Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,423)

Search Parameters:
Keywords = Gaussian states

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
29 pages, 4868 KiB  
Article
Stochastic Vibration of Damaged Cable System Under Random Loads
by Yihao Wang, Wei Li and Drazan Kozak
Vibration 2025, 8(3), 44; https://doi.org/10.3390/vibration8030044 (registering DOI) - 4 Aug 2025
Abstract
This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and [...] Read more.
This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and its static configuration is determined. Using the Pearl River Huangpu Bridge as a case study, the accuracy of the analytical solution for the cable’s sag displacement is validated through the finite difference method (FDM). Furthermore, a quantitative relationship between the damage parameters and structural response under stochastic excitation is developed, and the nonlinear stochastic dynamic equations governing the in-plane and out-of-plane motions of the damaged cable are derived. Subsequently, a Gaussian Radial Basis Function Neural Network (GRBFNN) method is employed to solve for the steady-state probability density function of the system response, enabling a detailed analysis of how various damage parameters affect structural behavior. Finally, the First-Order and Second-Order Reliability Method (FORM/SORM) are used to compute the reliability index and failure probability, which are further validated using Monte Carlo simulation (MCS). Results show that the severity parameter η shows the highest sensitivity in influencing the failure probability among the damage parameters. For the system of the Pearl River Huangpu bridge, an increase in the damage extent δ from 0.1 to 0.4 can reduce the reliability-based service life of by approximately 40% under fixed values of the damage severity and location, and failure risk is highest when the damage is located at the midspan of the cable. This study provides a theoretical framework from the point of stochastic vibration for evaluating the response and associated reliability of mechanical systems; the results can be applied in practice with guidance for the engineering design and avoid potential damages of suspended cables. Full article
18 pages, 622 KiB  
Article
Distributed Diffusion Multi-Distribution Filter with IMM for Heavy-Tailed Noise
by Guannan Chang, Changwu Jiang, Wenxing Fu, Tao Cui and Peng Dong
Signals 2025, 6(3), 37; https://doi.org/10.3390/signals6030037 (registering DOI) - 1 Aug 2025
Viewed by 74
Abstract
With the diversification of space applications, the tracking of maneuvering targets has gradually gained attention. Issues such as their wide range of movement and observation outliers caused by human operation are worthy of in-depth discussion. This paper presents a novel distributed diffusion multi-noise [...] Read more.
With the diversification of space applications, the tracking of maneuvering targets has gradually gained attention. Issues such as their wide range of movement and observation outliers caused by human operation are worthy of in-depth discussion. This paper presents a novel distributed diffusion multi-noise Interacting Multiple Model (IMM) filter for maneuvering target tracking in heavy-tailed noise. The proposed approach leverages parallel Gaussian and Student-t filters to enhance robustness against non-Gaussian process and measurement noise. This hybrid filter is implemented as a node within a distributed network, where the diffusion algorithm leads to the global state asymptotically reaching consensus as the filtering time progresses. Furthermore, a fusion of multiple motion models within the IMM algorithm enables robust tracking of maneuvering targets across the distributed network and process outlier caused by maneuver compared to previous studies. Simulation results demonstrate the effectiveness of the proposed filter in tracking maneuvering targets. Full article
Show Figures

Figure 1

20 pages, 8446 KiB  
Article
Extraction of Corrosion Damage Features of Serviced Cable Based on Three-Dimensional Point Cloud Technology
by Tong Zhu, Shoushan Cheng, Haifang He, Kun Feng and Jinran Zhu
Materials 2025, 18(15), 3611; https://doi.org/10.3390/ma18153611 (registering DOI) - 31 Jul 2025
Viewed by 112
Abstract
The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using [...] Read more.
The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables. Full article
(This article belongs to the Section Construction and Building Materials)
Show Figures

