Search Results (225)

Parameter variation caused by manufacturing tolerances and thermal drift makes PMSM fault-severity estimation difficult, because motor-level offsets and fault effects are coupled in the d–q model. This paper proposes a channel-selective BO-Fusion-PINN for parameter-generalized fault diagnosis. A healthy reference window is first used to estimate motor-parameter deviations through an integral least-squares observer, avoiding neural extrapolation of these offsets. A diagnostic window is then processed by a physics-informed LSTM branch and a data-driven LSTM branch, and Bayesian optimization assigns separate fusion weights to stator-resistance degradation and permanent-magnet flux weakening. Experiments over parameter out-of-distribution buckets and non-ideal simulation settings show that the fused estimator consistently improves on either branch alone. The method is especially effective in the flux channel and remains competitive with high-capacity data baselines while preserving physical interpretability. The primary scientific contribution is an identifiability-guided fusion rule that assigns physics and data trust to each fault channel according to its statistical observability rather than through a single global weight; in practical terms, this yields a compact and interpretable estimator that transfers across the parameter-tolerance band of a machine class and can, in principle, support controlled end-of-line screening and scheduled diagnostic assessment under a matched-window acquisition protocol. Full article

Occluded person re-identification (Re-ID) is a challenging task, as non-target pedestrians or surrounding obstacles often interfere with the visual cues of the target person, making it difficult for models to effectively learn discriminative feature representations. Most existing methods focus on salient body parts via spatial partitioning or external cues; however, they are either limited in capturing diverse semantic information or tend to introduce additional network complexity. To address these issues, we propose a Diversity Feature Learning Network (DFLNet). Specifically, a Scene-Level Occlusion (SLO) strategy is designed to automatically simulate two common occlusion scenarios by modeling the relative spatial relationships between the target person and surrounding occluders in real-world scenes. Subsequently, multiple class tokens are introduced to capture diverse representations of the target identity. A Token Diversity Constraint (TDC) loss is further imposed on these class tokens to encourage the learning of discriminative and diverse feature embeddings. Finally, we design a Diversity Feature Fusion (DFF) module, which facilitates the interaction and integration of dual-branch features by modeling global feature correlations and optimizing inter-feature distribution distances. Extensive experiments on occluded, partial, and holistic Re-ID datasets demonstrate the effectiveness of the proposed DFLNet. Full article

In recent years, UAV-based visible and thermal (RGB-T) multimodal object detection has demonstrated significant potential for monitoring forest fires in complex environments. However, restricted by the intrinsic discrepancies between modalities, existing RGB-T models still struggle to achieve optimal detection accuracy. To address the aforementioned issues, this paper proposes FireRGBTNet, a lightweight and efficient RGB-T fusion model for UAV-based forest fire detection. First, a heterogeneous dual-stream backbone is designed to precisely capture modality-specific information via customized feature extraction modules, utilizing Target-Enhanced Downsampling to adaptively preserve the fragile features of small targets. Second, a multi-scale semantic alignment enhancement branch is proposed to explicitly bridge the semantic gap through the dual constraints of statistical distribution and semantic direction. Finally, a multimodal spatial gated fusion module is constructed, which utilizes spatial gated interactions to effectively suppress multimodal noise and incorporates Mish Gated Linear Units to compensate for global semantics, thereby achieving high-quality heterogeneous feature fusion. Extensive experiments on the RGBT-3M dataset demonstrate that with a mere 4.13M parameters, FireRGBTNet achieves accuracies of 95.9% and 62.8% in terms of mAP@0.5 and mAP@0.5:0.95, respectively. The proposed model achieves an optimal trade-off between detection accuracy and computational efficiency, providing a highly effective solution for UAV-based forest fire monitoring in complex environments. Full article

