ETRI Journal最新文献

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.

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.

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.

The first invited paper [1], 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

In typical scenarios in which parts of the face are occluded by objects such as masks, sunglasses, hats, or scarves, previous studies have focused on analyzing how the recognition rate varies depending on the degree and location of occlusion, and several approaches have been proposed for detecting occluded facial areas and recognizing partial faces. However, in this study, we aim to determine which facial regions are the most crucial for occluded face recognition. Identifying in advance which features of a partial face influence recognition performance the most could help prediction and enhance recognition accuracy. To evaluate performance based on the direction and position of the occluded areas, three common deep learning-based face recognition models (ArcFace, MobileFaceNet, and iResNet) are compared using the well-known public face datasets, LFW, CFP-FP, AgeDB, and IJB-C. Extensive experiments confirmed that the eye-centered horizontal and nose-centered vertical regions are the most critical for face recognition. When recognition was performed using only these two regions as input, the models achieved high recognition accuracy despite the absence of other facial features.

The National Fire Agency (NFA) and National Police Agency (NPA) have defined risk levels based on the severity of disasters. Risk-level data possess the characteristics of ordinal data such as NPA's Emergency Service Response Code (ESRC) data, which are classified based on their magnitudes (from C0 to C4). In this study, we propose a distance mean-square (DiMS) loss function to improve the accuracy of ordinal data classification. The DiMS loss function calculates loss values based on the distances between the predicted and true labels: value distances (commonly used in regression analysis for magnitude data) and probability distances (typically used in classification analysis). Therefore, the DiMS loss function contributes to improved accuracy when classifying ordinal data, such as ESRC. In addition, using the DiMS loss function, we achieved state-of-the-art performance in classifying the SST-5 data, which is a representative ordinal dataset. The DiMS loss function for ordinal classification enabled accurate risk recognition. Thus, accurate risk recognition using the DiMS loss function enhances disaster response.

Recently, numerous studies have been conducted to incorporate the knowledge of massive text corpora into speech recognition via text to speech (TTS). However, the distribution mismatch between synthetic and real speech has always been an issue. In this paper, we analyzed how these mismatches affect the acoustic and linguistic aspects of automatic speech recognition (ASR) performance. For acoustics, we divided the acoustic mismatch into TTS-related and non-TTS-related and analyzed how each acoustic mismatch affected ASR performance. Next, from a linguistic perspective, we experimented to determine how synthetic speech from a large text corpus affects the performance of speech recognition in various domains. The experimental results show that (i) substitution errors, which are the bulk of the recognition errors in ASR trained on synthetic speech data, are affected by the prosody mismatch between synthetic and real speech; (ii) pretraining ASR with synthetic speech data first and performing transfer learning with real speech outperformed training in the reverse order; and (iii) pretraining with a large amount of synthetic speech improves performance further in language model shallow fusion.

Edge computing deploys computing resources to the network edge to diminish task processing delays and power consumption. However, in traffic video surveillance systems, vehicular movement can lead to service interruptions. Moreover, the low credibility of the edge systems affects the success rate of edge offloading. To address these issues, we propose a secure task offloading scheme for traffic video surveillance. This scheme comprehensively evaluates trust values by integrating direct and indirect trust values, selecting nodes based on predefined trust thresholds, and achieving secure offloading and migration with short delays and energy consumption. To model the service migration problem, we adopted a Markov decision process-based approach and employed a $�Q$-learning algorithm to determine the optimal solution, thereby establishing an effective migration path. Simulation results demonstrate that the proposed scheme yields higher comprehensive trust values and improved reliability. Furthermore, while ensuring seamless task migration, the migration success rate exceeded 90%, with the energy consumption reduced by more than 9.8%.

