Search Results (137)

The accurate identification of seismic faults, which serve as crucial fluid migration pathways in hydrocarbon reservoirs, is of paramount importance for reservoir characterization. Traditional interpretation is inefficient. It also struggles with complex geometries, failing to meet the current exploration demands. Deep learning boosts fault identification significantly but struggles with edge accuracy and noise robustness. To overcome these limitations, this research introduces SwiftSeis-AWNet, a novel lightweight and high-precision network. The network is based on an optimized MedNeXt architecture for better fault edge detection. To address the noise from simple feature fusion, a Semantics and Detail Infusion (SDI) module is integrated. Since the Hadamard product in SDI can cause information loss, we engineer an Attention-Weighted Semantics and Detail Infusion (AWSDI) module that uses dynamic multi-scale feature fusion to preserve details. Validation on field seismic datasets from the Netherlands F3 and New Zealand Kerry blocks shows that SwiftSeis-AWNet mitigates challenges like the loss of small-scale fault features and misidentification of fault intersection zones, enhancing the accuracy and geological reliability of automated fault identification. Full article

Traditional antenna arrays for direction-of-arrival (DOA) estimation typically require numerous elements to achieve target performance, increasing system complexity and cost. Reconfigurable intelligent surfaces (RISs) offer a promising alternative, yet their performance critically depends on phase coding design. To address this, we propose a phase coding design method for RIS-aided DOA estimation with a single receiving channel. First, we establish a system model where averaged received signals construct a power-based formulation. This transforms DOA estimation into a compressed sensing-based sparse recovery problem, with the RIS far-field power radiation pattern serving as the measurement matrix. Then, we derive the decoupled expression of the measurement matrix, which consists of the phase coding matrix, propagation phase shifts, and array steering matrix. The phase coding design is then formulated as a Frobenius norm minimization problem, approximating the Gram matrix of the equivalent measurement matrix to an identity matrix. Accordingly, the phase coding design problem is reformulated as a Frobenius norm minimization problem, where the Gram matrix of the equivalent measurement matrix is approximated to an identity matrix. The phase coding is deterministically constructed as the product of a unitary matrix and a partial Hadamard matrix. Simulations demonstrate that the proposed phase coding design outperforms random phase coding in terms of angular estimation accuracy, resolution probability, and the requirement of coding sequences. Full article

The study of various subclasses of univalent functions involving the solutions to various differential equations is not totally new, but studies of analytic functions with respect to $(\mathsf{\ell },\kappa )$-symmetric points are rarely conducted. Here, using a differential operator which was defined using the Hadamard product of Mittag–Leffler function and general analytic function, we introduce a new class of starlike functions with respect to $(\mathsf{\ell },\kappa )$-symmetric points associated with the vertical domain. To define the function class, we use a Carathéodory function which was recently introduced to study the impact of various conic regions on the vertical domain. We obtain several results concerned with integral representations and coefficient inequalities for functions belonging to this class. The results obtained by us here not only unify the recent studies associated with the vertical domain but also provide essential improvements of the corresponding results. Full article

The direct porting of the Range Migration Algorithm to GPUs for three-dimensional (3D) cylindrical synthetic aperture radar (CSAR) imaging faces difficulties in achieving real-time performance while the architecture and programming models of GPUs significantly differ from CPUs. This paper proposes a GPU-optimized implementation for accelerating CSAR imaging. The proposed method first exploits the concentric-square-grid (CSG) interpolation to reduce the computational complexity for reconstructing a uniform 2D wave-number domain. Although the CSG method transforms the 2D traversal interpolation into two independent 1D interpolations, the interval search to determine the position intervals for interpolation results in a substantial computational burden. Therefore, binary search is applied to avoid traditional point-to-point matching for efficiency improvement. Additionally, leveraging the partition independence of the grid distribution of CSG, the 360° data are divided into four streams along the diagonal for parallel processing. Furthermore, high-speed shared memory is utilized instead of high-latency global memory in the Hadamard product for the phase compensation stage. The experimental results demonstrate that the proposed method achieves CSAR imaging on a $1440\times 100\times 128$ dataset in 0.794 s, with an acceleration ratio of 35.09 compared to the CPU implementation and 5.97 compared to the conventional GPU implementation. Full article