Children with autism spectrum disorder (ASD) often exhibit atypical patterns of visual attention allocation and social-cue processing. Eye-tracking scanpath (ETSP) retains information about fixation points, saccade paths and their temporal changes in the form of images, providing an intuitive and computable data representation for analyzing ASD-related visual attention patterns. However, in ASD auxiliary identification studies, the same participant often generates multiple eye-tracking recordings or multiple visual representation samples. If participant independence is not properly considered during model evaluation, the training and test sets may share individualized eye-movement patterns from the same child. In such cases, the model may learn subject-specific characteristics rather than stable and transferable ASD-related visual attention features, leading to an overestimation of its recognition ability on unseen participants. To address this issue, we propose a Global–Local Collaborative Fusion Network (GLCF-Net) under a strict participant-independent splitting protocol. Specifically, the proposed method first maps ETSP images into patch token sequences through a shared Patch Embedding layer. A CNN-based local branch is then used to extract local trajectory morphology, path density, and spatial neighborhood structure, while a ViT-based global branch models cross-region gaze transitions and the overall attention distribution. Finally, a gated adaptive fusion module dynamically integrates local and global information to enhance the representation of stable visual attention features. In the primary repeated stratified five-fold participant-level evaluation, averaging the two out-of-fold probabilities for each participant yielded an Accuracy of 87.0% and a ROC-AUC of 93.7%; the original participant split, retained as a secondary analysis, yielded an Accuracy of 83.52% and a ROC-AUC of 90.27%. Under the reported frozen-backbone configurations, the model also showed a balanced pattern across Accuracy, Recall, and F1-score. These results characterize performance for unseen participants within the same dataset and acquisition conditions. Full article

Han stone reliefs contain rich historical and artistic information, but their images often exhibit severe weathering, rubbing noise, and complex carving textures, making fine-grained classification challenging. To address the strong coupling between global semantic structures and local material textures, this paper proposes Frequency-Guided Collaborative ConvNeXt (FGC-ConvNeXt), a dual-branch spatial–frequency classification framework. The spatial branch extracts multi-scale semantic features using ConvNeXt-Tiny, while the frequency branch applies Fast Fourier Transform to model complementary structural and texture-sensitive information. A fixed frequency threshold of τ = 0.22 is used for frequency decomposition, and both branches are projected into a 512-dimensional feature space. The Spatial–Frequency Gated Fusion module dynamically adjusts their contributions at the sample level, while frequency-domain auxiliary supervision with a loss weight of λ = 0.4 prevents branch degradation during joint optimization. Experiments were conducted on a self-constructed four-class dataset containing 6368 Han stone relief images with an input resolution of 224 × 224 pixels. Over three independent runs, FGC-ConvNeXt achieved an accuracy of 97.6 ± 0.2%, a precision of 97.8 ± 0.2%, a recall of 97.4 ± 0.3%, and an F1-score of 97.6 ± 0.2%, outperforming the ConvNeXt-Tiny baseline by 4.1 percentage points in accuracy. The proposed model contains 29.8 M parameters. Visualization analyses provide qualitative support for more separated feature distributions and more concentrated activation patterns, while the robustness experiments indicate improved classification stability under the evaluated conditions. Full article

Multimodal medical image fusion integrates complementary information from heterogeneous imaging modalities to provide comprehensive visual support for clinical analysis. Most existing methods adopt an “encode–fuse–decode” paradigm that applies a single fusion rule only at the deepest network layer, often discarding shallow detail features and yielding blurred outputs with poor textural fidelity. To address this limitation, we propose MSFusion, a novel hierarchical framework that distributes adaptive fusion throughout the entire decoder stage. By leveraging skip connections to align decoder layers with corresponding encoder features, MSFusion enables full-scale integration of multi-resolution representations. The architecture employs a dual-branch convolutional encoder and introduces two core modules in the decoder: (1) the Multi-Scale Adaptive Fusion (MSAF) module, which addresses insufficient exploitation of cross-scale complementarity by dynamically weighting features via learnable attention, thereby balancing fine details and global semantics, and (2) the Multi-Scale Cross-Modal Cooperative Fusion (MSCMCF) module, which mitigates semantic misalignment through a cross-modal interactive attention mechanism that establishes robust inter-modality correspondences and promotes deep feature alignment. Additionally, a Vision RWKV (VRWKV) block is integrated to efficiently model both local and global spatial dependencies with linear computational complexity. Extensive experiments on public CT–MRI, PET–MRI, and SPECT–MRI datasets are evaluated using six standard metrics. On the CT–MRI benchmark, our method achieves MSE (↓) = 0.0423, CC = 0.8198, and SCD = 0.7023, outperforming state-of-the-art approaches. These results—combined with superior visual quality—demonstrate that MSFusion sets a new standard for accurate, detailed, and clinically meaningful multimodal image fusion. Full article