The complexity of modern battlefields demands advanced military training systems that prepare forces for realistic scenarios. Even though traditional training methods are effective, they are costly and time consuming and are associated with safety risks. Virtual training systems offer a safer and more cost-effective alternative; however, current solutions often compromise realism because of the need for multiple sensors and wearable devices that can diminish immersion. To address these limitations, we propose a marker-based, adaptive, virtual military training system (MAVMTS) that enhances realism by using a minimal set of fiducial markers and multiview cameras to estimate the trainee's posture and weapon orientation without cumbersome wearables. The system integrates action recognition to generate responsive virtual adversaries, thereby creating dynamic and immersive training environments. MAVMTS reduces considerably the equipment burden and enhances the realism of virtual military training, thereby offering a more effective solution for preparing personnel for modern warfare.

This paper introduces next-generation wireless communication technologies applicable to disaster response. With recent technological advances—such as 5G new radio vehicle-to-everything (NR-V2X)—real-time communication, decision-making, and emergency operations can be enhanced through ultralow latency and high-speed transmission. Additionally, several technologies—such as autonomous mobility, drones, and Internet of Things (IoT)-based sensor networks—can improve rescue operations and mitigate human risks during disasters. Moreover, future communication technologies—including platooning, digital twins, and cell on wheels (COW)—can support situational awareness and rapid decision-making in disaster environments. Consequently, we propose industrial applications and standardization strategies to effectively utilize next-generation communication technologies for disaster response. Lastly, we analyze current wireless technologies and suggest future research directions for optimizing disaster preparedness and response.

Traditional navigation methods for mobile robots face significant challenges in dynamic environments, including local minima avoidance and efficient path planning. This paper introduces the semantic potential field (SPF) method, which synergizes geometric and semantic data using a semantic grid map to improve navigation efficiency and adaptability. The key features of the SPF method include (i) a semantic grid map combining light detection and ranging (LiDAR) and camera data to distinguish static and dynamic obstacles and (ii) a dynamically modulated potential field incorporating semantic weights for adaptive path planning and obstacle avoidance. The experimental results demonstrate that the SPF method significantly reduces the travel distance and computation time compared with those of traditional methods, ensuring robust navigation in diverse environments. By addressing the limitations in real-time navigation systems, the SPF represents a significant advancement in mobile robot path planning, with promising applications in disaster response, autonomous logistics, and defense.

As 5G technology expands globally, the demand for secure and reliable military communication is growing significantly. This study examines 5G military network architectures that integrate commercial 5G elements, ranging from isolated to shared configurations, to balance security, cost, and coverage. We propose a network leveraging commercial next-generation Node B (gNodeB or gNB) with a military Public Land Mobile Network (PLMN) ID, ensuring extensive coverage, robust security, and cost efficiency. To enhance secure access, a zero trust (ZT) architecture using a software-defined perimeter (SDP) is employed. The connection process for user equipment accessing private networks is detailed through 5G authentication and ZT access management. Simulated 5G analysis compares scenarios before and after applying SDP under denial-of-service (DoS) and IP scanning attacks. Results demonstrate that SDP mitigates IP scanning threats, improves throughput during DoS attacks, and maintains performance. Validation on a real-world 5G testbed confirms feasibility, robust security, and stability. These findings underscore the potential of ZT-based 5G military networks for modern defense communication.

Traditional camouflage focuses on visual concealment, but the growing use of infrared (IR) detection systems has created a need for materials that can manipulate IR wavelengths. Electrochromic devices—commonly used for light and heat control—offer a promising solution for dynamic IR control. These systems rely on transparent conducting electrodes, with indium tin oxide (ITO) being the most common. However, ITO presents a challenge, as it generally blocks IR light transmissions. In this study, we optimize for IR transmissions by adjusting the ITO thickness, demonstrating excellent modulation in electrochromic devices. Devices with ITO thicknesses of 40 nm, 75 nm, and 302 nm are tested, with the 75-nm electrode achieving 67.73% transmittance modulation in the visible range and 51.41% in the near-infrared range. Response times for bleaching and coloration are 4.0 s and 2.8 s, respectively.