Search Results (398)

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

Generative adversarial networks (GANs) are increasingly used for image enhancement tasks like denoising, super-resolution, and artifact removal. In this study, we propose a robust GAN-based architecture featuring a U-Net-style generator and DCNN-based discriminator, optimized for real-time enhancement. Key contributions include multi-task image refinement using residual and attention modules. Our method is tested on paired datasets comprising low-light medical and artificially degraded images. Results show significant improvements in visual clarity, reduced noise, and improved resolution compared to traditional methods. Evaluation metrics such as inception score (IS) and Fréchet inception distance (FID) confirm the system’s performance. The optimal learning rate (0.0008) was empirically selected for training stability. These results validate the proposed GAN’s efficiency for practical deployment in domains requiring high-quality imaging. Full article

As a critical technology of autonomous mining trucks, object detection directly determines system safety and operational reliability. However, autonomous mining trucks often work in dusty open-pit environments, in which dusty interference significantly degrades the accuracy of object detection. To overcome the problem mentioned above, a multi-branch feature interaction and location detection network (MBFILNet) is proposed in this study, consisting of multi-branch feature interaction with differential operation (MBFI-DO) and depthwise separable convolution-enhanced non-local attention (DSC-NLA). On one hand, MBFI-DO not only strengthens the extraction of channel-wise semantic features but also improves the representation of salient features of images with dusty interference. On the other hand, DSC-NLA is used to capture long-range spatial dependencies to focus on target-object structural information. Furthermore, a custom dataset called Dusty Open-pit Mining (DOM) is constructed, which is augmented using a cycle-consistent generative adversarial network (CycleGAN). Finally, a large number of experiments based on DOM are conducted to evaluate the performance of MBFILNet in dusty open-pit environments. The results show that MBFILNet achieves a mean Average Precision (mAP) of 72.0% based on the DOM dataset, representing a 1.3% increase compared to the Featenhancer model. Moreover, in comparison with YOLOv8, there is an astounding 2% increase in the mAP based on MBFILNet, demonstrating detection accuracy in dusty open-pit environments can be effectively improved with the method proposed in this paper. Full article

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems. Full article

With the rapid development of information technology and the deep integration of the Internet platform, the scale and form of financial transactions continue to grow and expand, significantly improving users’ payment experience and life efficiency. However, financial transactions bring us convenience but also expose many security risks, such as money laundering activities, forged checks, and other financial fraud that occurs frequently, seriously threatening the stability and security of the financial system. Due to the imbalance between the proportion of normal and abnormal transactions in the data, most of the existing deep learning-based methods still have obvious deficiencies in learning small numbers sample classes, context modeling, and computational complexity control. To address these deficiencies, this paper proposes a symmetrical structure-based GAN-CNN model for lightweight financial fraud detection. The symmetrical structure can improve the feature extraction and fusion ability and enhance the model’s recognition effect for complex fraud patterns. Synthetic fraud samples are generated based on a GAN to alleviate category imbalance. Multi-scale convolution and attention mechanisms are designed to extract local and global transaction features, and adaptive aggregation and context encoding modules are introduced to improve computational efficiency. We conducted numerous replicate experiments on two public datasets, YelpChi and Amazon. The results showed that on the Amazon dataset with a 50% training ratio, compared with the CNN-GAN model, the accuracy of our model was improved by 1.64%, and the number of parameters was reduced by approximately 88.4%. Compared with the hybrid CNN-LSTM–attention model under the same setting, the accuracy was improved by 0.70%, and the number of parameters was reduced by approximately 87.6%. The symmetry-based lightweight architecture proposed in this work is novel in terms of structural design, and the experimental results show that it is both efficient and accurate in detecting imbalanced transactions. Full article

Natural gas wells operate under complex conditions with frequent environmental disturbances. Fault types vary significantly and often present weak signals, affecting both safety and efficiency. This paper proposes an intelligent fault-diagnosis method based on a CNN-Transformer model using real-time wellsite data. A time series diffusion model is applied to enhance small-sample data by generating synthetic fault samples, and the CNN-Transformer model extracts both local and global features from time series inputs to improve fault recognition in complex scenarios. Validation on a real-world dataset demonstrates that the proposed method achieves a macro F1-Score of 99.52% in multi-class fault diagnosis, significantly outperforming baseline models (1D-CNN: 95.83%, LSTM: 93.54%, GRU: 94.98%). Quantitative analysis confirms the diffusion model’s superiority in data augmentation, with lower Earth Mover’s Distance (0.087), KL Divergence (0.245), and Mean Squared Error (0.298) compared to GAN and VAE variants. Ablation studies show that removing diffusion-based augmentation leads to a 14.96% drop in F1-Score, highlighting its critical role in mitigating class imbalance. Results validate the diffusion model’s effectiveness for data augmentation and the CNN-Transformer’s superior ability to capture complex time series patterns, providing theoretical support and practical tools for intelligent monitoring and maintenance in natural gas well systems. Full article

