Search Results (2,521)

In order to overcome the computational challenges associated with block preconditioners for Krylov subspace methods, particularly those arising from Schur complement systems, this paper proposes an improved new block (INB) preconditioner for solving 3 × 3 block saddle point problems. A detailed semi-convergence analysis of the iterative scheme induced by the INB preconditioner is provided. Moreover, the spectral properties of the preconditioned matrix are analyzed, revealing strong eigenvalue clustering around one. Efficient formulas for selecting quasi-optimal parameters are derived based on Frobenius-norm minimization. Extensive numerical experiments demonstrate that the proposed INB preconditioner significantly reduces iteration counts and CPU time compared with several existing block preconditioners. Full article

The rapid development of remote sensing satellite technology has enabled remote sensing images to be widely used in agriculture, meteorology, environmental monitoring and other fields. However, the presence of clouds in these images can lead to blurred and incomplete observations of the Earth’s surface, limiting the quality and applicability of the data. Current cloud detection networks usually adopt a single encoder–decoder structure that uniformly processes all spectral features without distinguishing between various spectral bands. To overcome this limitation, this paper proposes an Optical characteristics-guided Asymmetric Dual Encoder Feature Fusion cloud detection algorithm (OADEF²). The algorithm adopts an asymmetric dual encoder framework to divide the spectral bands of Sentinel-2A into two groups: RGB visible light bands and infrared/atmospheric correction bands, which are subsequently input into two different encoder branches. This method utilizes the unique physical characteristics of different spectral bands to improve the accuracy of cloud detection. In order to direct the focus of the network to cloud-related optical characteristics, an Optical characteristics-guided Multi-Scale cloud feature module (OCGMSCFM) based on Dynamic HOT Index and Full-Band Cloud Index is introduced. This module effectively solves the problem of insufficient representation of cloud features. In order to improve the efficiency of feature fusion, a Feature Aggregation and Filtering module (FAFM) is proposed. This module uses aggregation and techniques to filter basic features, thereby improving the accuracy of cloud detection. In order to overcome the limitations of feature modeling, a dual attention module that fuses Multi-interaction Local Spatial Attention mixed Channel Attention (MILSAMCAM) is added to the decoder. The experimental results validated the effectiveness of this algorithm in cloud detection tasks, achieving an F1-score of 97.30% on the S2-CMC dataset. Full article

This study aimed to investigate the emotional changes in the brain activity of 34 special education teachers using electroencephalography (EEG) signals in response to common problem behaviours observed in students with Autism Spectrum Disorder (ASD), such as self-harm, aggression, tantrums, and stereotyped behaviours. Vignettes with Turkish narration and stimulus videos were used for each behaviour type to trigger emotions. EEG data were collected from the frontal, temporal, parietal, and occipital regions, and subjected to pre-processing steps such as band-pass filtering (0.5–40 Hz) and Independent Component Analysis (ICA), and various spectral and statistical features were extracted. To improve classification performance, feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method, and Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and Random Forest (RF) algorithms were used for classification. The machine learning techniques used achieved success rates of up to 97.66% F1 score in classifying teachers’ brain activity in response to different behavioural patterns. Teachers showed strong negative emotional responses to self-harm, aggression, and tantrums, while showing less response to the stereotypical behaviours. It is recommended that the study be replicated with different signals and teachers. Full article

Addressing the problems of the difficulty in reconstructing high-resolution hyperspectral images caused by dynamic degradation characteristics, the poor adaptability of traditional static degradation models, and the oversimplified noise modeling, this paper proposes a degradation-aware dynamic Fourier network (DADFN) for hyperspectral super-resolution. This method employs a dual-channel split module to decouple and encode spectral and spatial degradation information, realizes the independent mapping of spectral and spatial features via a multi-layer perceptron module, and integrates a spectral–spatial dynamic cross-attention fusion module to generate 3D dynamic blur kernels tailored to different bands and spatial positions. The proposed method designs a multi-scale spectral–spatial collaborative constraint (MSSCC) loss function to ensure the coordinated optimization of modeling rationality, spectral continuity, and spatial detail fidelity. Experiments on the CAVE and Harvard benchmark datasets demonstrate that the DADFN algorithm outperforms the baseline methods in all evaluation metrics, which proves the proposed method’s strong robustness in real-world complex degradation scenarios. This method provides a novel solution balancing physical interpretability and performance superiority for hyperspectral image super-resolution tasks and holds significant value for advancing its applications in remote sensing monitoring, precision agriculture, and other related fields. Full article