Figure 1

33 pages, 14330 KiB  
Article
Noisy Ultrasound Kidney Image Classifications Using Deep Learning Ensembles and Grad-CAM Analysis
by Walid Obaid, Abir Hussain, Tamer Rabie and Wathiq Mansoor
AI 2025, 6(8), 172; https://doi.org/10.3390/ai6080172 - 31 Jul 2025
Viewed by 274
Abstract
Objectives: This study introduces an automated classification system for noisy kidney ultrasound images using an ensemble of deep neural networks (DNNs) with transfer learning. Methods: The method was tested using a dataset with two categories: normal kidney images and kidney images with stones. [...] Read more.
Objectives: This study introduces an automated classification system for noisy kidney ultrasound images using an ensemble of deep neural networks (DNNs) with transfer learning. Methods: The method was tested using a dataset with two categories: normal kidney images and kidney images with stones. The dataset contains 1821 normal kidney images and 2592 kidney images with stones. Noisy images involve various types of noises, including salt and pepper noise, speckle noise, Poisson noise, and Gaussian noise. The ensemble-based method is benchmarked with state-of-the-art techniques and evaluated on ultrasound images with varying quality and noise levels. Results: Our proposed method demonstrated a maximum classification accuracy of 99.43% on high-quality images (the original dataset images) and 99.21% on the dataset images with added noise. Conclusions: The experimental results confirm that the ensemble of DNNs accurately classifies most images, achieving a high classification performance compared to conventional and individual DNN-based methods. Additionally, our method outperforms the highest-achieving method by more than 1% in accuracy. Furthermore, our analysis using Gradient-weighted Class Activation Mapping indicated that our proposed deep learning model is capable of prediction using clinically relevant features. Full article
(This article belongs to the Section Medical & Healthcare AI)
Show Figures

Figure 1

33 pages, 8930 KiB  
Article
Network-Aware Gaussian Mixture Models for Multi-Objective SD-WAN Controller Placement
by Abdulrahman M. Abdulghani, Azizol Abdullah, Amir Rizaan Rahiman, Nor Asilah Wati Abdul Hamid and Bilal Omar Akram
Electronics 2025, 14(15), 3044; https://doi.org/10.3390/electronics14153044 - 30 Jul 2025
Viewed by 138
Abstract
Software-Defined Wide Area Networks (SD-WANs) require optimal controller placement to minimize latency, balance loads, and ensure reliability across geographically distributed infrastructures. This paper introduces NA-GMM (Network-Aware Gaussian Mixture Model), a novel multi-objective optimization framework addressing key limitations in current controller placement approaches. Three [...] Read more.
Software-Defined Wide Area Networks (SD-WANs) require optimal controller placement to minimize latency, balance loads, and ensure reliability across geographically distributed infrastructures. This paper introduces NA-GMM (Network-Aware Gaussian Mixture Model), a novel multi-objective optimization framework addressing key limitations in current controller placement approaches. Three principal contributions distinguish NA-GMM: (1) a hybrid distance metric that integrates geographic distance, network latency, topological cost, and link reliability through adaptive weighting, effectively capturing multi-dimensional network characteristics; (2) a modified expectation–maximization algorithm incorporating node importance-weighting to optimize controller placements for critical network elements; and (3) a robust clustering mechanism that transitions from probabilistic (soft) assignments to definitive (hard) cluster selections, ensuring optimal placement convergence. Empirical evaluations on real-world topologies demonstrate NA-GMM’s superiority, achieving up to 22.7% lower average control latency compared to benchmark approaches, maintaining near-optimal load distribution with node distribution ratios, and delivering a 12.9% throughput improvement. Furthermore, NA-GMM achieved exquisite computational efficiency, executing 68.9% faster and consuming 41.5% less memory than state of the art methods, while achieving exceptional load balancing. These findings confirm NA-GMM’s practical viability for large-scale SD-WAN deployments where real-time multi-objective optimization is essential. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)
Show Figures

Figure 1

16 pages, 1550 KiB  
Article
Understanding and Detecting Adversarial Examples in IoT Networks: A White-Box Analysis with Autoencoders
by Wafi Danesh, Srinivas Rahul Sapireddy and Mostafizur Rahman
Electronics 2025, 14(15), 3015; https://doi.org/10.3390/electronics14153015 - 29 Jul 2025
Viewed by 229
Abstract
Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks [...] Read more.
Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article
Show Figures

