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Item
Non-linear digital twin-based performance and service life assessment
(IA-FraMCoS, 2025-04-25) Strauss, Alfred; Taubling-Fruleux, Benjamin; Novák, Drahomír; Novák, Lukáš; Lehký, David
This work focuses on the load-bearing behavior of a specially reinforced existing pier of the Jauntal Bridge in Carinthia (DT Physical Model). This behavior is virtually simulated using a non-linear finite element model (DT Virtual Model). The aim is to draw conclusions about the constantly changing load conditions by observing the development of cracks and the geometric deformations (DT Monitoring, e.g., via orthographic and radar interferometry) and thus to make statements about the constantly fluctuating safety level and the fluctuating service life. Based on these findings, it should be possible to optimize the existing verification formats and the arrangement of the reinforcement measures, particularly the prestressing bars and the interaction between new and old concrete at the pier heads. Consequently, this would allow for a direct assessment of the constantly fluctuating loads and safety levels.
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Adaptive Resource Optimization for IoT-Enabled Disaster-Resilient Non-Terrestrial Networks using Deep Reinforcement Learning
(Společnost pro radioelektronické inženýrství, 2025-06) Jeribi, Fathe; John Martin, R.
The increasing deployment of IoT devices across sectors such as agriculture, transportation, and infrastructure has intensified the need for connectivity in remote and non-terrestrial regions. Non-terrestrial networks (NTNs), which include maritime and space platforms, face unique challenges for IoT connectivity, including mobility and weather conditions, which are critical for maintaining quality of service (QoS), especially in disaster management scenarios. The dynamic nature of NTNs makes static resource allocation insufficient, necessitating adaptive strategies to address varying demands and environmental conditions during disaster management. In this paper, we propose an adaptive resource optimization approach for disaster-resilient IoT connectivity in non-terrestrial environments using deep reinforcement learning. Initially, we design the chaotic plum tree (CPT) algorithm for clustering IoT nodes to maximize the number of satisfactory connections, ensuring all nodes meet sustainability requirements in terms of delay and QoS. Additionally, unmanned aerial vehicles (UAVs) are used to provide optimal coverage for IoT nodes in disaster areas, with coverage optimization achieved through the non-linear smooth optimization (NLSO) algorithm. Furthermore, we develop the multi-variable double deep reinforcement learning (MVD-DRL) framework for resource management, which addresses congestion and transmission power of IoT nodes to enhance network performance by maximize successful connections. Simulation results demonstrate that our MVD-DRL approach reduces the average end-to-end delay by 50.24% compared to existing approaches. It also achieves a throughput improvement of 13.01%, an energy consumption efficiency of 68.71%, and an efficiency in the number of successful connections of 17.51% compared to current approaches.
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Aerial RIS Aided NOMA Networks with Optimized Secrecy Metrics Performance
(Společnost pro radioelektronické inženýrství, 2025-06) Titel, F.; Belattar, M.; Lashab, M.; Abd-Alhameed, R.
Reconfigurable Intelligent Surface (RIS) technology is a promising technique for enhancing the performance of reconfigurable next-generation wireless networks. In this paper, we investigate the physical layer security of the downlink in RIS-aided non-orthogonal multiple access (NOMA) networks in the presence of an eavesdropper. To characterize the network performance, the expected value of the new channel statistics is derived for the reflected links in the case of Rayleigh fading distribution. Furthermore, the performance of the proposed network is evaluated in terms of the secrecy outage probability (SOP) and the strictly positive secrecy capacity (SPSC). To optimize these metrics, we employ the multi-objective artificial vultures optimization algorithm (MOAVOA), using the power allocation coefficients of the nearby and distant users as key parameters. Two case studies are considered in simulation: perfect channel state information (CSI) and imperfect CSI.
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An Efficient Optimization Algorithm for Measurement Matrix Based on SVD and Improved Nesterov Accelerated Gradient
(Společnost pro radioelektronické inženýrství, 2025-06) Zhang, B.; Yi, R.; Wang, Z.; Pu, J.; Sun, Y.
In compressed sensing, a measurement matrix having low coherence with a specified sparse dictionary has been shown to be advantageous over a Gaussian random matrix in terms of reconstruction performance. In this paper the problem of efficiently designing the measurement matrix is addressed. The measurement matrix is designed by iteratively minimizing the difference between the Gram matrix of the sensing matrix and a target Gram matrix. A new target Gram matrix is designed by applying singular value decomposition to the sensing matrix and utilizing entry shrinking in the Gram matrix, leading to lower mutual coherence indicators. An improved Nesterov accelerated gradient algorithm is derived to update the measurement matrix, which can improve the convergence behavior. An efficient optimization algorithm for measurement matrix is proposed on the basis of alternating minimization. The experimental results and analysis show that the proposed algorithm performs well in terms of both computational complexity and reconstruction performance.
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An Improved Small Target Detection Algorithm Based on YOLOv8s
(Společnost pro radioelektronické inženýrství, 2025-06) Ma, G.; Xu, C.; Xu, Z.; Song, X.
Due to challenges such as the small size of targets, complex backgrounds, limited feature extraction capa-bilities, and frequent false positives and false negatives, traditional detection algorithms often perform poorly in small object detection tasks. To address these challenges, this pa¬per proposes an enhanced small object detection algorithm, SOD-YOLO, based on YOLOv8s. First, the S_C2f_CAFM module is integrated into the feature extraction network, enabling the effective capture of fine-grained local features and broad contextual information, while simultaneously reducing model parameters and computational complexity. Second, in the feature fusion stage, the redesigned bidirectional feature pyramid network employs a spatial context awareness module to extract key features, adding a top-down path to optimize feature fusion and enhance discriminative information. In the Neck section, the D_C2f_MSPA module is introduced, which, while being lightweight, accurately models channel dependencies in feature maps, effectively reducing both false positives and false negatives for small objects. Finally, the inclusion of Normalized Wasserstein Distance (NWD) further improves detection accuracy and reduces the modelâ s sensitivity to small positional deviations in small objects. Experimental results on the DOTAv1.0, VisDrone2019, and TT100K datasets confirm that SOD-YOLO achieves excellent performance, demonstrating the effectiveness of the modifications made to the original YOLOv8 model.