Closed-form analytic inverses allow explicit tracking of parameter effects, facilitate interpretation of experimental signals, and support solving inverse problems. Here, we derive a rigorous closed-form expression for the inverse Laplace transform of a class of shifted quasi-rational spectral functions with a square-root radical and a power-law decaying factor. These functions appear in coupled diffusion processes in physics and in the analysis of electromagnetic signal propagation through electrically cascaded networks, signal processing, and related areas. The transform is expressed as a finite sum of three generalized hypergeometric functions—two Kummer functions and one five-parameter Kampé de Fériet function—each multiplied by a monomial depending on the decay parameter. The validity of the result is confirmed by direct Laplace transformation, which recovers the original spectral function. Several known inverse transforms appear as limiting cases, illustrating the generality of the solution. Additionally, reduction formulas for a subclass of Kampé de Fériet functions demonstrate how the general solution encompasses previously known results and highlight the generality of the method. Full article

This paper presents a stochastic framework for the inverse identification of structural material degradation (SMD) in cantilever beams. The method combines the Karhunen–Loéve (KL) expansion for the efficient parameterisation of spatially varying material decay with experimental Frequency Response Function (FRF) data within a Bayesian inference scheme. This approach employs a low-dimensional spectral parameterisation via the KL expansion, which mitigates the curse of dimensionality inherent in element-wise model updating, and provides a full-field probabilistic description of SMD. A two-phase constraint strategy was developed to address the fundamental tension between physical plausibility and algorithmic stability of the inverse identification algorithm: (1) physical regularisation during identification stabilises the ill-posed inverse problem, and (2) post-convergence selective regularisation eliminates physically impossible stiffness enhancements (exceeding 1.1 × baseline) that arise from measurement and modelling uncertainties. This phased approach prevents the algorithm distortion that occurs when constraints are applied too stringently during iteration, while ensuring final results respect fundamental physical principles. The framework is experimentally validated on a steel cantilever beam with a symmetric open-edge cut. Laser vibrometry measurements under swept-sine excitation demonstrate successful localisation and quantification of SMD, with the 95% credible interval accurately capturing the damaged region after physical constraint application. The adaptive constraint strategy resolves the delicate balance between mathematical stability and physical plausibility in inverse identification. Full article

Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain–computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal–occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs. Full article

Thick cloud removal is a critical preprocessing step for multitemporal remote sensing images (MTRSIs), as it directly determines the reliability of downstream analysis and applications. In MTRSIs, the same geographic region is observed at different times, and the underlying edge structures often remain physically consistent across temporal observations. Leveraging this intrinsic property, we introduce a Sobel-consistent term that explicitly enforces temporal consistency of edge-related features, thereby improving the reconstruction of fine structures and textures in cloud-obscured regions. Building on this insight, we propose a novel thick cloud removal model that integrates Sobel-based edge consistency with subspace-based spatiospectral low-rank tensor regularization. In this model, intrinsic images derived from subspace representation are organized into a fourth-order tensor, and low-rank constraints are applied to jointly capture the spatial, spectral, and temporal correlations inherent in MTRSIs. To efficiently solve the resulting optimization problem, we introduce an algorithm based on proximal alternating minimization. Experiments on both simulated and real-world MTRSI datasets demonstrate that the proposed method achieves superior reconstruction accuracy and visual fidelity, validating the physical interpretability and effectiveness of the approach. Full article

This study proposes a new formalized approach to the stabilization of linear transformations in artificial neural networks, based on discrete algebraic properties. In contrast to existing stability methods that rely on spectral norms, regularization techniques, or empirical heuristics, this work introduces the concept of algebraic stabilization—stability that arises from the structural properties of the matrices defining linear operators. The central object of investigation is the class of integer-valued matrices for which exponentiation to a form of the type ${\mathrm{W}}^{\mathrm{k}}=\mathrm{I}+\mathsf{\mu }\mathrm{D}$ is possible, where $\mathrm{D}\in {\mathbb{Z}}^{\mathrm{n}\times \mathrm{n}},\mathsf{\mu }\in {\mathbb{Z}}_{>1}$. A well-known problem in group algebra is considered that guarantees the existence of such an exponent under the condition that $\mathsf{\mu }$ is coprime with the determinant of $\mathrm{W}$. Within this framework, modular arithmetic, reduction modulo $\mathsf{\mu }$, and the group structure of ${\mathrm{G}\mathrm{L}}_{\mathrm{n}}\left({\mathbb{Z}}_{\mathsf{\mu }}\right)$ are employed, thereby linking the proposed method to the theory of finite groups and linear automata. The advantages of the approach are discussed, including formal control over the iterative behavior of transformations, compatibility with quantized and finitely representable networks, the possibility of embedding stabilizing conditions directly into the network architecture, and the potential to improve model interpretability and reliability. At the same time, limitations are identified, particularly those related to constructive implementation, the selection of suitable hyperparameters, and generalization to broader classes of transformations. Full article