Modern remote sensing images (RSIs) provide increasingly fine spatial detail, making pronounced scale variations and complex spatial distributions of land-cover classes more apparent and thereby increasing the difficulty of semantic segmentation. Recent remote sensing semantic segmentation methods therefore have increasingly adopted Transformer- and Mamba-based models to improve global contextual modeling. However, these models often suffer from intraclass inconsistency and interclass feature confusion, leading to fragmented object structures and imprecise boundary delineation. Accordingly, we develop SPFMamba, an architecture built around Mamba that combines semantic prompting with frequency-adaptive fusion, thereby improving global context modeling and fine-detail representation. During feature reconstruction, we propose a semantic prompt global–local Mamba (SPGLM) block to jointly model global semantic information and local spatial cues. Its parallel semantic prompt global and multidirectional local perception branches promote semantically coherent and spatially continuous feature distributions, thereby effectively preserving the structural integrity of ground objects. To further alleviate cross-level semantic discrepancies and strengthen the representation of small-scale targets, we design a high-frequency adaptive fusion module (HFAFM). It first refines high-frequency responses in shallow layers to retain small-scale object structures and boundary cues. Subsequently, deep semantic priors guide local cross-attention, allowing low-level spatial cues to be selectively integrated with deeper semantic representations. Evaluations across ISPRS Vaihingen, ISPRS Potsdam, and OpenEarthMap datasets show that SPFMamba delivers favorable segmentation accuracy with only 17.85 M parameters, while showing improved preservation of object structures and fine spatial details in qualitative comparisons. Full article

This paper presents an attitude filter that encodes both orientation and uncertainty in a single four-dimensional vector, requiring no covariance propagation or normalization constraints. The filter state is the natural parameter of the von Mises–Fisher (vMF) distribution on S³, whose exponential family structure reduces measurement updates to vector addition. Prediction is governed by a continuous-time ODE (Ordinary Differential Equation) that couples rotational kinematics with concentration decay. The QUEST-based construction of measurement natural parameters with a Fisher-information-matched concentration, an antipodal switching mechanism for the quaternion double cover, and a global exponential convergence analysis of the attitude error are described. The filter construction is left-invariant: it commutes with rotations of the reference frame, making the error dynamics trajectory-independent. The result is a filter with the computational simplicity of a complementary filter and the statistical grounding of Bayesian vMF fusion, operating entirely in unconstrained ℝ⁴ space. The filter is validated in simulation, on two recorded flights—including an evaluation against an EFIS attitude reference—and its computational cost is measured down to on-target microcontroller cycle counts. Gyroscope bias estimation is not included and is left to future work. Full article

Foundation models such as the Segment Anything Model 2 (SAM2) have demonstrated strong performance in image segmentation; however, their application to industrial defect detection faces significant challenges due to the substantial domain gap between natural and industrial images, insufficient sensitivity to fine-grained high-frequency structures, and reliance on manual prompts. To address these issues, this study proposes a Hierarchical Cross-Branch Frequency-Aware Network (HCFNet) to adapt SAM2 for prompt-free industrial defect segmentation. First, a Gated Adapter is introduced into the frozen SAM2 encoder, enabling efficient cross-domain transfer without massive parameter retraining, thereby effectively preserving the pre-trained visual priors. Secondly, a Laplacian-enhanced Auxiliary Branch is designed to explicitly amplify high-frequency components, compensating for the inherent perception limitations of the Transformer backbone and significantly awakening the model’s sensitivity to subtle defects like micro-cracks. Finally, a Cross-branch Multi-scale Fusion Module is proposed to seamlessly align and integrate global semantic information with local structural details in a unified manner, resolving heterogeneous feature distribution conflicts. Extensive experiments on the MVTec AD and VisA datasets demonstrate that the proposed method consistently outperforms SAM2-based baselines in terms of mIoU and mDice. This study establishes an effective approach for leveraging foundation models in automated industrial inspection and is expected to drive advancements in precise defect perception technologies. Full article