Figure 1

20 pages, 2776 KiB  
Article
Automatic 3D Reconstruction: Mesh Extraction Based on Gaussian Splatting from Romanesque–Mudéjar Churches
by Nelson Montas-Laracuente, Emilio Delgado Martos, Carlos Pesqueira-Calvo, Giovanni Intra Sidola, Ana Maitín, Alberto Nogales and Álvaro José García-Tejedor
Appl. Sci. 2025, 15(15), 8379; https://doi.org/10.3390/app15158379 - 28 Jul 2025
Viewed by 197
Abstract
This research introduces an automated 3D virtual reconstruction system tailored for architectural heritage (AH) applications, contributing to the ongoing paradigm shift from traditional CAD-based workflows to artificial intelligence-driven methodologies. It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) [...] Read more.
This research introduces an automated 3D virtual reconstruction system tailored for architectural heritage (AH) applications, contributing to the ongoing paradigm shift from traditional CAD-based workflows to artificial intelligence-driven methodologies. It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) and its successor, Gaussian splatting (GS)—as state-of-the-art techniques in the domain. The study advocates for replacing point cloud data in heritage building information modeling workflows with image-based inputs, proposing a novel “photo-to-BIM” pipeline. A proof-of-concept system is presented, capable of processing photographs or video footage of ancient ruins—specifically, Romanesque–Mudéjar churches—to automatically generate 3D mesh reconstructions. The system’s performance is assessed using both objective metrics and subjective evaluations of mesh quality. The results confirm the feasibility and promise of image-based reconstruction as a viable alternative to conventional methods. The study successfully developed a system for automated 3D mesh reconstruction of AH from images. It applied GS and Mip-splatting for NeRFs, proving superior in noise reduction for subsequent mesh extraction via surface-aligned Gaussian splatting for efficient 3D mesh reconstruction. This photo-to-mesh pipeline signifies a viable step towards HBIM. Full article
Show Figures

Figure 1

19 pages, 2243 KiB  
Article
Theoretical Calculation of Ground and Electronically Excited States of MgRb+ and SrRb+ Molecular Ions: Electronic Structure and Prospects of Photo-Association
by Mohamed Farjallah, Hela Ladjimi, Wissem Zrafi and Hamid Berriche
Atoms 2025, 13(8), 69; https://doi.org/10.3390/atoms13080069 - 25 Jul 2025
Viewed by 299
Abstract
In this work, a comprehensive theoretical investigation is carried out to explore the electronic and spectroscopic properties of selected diatomic molecular ions MgRb+ and SrRb+. Using high-level ab initio calculations based on a pseudopotential approach, along with large Gaussian basis [...] Read more.
In this work, a comprehensive theoretical investigation is carried out to explore the electronic and spectroscopic properties of selected diatomic molecular ions MgRb+ and SrRb+. Using high-level ab initio calculations based on a pseudopotential approach, along with large Gaussian basis sets and full valence configuration interaction (FCI), we accurately determine adiabatic potential energy curves, spectroscopic constants, transition dipole moments (TDMs), and permanent electric dipole moments (PDMs). To deepen our understanding of these systems, we calculate radiative lifetimes for vibrational levels in both ground and low-lying excited electronic states. This includes evaluating spontaneous and stimulated emission rates, as well as the effects of blackbody radiation. We also compute Franck–Condon factors and analyze photoassociation processes for both ions. Furthermore, to explore low-energy collisional dynamics, we investigate elastic scattering in the first excited states (21Σ+) describing the collision between the Ra atom and Mg+ or Sr+ ions. Our findings provide detailed insights into the theoretical electronic structure of these molecular ions, paving the way for future experimental studies in the field of cold and ultracold molecular ion physics. Full article
Show Figures

Figure 1

22 pages, 5346 KiB  
Article
Numerical Study of Stud Welding Temperature Fields on Steel–Concrete Composite Bridges
by Sicong Wei, Han Su, Xu Han, Heyuan Zhou and Sen Liu
Materials 2025, 18(15), 3491; https://doi.org/10.3390/ma18153491 - 25 Jul 2025
Viewed by 320
Abstract
Non-uniform temperature fields are developed during the welding of studs in steel–concrete composite bridges. Due to uneven thermal expansion and reversible solid-state phase transformations between ferrite/martensite and austenite structures within the materials, residual stresses are induced, which ultimately degrades the mechanical performance of [...] Read more.
Non-uniform temperature fields are developed during the welding of studs in steel–concrete composite bridges. Due to uneven thermal expansion and reversible solid-state phase transformations between ferrite/martensite and austenite structures within the materials, residual stresses are induced, which ultimately degrades the mechanical performance of the structure. For a better understanding of the influence on steel–concrete composite bridges’ structural behavior by residual stress, accurate simulation of the spatio-temporal temperature distribution during stud welding under practical engineering conditions is critical. This study introduces a precise simulation method for temperature evolution during stud welding, in which the Gaussian heat source model was applied. The simulated results were validated by real welding temperature fields measured by the infrared thermography technique. The maximum error between the measured and simulated peak temperatures was 5%, demonstrating good agreement between the measured and simulated temperature distributions. Sensitivity analyses on input current and plate thickness were conducted. The results showed a positive correlation between peak temperature and input current. With lower input current, flatter temperature gradients were observed in both the transverse and thickness directions of the steel plate. Additionally, plate thickness exhibited minimal influence on radial peak temperature, with a maximum observed difference of 130 °C. However, its effect on peak temperature in the thickness direction was significant, yielding a maximum difference of approximately 1000 °C. The thermal influence of group studs was also investigated in this study. The results demonstrated that welding a new stud adjacent to existing ones introduced only minor disturbances to the established temperature field. The maximum peak temperature difference before and after welding was approximately 100 °C. Full article
(This article belongs to the Section Construction and Building Materials)
Show Figures