Existing studies on ontology evolution lack automated mechanisms to balance semantic coherence and adaptability under real-time uncertainties, particularly in resolving spatiotemporal asymmetry and multidimensional coupling imbalances in dam-break scenarios. Traditional methods such as WordNet’s tree symmetry and FrameNet’s frame symmetry fail to formalize dynamic adjustments through quantitative metrics, leading to path dependency and delayed responses. This study addresses this gap by introducing a novel symmetry-entropy-constrained matrix fusion algorithm, which integrates algebraic direct sum operations and Hadamard product with entropy-driven adaptive weighting. The original contribution lies in the symmetry entropy metric, which quantifies structural deviations during fusion to systematically balance semantic stability and adaptability. This work formalizes ontology evolution as a symmetry-driven optimization process. Experimental results demonstrate that shared concepts between ontologies (s = 3) reduce structural asymmetry by 25% compared to ontologies (s = 1), while case studies validate the algorithm’s ability to reconcile discrepancies between theoretical models and practical challenges in evacuation efficiency and crowd dynamics. This advancement promotes the evolution of traditional emergency management systems towards an adaptive intelligent form. Full article

In this paper, a novel class of meromorphic functions associated with the Mittag–Leffler function $E_{\mu ,\vartheta }\left(z\right)$ is introduced using the Hilbert space operator. In the punctured symmetric domain ${⋓}^{\ast }$, essential properties of this class are systematically investigated. These properties include coefficient inequalities, growth and distortion bounds, as well as weighted and arithmetic mean estimates. Furthermore, the extreme points and radii of geometric properties such as close-to-convexity, starlikeness, and convexity are analyzed in detail. Additionally, the Hadamard product (or convolution) is explored to demonstrate the algebraic structure and stability of the introduced function class under this operation. Integral mean inequalities are also established to provide further insights into the behavior of these functions within the given domain. Full article

Fault diagnosis in pressurized water reactor nuclear power plants faces the challenges of limited labeled data and severe class imbalance, particularly under Design Basis Accident (DBA) conditions. To address these issues, this study proposes a novel framework integrating three key stages: (1) feature selection via a signed directed graph to identify key parameters within datasets; (2) temporal feature encoding using Gramian Angular Difference Field (GADF) imaging; and (3) an improved Deep Subdomain Adaptation Network (DSAN) using weighted Focal Loss and confidence-based pseudo-label calibration. The improved DSAN uses the Hadamard product to achieve feature fusion of ResNet-50 outputs from multiple GADF images, and then aligns both global and class-wise subdomains. Experimental results show that, on the transfer task from the NPPAD source set to the PcTran-simulated AP-1000 target set across five DBA scenarios, the framework raises the overall accuracy from 72.5% to 80.5%, increases macro-F1 to 0.75 and AUC-ROC to 0.84, and improves average minority-class recall to 74.5%, outperforming the original DSAN and four baselines by explicitly prioritizing minority-class samples and mitigating pseudo-label noise. However, our evaluation is confined to simulated data, and validating the framework on actual plant operational logs will be addressed in future work. Full article

We introduce and systematically study a new class $k^{\lambda ,p}(\alpha ,\beta )$ of meromorphic p-valent functions defined by means of the Ruscheweyh-type operator $D_{*}^{\lambda ,p}$, where $p\in \mathbb{N}$, $\lambda >-p$, $0\le \alpha <1$, and $\beta >0$. Membership in this class is characterized through coefficient estimates. Also investigated are growth, distortion, stability under convex combinations, radii of starlikeness and convexity of order $\rho$$(0\le \rho <1)$, convolution, the action of an integral operator of Bernardi–Libera–Livingston type, and neighborhoods. Full article

In this work, we study a numerical method to approximate the exact solution of a simple in situ combustion model. To achieve this, we use the mixed nonlinear complementarity method (MNCP), a variation of the Newton method for solving nonlinear systems, incorporating a single Hadamard product in its formulation. The method is based on an implicit finite difference scheme and a mixed nonlinear complementarity algorithm (FDA-MNCP). One of its main advantages is that it ensures global convergence, unlike the finite difference method and the Newton method, which only guarantee local convergence. We apply this theory to an in situ combustion model, reformulating it in terms of mixed complementarity. Additionally, we compare it with the FDA-NCP method, demonstrating that the FDA-MNCP is computationally more efficient when the spatial discretization is refined. Full article

In this effort, we extend the fractal–fractional operators into the complex plane together with the quantum calculus derivative to obtain a quantum–fractal–fractional operators (QFFOs). Using this newly created operator, we create an entirely novel subclass of analytical functions in the unit disk. Motivated by the concept of differential subordination, we explore the most important geometric properties of this novel operator. This leads to a study on a set of differential inequalities in the open unit disk. We focus on the conditions to obtain a bounded turning function of QFFOs. Some examples are considered, involving special functions like Bessel and generalized hypergeometric functions. Full article