With the increasing diversity of remote sensing image acquisition conditions and imaging scenarios, building extraction models often experience significant performance degradation in cross-dataset applications due to variations in sensors and scene characteristics. Improving their cross-domain generalization ability has therefore become a critical research problem. To address the challenges of appearance style discrepancy and feature distribution shift in cross-domain building extraction, this paper proposes WCMNet, a wavelet-guided and CNN–Mamba hybrid network for unsupervised domain adaptation in building extraction. Specifically, a Mamba Wavelet Alignment (MWA) module is designed to align low-frequency style information in the wavelet domain while preserving directional high-frequency edge structures, thereby mitigating cross-domain appearance discrepancies and reducing structural degradation during domain translation. In addition, a Global–Local Mamba Block (GLMB) is developed to jointly model local textures and global semantic dependencies. In GLMB, the CNN branch captures fine-grained local details and boundary cues, while the Mamba branch models long-range contextual information; an adaptive gated fusion mechanism further integrates the two types of features. Experimental results on six cross-domain transfer tasks across the WHU, Massachusetts, and Potsdam datasets demonstrate that WCMNet consistently outperforms existing state-of-the-art domain adaptation methods. In particular, WCMNet achieves an average IoU of 65.13% and an average BIoU of 74.80% across all transfer settings, with improvements of up to 27.35 percentage points in IoU and 38.32 percentage points in BIoU compared with the strongest competing methods. These results demonstrate that the proposed MWA and GLMB effectively improve building completeness, boundary delineation accuracy, and cross-domain robustness. Full article

High-dimensional change point detection is a fundamental problem in modern statistical learning, particularly when distributional changes are driven by a small and unknown subset of variables. In heterogeneous settings, uniform aggregation across coordinates may suffer from signal dilution, because stable or noisy variables can mask the evidence carried by structurally unstable coordinates. Moreover, many existing procedures primarily focus on temporal localization and provide limited information about the variables responsible for a detected structural break. To address these challenges, we propose WAVE, a weighted adaptive variable selection procedure for interpretable change point detection. WAVE constructs variance-standardized global CUSUM evidence and locally standardized exponentially weighted evidence for each coordinate and then adaptively maps intervalwise coordinate evidence into a continuous weight vector. The learned weights strengthen coordinates with persistent or local evidence of change while downweighting nuisance coordinates with weak evidence. The resulting weighted scan statistic is calibrated by a residual moving block bootstrap that preserves temporal and cross-sectional dependence and re-applies the weighting rule within bootstrap samples to account for data-adaptive aggregation. Detected change points are further equipped with coordinate-level attribution through a multi-criteria fusion rule combining adaptive weights, local standardized effect sizes, and marginal testing evidence. Simulation studies show that WAVE achieves accurate localization and reliable support recovery in both single and multiple change point settings, particularly under sparse and heterogeneous alternatives. An empirical analysis of S&P 100 stock returns in 2020 further demonstrates that WAVE identifies economically meaningful market regime shifts with interpretable coordinate-level attribution. Full article

In the intelligent transportation system of smart cities, object detection from UAV aerial imagery serves as the core technical support for traffic flow monitoring, violation detection, and emergency response. However, traffic objects captured from UAV perspectives typically exhibit extremely low pixel occupancy and are embedded in complex backgrounds, leading to three fundamental limitations in existing detection methods: insufficient utilization of global context information, inaccurate weak feature enhancement, and severe feature scale confusion. To address these challenges, this paper proposes ContextTiny-Net, an ultra-tiny object detection network built upon multi-dimensional symmetry design principles for urban UAV scenarios. Specifically, we first construct a global–local perception symmetric MetaFormer backbone and a hierarchical scale symmetric four-layer detection head, which achieves full-coverage detection from ultra-tiny to regular traffic objects with minimal computational overhead. Second, we design an information-theoretic and spatial-distribution-complementary symmetric-weak feature enhancement module, which accurately locates and strengthens weakly activated regions of small objects from two mutually complementary and symmetric dimensions. Finally, we propose a cross-scale decoupling symmetric feature fusion module and a symmetric Gaussian distribution-based normalized Wasserstein distance loss, which effectively eliminate scale confusion and significantly improve the robustness of small object bounding box regression. Extensive experiments on three mainstream benchmarks (AI-TOD, VisDrone, and COCO) demonstrate that ContextTiny-Net outperforms state-of-the-art methods in both overall detection accuracy and ultra-tiny object detection performance, verifying the effectiveness of the proposed symmetry-enhanced design paradigm. Full article