Figure 1

24 pages, 988 KiB  
Article
Consistency-Oriented SLAM Approach: Theoretical Proof and Numerical Validation
by Zhan Wang, Alain Lambert, Yuwei Meng, Rongdong Yu, Jin Wang and Wei Wang
Electronics 2025, 14(15), 2966; https://doi.org/10.3390/electronics14152966 - 24 Jul 2025
Viewed by 216
Abstract
Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging task in robotics literature, where safety and reliability are the critical issues for successfully autonomous applications of robots. Classically, the SLAM problem is tackled via probabilistic or optimization methods (such as [...] Read more.
Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging task in robotics literature, where safety and reliability are the critical issues for successfully autonomous applications of robots. Classically, the SLAM problem is tackled via probabilistic or optimization methods (such as EKF-SLAM, Fast-SLAM, and Graph-SLAM). Despite their strong performance in real-world scenarios, these methods may exhibit inconsistency, which is caused by the inherent characteristic of model linearization or Gaussian noise assumption. In this paper, we propose an alternative monocular SLAM algorithm which theoretically relies on interval analysis (iMonoSLAM), to pursue guaranteed rather than probabilistically defined solutions. We consistently modeled and initialized the SLAM problem with a bounded-error parametric model. The state estimation process is then cast into an Interval Constraint Satisfaction Problem (ICSP) and resolved through interval constraint propagation techniques without any linearization or Gaussian noise assumption. Furthermore, we theoretically prove the obtained consistency and propose a versatile method for numerical validation. To the best of our knowledge, this is the first time such a proof has been proposed. A plethora of numerical experiments are carried to validate the consistency, and a preliminary comparison with classical EKF-SLAM in different noisy situations is also presented. Our proposed iMonoSLAM shows outstanding performance in obtaining reliable solutions, highlighting the potential application prospect in safety-critical scenarios of mobile robots. Full article
(This article belongs to the Special Issue Simultaneous Localization and Mapping (SLAM) of Mobile Robots)
Show Figures

Figure 1

15 pages, 2886 KiB  
Article
Electrical Characteristics of Mesh-Type Floating Gate Transistors for High-Performance Synaptic Device Applications
by Soyeon Jeong, Jaemin Kim, Hyeongjin Chae, Taehwan Koo, Juyeong Chae and Moongyu Jang
Appl. Sci. 2025, 15(15), 8174; https://doi.org/10.3390/app15158174 - 23 Jul 2025
Viewed by 203
Abstract
Nanoparticle floating gate (NPFG) transistors have gained attention as synaptic devices due to their discrete charge storage capability, which minimizes leakage currents and enhances the memory window. In this study, we propose and evaluate a mesh-type floating gate transistor (Mesh-FGT) designed to emulate [...] Read more.
Nanoparticle floating gate (NPFG) transistors have gained attention as synaptic devices due to their discrete charge storage capability, which minimizes leakage currents and enhances the memory window. In this study, we propose and evaluate a mesh-type floating gate transistor (Mesh-FGT) designed to emulate the characteristics of NPFG transistors. Individual floating gates with dimensions of 3 µm × 3 µm are arranged in an array configuration to form the floating gate structure. The Mesh-FGT is composed of an Al/Pt/Cr/HfO2/Pt/Cr/HfO2/SiO2/SOI (silicon-on-insulator) stack. Threshold voltages (Vth) extracted from the transfer and output curves followed Gaussian distributions with means of 0.063 V (σ = 0.100 V) and 1.810 V (σ = 0.190 V) for the erase (ERS) and program (PGM) states, respectively. Synaptic potentiation and depression were successfully demonstrated in a multi-level implementation by varying the drain current (Ids) and Vth. The Mesh-FGT exhibited high immunity to leakage current, excellent repeatability and retention, and a stable memory window that initially measured 2.4 V. These findings underscore the potential of the Mesh-FGT as a high-performance neuromorphic device, with promising applications in array device architectures and neuromorphic neural network implementations. Full article
Show Figures