With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability. Full article

This article investigates the applications of the q-Carlson–Shaffer operator on subclasses of q-uniformly starlike functions, introducing the class $S{T}_q(m,c,d,\beta )$. The study establishes a necessary condition for membership in this class and examines its behavior within conic domains. The article delves into properties such as coefficient bounds, the Fekete–Szegö inequality, and criteria defined via the Hadamard product, providing both necessary and sufficient conditions for these properties. Full article

Since multi-view learning leverages complementary information from multiple feature sets to improve model performance, a tensor-based data fusion layer for neural networks, called Multi-View Data Tensor Fusion (MV-DTF), is used. It fuses M feature spaces ${\mathcal{X}}_1,\cdots ,{\mathcal{X}}_M$, referred to as views, in a new latent tensor space, $\mathcal{S}$, of order P and dimension $J_1\times \cdots \times J_P$, defined in the space of affine mappings composed of a multilinear map $\mathcal{T}:{\mathcal{X}}_1\times \cdots \times {\mathcal{X}}_M\to \mathcal{S}$—represented as the Einstein product between a $(P+M)$-order tensor $\mathcal{A}$ anda rank-one tensor, $\mathcal{X}={\mathbf{x}}^{(1)}\otimes \cdots \otimes {\mathbf{x}}^{(M)}$, where ${\mathbf{x}}^{(m)}\in {\mathcal{X}}_m$ is the m-th view—and a translation. Unfortunately, as the number of views increases, the number of parameters that determine the MV-DTF layer grows exponentially, and consequently, so does its computational complexity. To address this issue, we enforce low-rank constraints on certain subtensors of tensor $\mathcal{A}$ using canonical polyadic decomposition, from which M other tensors ${\mathcal{U}}^{(1)},\cdots ,{\mathcal{U}}^{(M)}$, called here Hadamard factor tensors, are obtained. We found that the Einstein product $\mathcal{A}{⊛}_M\mathcal{X}$ can be approximated using a sum of R Hadamard products of M Einstein products encoded as ${\mathcal{U}}^{(m)}{⊛}_1{\mathbf{x}}^{(m)}$, where R is related to the decomposition rank of subtensors of $\mathcal{A}$. For this relationship, the lower the rank values, the more computationally efficient the approximation. To the best of our knowledge, this relationship has not previously been reported in the literature. As a case study, we present a multitask model of vehicle traffic surveillance for occlusion detection and vehicle-size classification tasks, with a low-rank MV-DTF layer, achieving up to $92.81\%$ and $95.10\%$ in the normalized weighted Matthews correlation coefficient metric in individual tasks, representing a significant $6\%$ and $7\%$ improvement compared to the single-task single-view models. Full article

Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an Attention-based Feature Weighting Layer combined with deep neural network layers to achieve precise diabetes prediction. In this study, we utilized the Diabetes-NHANES dataset and the Pima Indians Diabetes dataset. To handle significant missing values and outliers, group median imputation was applied. Oversampling techniques were used to balance the diabetes and non-diabetes groups. The data were processed through an Attention-based Feature Weighting Layer for feature extraction, producing a feature matrix. This matrix was subjected to Hadamard product operations with the raw data to obtain weighted data, which were subsequently input into deep neural network layers for training. The parameters were fine-tuned and the L2 regularization and dropout layers were added to enhance the generalization performance of the model. The model’s reliability was thoroughly assessed through various metrics, including the accuracy, precision, recall, F1 score, mean squared error (MSE), and R2 score, as well as the ROC and AUC curves. The proposed model achieved a prediction accuracy of 98.4% in the Pima Indians Diabetes dataset. When the test dataset was expanded to the large-scale Diabetes-NHANES dataset, which contains 52,390 samples, the test precision of the model improved further to 99.82%, with an AUC of 0.9995. A comparative analysis was conducted using multiple models, including logistic regression with L1 regularization, support vector machine (SVM), random forest, K-nearest neighbors (KNNs), AdaBoost, XGBoost, and the latest semi-supervised XGBoost. The feature extraction method using attention mechanisms was compared with the classical feature selection methods, Lasso and Ridge. The experiments were performed on the same dataset, and the conclusion was that the Attention-based Ensemble Deep Neural Network (AEDNN) outperformed all the aforementioned methods. These results indicate that the model not only performs well on smaller datasets but also fully leverages its advantages on larger datasets, demonstrating strong generalization ability and robustness. The proposed model can effectively assist clinicians in the early screening of diabetes patients. This is particularly beneficial for the preliminary screening of high-risk individuals in large-scale, extensive healthcare datasets, followed by detailed examination and diagnosis. Compared to the existing methods, our AEDNN model showed an overall performance improvement of 1.75%. Full article

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer. Full article