Detecting unmanned aerial vehicles (UAVs) remains a difficult task, primarily due to their tiny size, rapid motion, and complex backgrounds. Fusing visible and infrared imagery offers complementary advantages for robust detection, yet existing methods rely on spatial feature aggregation that overlooks spectral disparities, coupling noise with textures. Moreover, the small scale and high dynamics of UAVs hinder standard convolution from decoupling target signals from background interference due to limited receptive fields. To solve these limitations, the Wavelet-guided Frequency–Spatial Decoupling Network (WFSD-Net) is designed for visible–infrared UAV detection. First, to tackle fusion noise, the Discrete Wavelet Band-Differentiated Fusion (DWBF) module is designed to explicitly decouple noise-dominant sub-bands from information-rich components by performing spectral decomposition. It aligns low-frequency distributions via adaptive spatial weighting and disentangles high-frequency details using physics-aware rules, achieving source-level noise suppression. Second, an Axial Strip Contextual Attention (ASCA) module is proposed. By utilizing anisotropic strip convolution via orthogonal decomposition, this module captures global contextual dependencies to effectively decouple weak target features from background clutter, enhancing the spatial position encoding capability for weak targets. Finally, the proposed WFSD-Net method is validated on Anti-UAV300 and Multi-Sensor and Multi-View Fixed-Wing UAV (MMFW-UAV) datasets, and experiments demonstrate that the proposed method is superior to existing state-of-the-art (SOTA) methods. Full article

The increasing sophistication of cyber attacks has created a growing demand for effective Network Intrusion Detection Systems (NIDSs). Although deep learning has improved NIDS performance, existing models often lack adaptive mechanisms for spatiotemporal feature fusion and struggle with complex traffic distributions characterized by severe intra-class heterogeneity and inter-class overlap. Meanwhile, current interpretability methods mainly rely on feature importance analysis and provide limited insight into the model’s decision process. To address these challenges, we propose HARMONI, a two-stage hybrid learning framework that enhances both detection accuracy and model interpretability. In the first stage, a dual-branch Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) architecture extracts spatiotemporal features, which are dynamically fused through a lightweight adaptive gating network. The representation learning process is jointly optimized using a Dynamic Class-Center Loss to enforce intra-class compactness and inter-class separability in the latent space. In the second stage, the learned deep representations are concatenated with raw traffic features and fed into an ensemble classifier. This residual-style design mitigates information loss during deep encoding while leveraging the non-linear modeling capability of ensemble learning. We further develop a multi-level interpretability framework based on SHapley Additive exPlanations (SHAP) that analyzes global feature importance, individual feature contributions, and feature interactions to provide quantitative insights into the model’s decision mechanisms. Experiments on four benchmark datasets show that HARMONI consistently outperforms state-of-the-art baselines, achieving 80.19% and 78.24% accuracy on NSL-KDD and UNSW-NB15 respectively, surpassing representative deep learning and ensemble methods. Full article

Reliability prediction of aerospace dormant components requires fusing natural-storage observations at the operating temperature with accelerated-storage testing data at elevated temperatures. Existing scalar-weight fusion methods apply a global weight that cannot reflect the time-varying trustworthiness of the accelerated branch as Arrhenius extrapolation distance grows. Physics-based fusion propagates accelerated-test scatter through least squares but leaves the dominant error source—the degradation-model form itself—unaccounted for, and no method in either class verifies the coverage of its intervals. This paper proposes an empirically calibrated multi-fidelity fusion that selects a mechanism-specific natural-branch degradation model by the corrected Akaike information criterion and augments the accelerated-branch variance with an additive model-form term fitted from natural-storage residuals. This term turns the fusion weight into a continuous, time-varying diagnostic that detects Arrhenius misspecification from training data alone and falls back safely to the natural-only estimate. Prediction intervals are calibrated by split-conformal prediction on a disjoint simulated population, giving finite-sample, distribution-free coverage, and the remaining-storage-life interval follows from the band’s first-passage time. On a 1000-run varying-truth simulation, the calibrated band attains 95.5% trajectory coverage at the narrowest band width among six methods; on the torsion-bar case, the fusion reaches a held-out RMSE of 0.045 N$·$m and a remaining-life interval of 10.4–12.6 years. The model-form variance ratio provides a single-number regime diagnostic across all cases. Full article