Figure 1

12 pages, 493 KiB  
Article
Exploring Non-Gaussianity Reduction in Quantum Channels
by Micael Andrade Dias and Francisco Marcos de Assis
Entropy 2025, 27(7), 768; https://doi.org/10.3390/e27070768 - 20 Jul 2025
Viewed by 230
Abstract
The quantum relative entropy between a quantum state and its Gaussian equivalent is a quantifying function of the system’s non-Gaussianity, a useful resource in several applications, such as quantum communication and computation. One of its most fundamental properties is to be monotonically decreasing [...] Read more.
The quantum relative entropy between a quantum state and its Gaussian equivalent is a quantifying function of the system’s non-Gaussianity, a useful resource in several applications, such as quantum communication and computation. One of its most fundamental properties is to be monotonically decreasing under Gaussian evolutions. In this paper, we develop the conditions for a non-Gaussian quantum channel to preserve the monotonically decreasing property. We propose a necessary condition to classify between Gaussian and non-Gaussian channels and use it to define a class of quantum channels that decrease the system’s non-Gaussianity. We also discuss how this property, combined with a restriction on the states at the channel’s input, can be applied to the security analysis of continuous-variable quantum key distribution protocols. Full article
Show Figures

Figure 1

23 pages, 2233 KiB  
Article
A Novel Back Propagation Neural Network Based on the Harris Hawks Optimization Algorithm for the Remaining Useful Life Prediction of Lithium-Ion Batteries
by Yuyang Zhou, Zijian Shao, Huanhuan Li, Jing Chen, Haohan Sun, Yaping Wang, Nan Wang, Lei Pei, Zhen Wang, Houzhong Zhang and Chaochun Yuan
Energies 2025, 18(14), 3842; https://doi.org/10.3390/en18143842 - 19 Jul 2025
Viewed by 271
Abstract
Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL [...] Read more.
Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO4) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability. Full article
(This article belongs to the Section D: Energy Storage and Application)
Show Figures

Graphical abstract

23 pages, 20932 KiB  
Article
Robust Small-Object Detection in Aerial Surveillance via Integrated Multi-Scale Probabilistic Framework
by Youyou Li, Yuxiang Fang, Shixiong Zhou, Yicheng Zhang and Nuno Antunes Ribeiro
Mathematics 2025, 13(14), 2303; https://doi.org/10.3390/math13142303 - 18 Jul 2025
Viewed by 289
Abstract
Accurate and efficient object detection is essential for aerial airport surveillance, playing a critical role in aviation safety and the advancement of autonomous operations. Although recent deep learning approaches have achieved notable progress, significant challenges persist, including severe object occlusion, extreme scale variation, [...] Read more.
Accurate and efficient object detection is essential for aerial airport surveillance, playing a critical role in aviation safety and the advancement of autonomous operations. Although recent deep learning approaches have achieved notable progress, significant challenges persist, including severe object occlusion, extreme scale variation, dense panoramic clutter, and the detection of very small targets. In this study, we introduce a novel and unified detection framework designed to address these issues comprehensively. Our method integrates a Normalized Gaussian Wasserstein Distance loss for precise probabilistic bounding box regression, Dilation-wise Residual modules for improved multi-scale feature extraction, a Hierarchical Screening Feature Pyramid Network for effective hierarchical feature fusion, and DualConv modules for lightweight yet robust feature representation. Extensive experiments conducted on two public airport surveillance datasets, ASS1 and ASS2, demonstrate that our approach yields substantial improvements in detection accuracy. Specifically, the proposed method achieves an improvement of up to 14.6 percentage points in mean Average Precision (mAP@0.5) compared to state-of-the-art YOLO variants, with particularly notable gains in challenging small-object categories such as personnel detection. These results highlight the effectiveness and practical value of the proposed framework in advancing aviation safety and operational autonomy in airport environments. Full article
Show Figures

Graphical abstract

21 pages, 5616 KiB  
Article
Symmetry-Guided Dual-Branch Network with Adaptive Feature Fusion and Edge-Aware Attention for Image Tampering Localization
by Zhenxiang He, Le Li and Hanbin Wang
Symmetry 2025, 17(7), 1150; https://doi.org/10.3390/sym17071150 - 18 Jul 2025
Viewed by 269
Abstract
When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet [...] Read more.
When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet (Fusion-Enhanced Network)—that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model’s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively. Full article
Show Figures

Figure 1

Back to TopTop