their intended information while simultaneously jamming eavesdroppers using harvested energy, aiming to achieve high security, energy efficiency, and spectral efficiency. The study analyzes the proposed NOMA scheme with EH-assisted FD receivers across various key performance metrics for a quick performance assessment. The proposed analysis is validated through simulations, which demonstrate the influence of the proposed model on multiple specifications. Furthermore, the proposed model is shown to be considerably more secure than the conventional orthogonal multiple access (OMA) with EH-assisted FD receivers, revealing the advantages of NOMA over OMA.

This paper presents a radio-frequency (RF) signal modeling technology that builds a fingerprinting database for indoor localization quickly and accurately. Fingerprinting-based localization technology uses location-specific signal characteristics as a database; therefore, it is less sensitive to multipath problems. The proposed approach predicts signal propagation paths and calculates attenuation based on an indoor map, reducing infrastructure installation and data collection time. Because the indoor map lacks accurate information about all structures, the modeling results contain errors when compared to measurements. To address this, measurements from a partial area improve modeling accuracy by accounting for received signal strength changes caused by indoor structures. In experiments with seven beacons, the proposed database construction method achieves an average error of 5.16 dBm and a localization error of 1.61 m, comparable to the 1.14-m error in measurement-based databases, while reducing database construction time by 41.06%. These results demonstrate the effectiveness of the proposed technology in rapidly and accurately building databases for indoor localization.

The Internet of Things (IoT) is an intelligent network paradigm created by interconnected device networks. Although the importance of IoT systems has increased in various applications, the increasing number of connected devices has made security even more critical. This study presents the ROUT-4-2023 dataset, which represents a step toward the security of IoT networks. This dataset simulates potential attacks on RPL-based IoT networks and provides a new platform for researchers in this field. Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). The results show that the RaD-FFNN model has high accuracy, precision, and retrieval rates, indicating that it can be used as an effective tool for the security of IoT networks. This study contributes to the protection of IoT networks from potential attacks by presenting ROUT-4-2023 and RaD-FFNN models, which will lead to further research on IoT security.

Unmanned aerial vehicles (UAVs) are highly mobile and easily deployable devices that have become an important component of wireless communication countermeasures. Covert communication, the main method used to ensure wireless communication security, has been extensively studied in recent years. However, existing research primarily uses UAVs as auxiliary tools for covert communications, to improve communication performance, ignoring situations in which the detector utilizes UAVs for interference suppression. In this study, we propose a UAV-assisted jamming detection covert communication game model. Specifically, the UAV actively transmits noise to Alice's transmission channels to disrupt covert transmission when Willie detects a covert communication transmission. Furthermore, we analyze the adversarial process between the detector and Alice under UAV-assisted jamming based on game theory, theoretically verify the conditions for the existence of a Nash equilibrium, and formulate optimal strategies for both sides.

We address the challenge of jointly representing uplink (UL) and downlink (DL) channels for a massive multiple-input multiple-output satellite system. We employ dictionary learning for sparse representation with the goal of minimizing the number of UL/DL pilots and improving accuracy. Additionally, by considering the angular reciprocity, a common dictionary support can be established to enhance the performance. However, what type of dictionary model is suited for UL/DL channel representation remains an unknown field. Previous research has utilized predefined dictionaries, such as DFT or ODFT bases, which are unable to adapt to dynamic scenarios. Training dictionaries have demonstrated the potential to significantly improve accuracy; however, a lack of analysis regarding dictionary constraints exists. To address this issue, we analyze the conditional constraints of the dictionary for joint UL/DL channel representation, aiming to quantify the maximum boundary while proposing a constrained dictionary learning algorithm with singular value decomposition to obtain an effective representation and conduct an adaptability analysis in dynamic satellite communication scenarios.

Several peak-to-average power ratio (PAPR) reduction methods have been used in orthogonal frequency division multiplexing (OFDM) applications. Among the available methods, partial transmit sequence (PTS) is an efficient PAPR reduction method but can be computationally expensive while determining optimal phase factors (OPFs). Therefore, an optimization algorithm, namely, the improved salp swarm optimization algorithm (ISSA), is incorporated with the PTS to reduce the PAPR of the OFDM signals with limited computational cost. The ISSA includes a dynamic weight element and Lévy flight process to improve the global exploration ability of the optimization algorithm and to control the global and local search ability of the population with a better convergence rate. Three evaluation measures, namely, the complementary cumulative distribution function (CCDF), bit error rate (BER), and symbol error rate (SER), demonstrate the efficacy of the PTS-ISSA model, which achieves a lower PAPR of 3.47 dB and is superior to other optimization algorithms using the PTS method.

The prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three-dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real-time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.

Existing spatial feature recognition and layout methods primarily identify spatial components manually, which is time-consuming and inefficient, and the constraint relationship between objects in space can be difficult to observe. Consequently, this study introduces an advanced spatial feature recognition and layout methodology employing enhanced CenterNet and LSTM (Long Short-Term Memory) frameworks, which is bifurcated into two major components—first, HCenterNet-based feature recognition enhances feature extraction through an attention mechanism and feature fusion technology, refining the identification of small targets within complex background areas; second, a GA-BiLSTM (Genetic Algorithm - Bidirectional LSTM)-based spatial layout model uses a bidirectional LSTM network optimized with a genetic algorithm (GA), aimed at fine-tuning the network parameters to yield more accurate spatial layouts. Experiments verified that compared with the CenterNet model, the recognition performance of the proposed HCenterNet-DIoU model improved by 7.44%. Moreover, the GA-BiLSTM model improved the overall layout accuracy by 10.08% compared with the LSTM model. Time cost analysis also confirmed that the proposed model could meet the real-time requirements.