We propose a two-level theory that connects Lin-equation-based dynamical coarse-graining of the turbulence cascade with an information-theoretic selection principle in logarithmic wavenumber space. This framework places the dissipation-range spectral shape on a verifiable logical basis rather than on ad hoc fitting. At the first (dynamical) level, we formulate an autonomous conservative Fokker–Planck equation for the normalized density and probability current. Under sufficient boundary decay and a strictly positive effective diffusion, the sign-reversed Kullback–Leibler divergence is shown to be a Lyapunov functional, yielding a rigorous H-theorem and fixing the arrow of time in scale space. At the second (selection) level, the dissipation range is treated as a stationary boundary-value problem for an open system by introducing a killing term for an unnormalized scale density. A WKB (Liouville–Green) analysis restricts the admissible tail to a stretched-exponential form and links the tail exponent to the high-wavenumber scaling of the effective diffusion. The exponential prefactor is fixed by dissipation-rate consistency, and the remaining degree of freedom is determined by one-dimensional Kullback–Leibler minimization (Hyper-MaxEnt) against a globally constructed reference distribution. The resulting exponent range is validated against the high-resolution DNS spectra reported in the literature. Full article

With the continuous improvement of remote sensing image resolution, accurately extracting road information from complex backgrounds remains challenging. This is because roads present diverse morphological characteristics across regions and scales, and their spectral features are highly similar to those of surrounding objects, such as buildings and bare soil, making them hard to distinguish. Occlusion by buildings and trees leads to incomplete road extraction. To solve the above problems, this paper proposed the atrous–strip–Unet (ASUNet), an encoder–decoder network into which atrous and strip convolution modules are inserted to extract roads with weak features and complex backgrounds from high-resolution remote sensing images. In this study, we construct the Zhouqu Road Dataset from high-resolution aerial imagery, covering representative road types (rural, suburban, and urban) characteristic of county-level settlements in western China. By comparing several advanced algorithms with excellent learning performance—including BiSeNet and LinkNet—on both the Zhouqu Road and DeepGlobe Datasets, the improved and optimized model presented in this paper demonstrates better extraction accuracy and effectiveness; it achieves F1 scores of 0.7292 and 0.7134 on the two datasets, respectively. It is particularly worth mentioning that our proposed algorithm shows better performance in scenarios where road features are weak or backgrounds are complex. Full article

Recent advances in physics-informed neural networks (PINN) have highlighted the need for systematic criteria for selecting appropriate algorithms to solve differential equations. This paper presents a numerical comparison between standard PINNs and gradient-enhanced PINNs (gPINNs) used to solve a high-order partial differential equations (PDE). To verify the accuracy and convergence behavior of all the methods, we solve a fourth-order PDE whose analytical solution is known. gPINN is recommended for problems requiring high accuracy in gradient fields or operating with sparse data, whereas standard PINN is advised for strongly nonlinear or computationally constrained scenarios. We synthesize our findings into a practical selection guide; gPINN is recommended for problems requiring high accuracy in gradient fields or operating with sparse data, whereas standard PINN is advised for strongly nonlinear or computationally constrained scenarios. This framework provides a clear, evidence-based policy for algorithm choice in SciML. Beyond numerical comparison, we provide an analytical interpretation linking solver performance to the spectral and stiffness properties of each PDE class, offering a principled basis for algorithm selection. Full article

Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red–green–blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

This review provides a comprehensive assessment of modelling techniques for flanking transmission, with a primary focus on building acoustics. The discussion is organised into three main parts. First, methods that address the full vibro-acoustic problem are examined, distinguishing between deterministic approaches—such as the Finite Element Method, spectral formulations, and modal techniques—and statistical approaches, in particular, Statistical Energy Analysis. Second, simplified characterisation methods for flanking transmission paths are reviewed, with emphasis on the EN 12354 framework for heavy structures and subsequent adaptations for lightweight constructions. Third, the parameters commonly used to characterise vibration transmission at structural junctions are introduced, followed by an extensive review of junction-level models. These include wave-based formulations, finite-dimension models suitable for low and mid frequencies, and simplified regression-based expressions intended for practical design workflows. The review concludes with a curated compilation of experimental data available in the literature. Full article