Deep neural networks have led to a substantial increase in multifaceted classification tasks by making use of large-scale and diverse annotated datasets. However, diverse optical coherence tomography (OCT) datasets in cardiovascular imaging remain an uphill task. This research focuses on improving the diversity and generalization ability of augmentation architectures while maintaining the baseline classification accuracy for coronary atrial plaques using a novel dual-generator and dynamically fused discriminator conditional generative adversarial network (DGDFGAN). Our method is demonstrated on an augmented OCT dataset with 6900 images. With dual generators, our network provides the diverse outputs for the same input condition, as each generator acts as a regulator for the other. In our model, this mutual regularization enhances the ability of both generators to generalize better across different features. The fusion discriminators use one discriminator for classification purposes, hence avoiding the need for a separate deep architecture. A loss function, including the SSIM loss and FID scores, confirms that perfect synthetic OCT image aliases are created. We optimize our model via the gray wolf optimizer during model training. Furthermore, an inter-comparison and recorded SSID loss of 0.9542 ± 0.008 and a FID score of 7 are suggestive of better diversity and generation characteristics that outperform the performance of leading GAN architectures. We trust that our approach is practically viable and thus assists professionals in informed decision making in clinical settings. Full article

Due to the challenges of low resolution and incomplete data in the process of power data collection and transmission and the lack of detail in the power data super-resolution algorithm, this paper proposes a generative adversarial network super-resolution perception model based on a linear attention mechanism. It uses the adversarial training mechanism of generator and discriminator to restore high-resolution power data from low-resolution power data. In the generator, the deep residual network structure is innovatively combined with the multi-scale linear attention mechanism, and the linear rectifier unit that can be dynamically learned is combined to improve the model’s ability to extract power data features. The discriminator employs a multi-scale architecture embedded with a dual-attention module, integrating both global and local features to enhance the model’s ability to capture fine details. Experiments were conducted on a dataset of multiple monitoring points in a city in East China. Experimental results indicate that the proposed Lmla-GAN delivers an overall average SSIM improvement of approximately 6.7% over the four baseline models-Bicubic, SRCNN, SubPixelCNN, and VDSR. Full article

To tackle phenotypic variability and detection accuracy issues of tea shoots in open-air gardens due to lighting and varietal differences, this study proposes Tea CycleGAN and a data augmentation method. It combines multi-scale image style transfer with spatial consistency dataset generation. Using Longjing 43 and Zhongcha 108 as cross-domain objects, the generator integrates SKConv and a dynamic multi-branch residual structure for multi-scale feature fusion, optimized by an attention mechanism. A deep discriminator with more conv layers and batch norm enhances detail discrimination. A global–local framework trains on 600 × 600 background and 64 × 64 tea shoots regions, with a restoration-paste strategy to preserve spatial consistency. Experiments show Tea CycleGAN achieves FID scores of 42.26 and 26.75, outperforming CycleGAN. Detection using YOLOv7 sees mAP rise from 73.94% to 83.54%, surpassing Mosaic and Mixup. The method effectively mitigates lighting/scale impacts, offering a reliable data augmentation solution for tea picking. Full article

Point cloud imaging technology usually faces the problem of point cloud sparsity, which leads to a lack of important geometric detail. There are many point cloud upsampling networks that have been designed to solve this problem. However, the existing methods have limitations in local–global relation understanding, leading to contour distortion and many local sparse regions. To this end, PU-DZMS is proposed with two components. (1) the Dense Zoom Encoder (DENZE) is designed to capture local–global features by using ZOOM Blocks with a dense connection. The main module in the ZOOM Block is the Zoom Encoder, which embeds a Transformer mechanism into the down–upsampling process to enhance local–global geometric features. The geometric edge of the point cloud would be clear under the DENZE. (2) The Multi-Scale Complementary Regression (MSCR) module is designed to expand the features and regress a dense point cloud. MSCR obtains the features’ geometric distribution differences across scales to ensure geometric continuity, and it regresses new points by adopting cross-scale residual learning. The local sparse regions of the point cloud would be reduced by the MSCR module. The experimental results on the PU-GAN dataset and the PU-Net dataset show that the proposed method performs well on point cloud upsampling tasks. Full article

Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent’s capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments. Full article

Accurate and uncertainty-aware wind power forecasting is essential for reliable and cost-effective power system operations. This paper presents a novel probabilistic forecasting framework based on diffusion probabilistic models. We adopted a two-stage modeling strategy—a deterministic predictor first generates baseline forecasts, and a conditional diffusion model then learns the distribution of residual errors. Such a two-stage decoupling strategy improves learning efficiency and sharpens uncertainty estimation. We employed the elucidated diffusion model (EDM) to enable flexible noise control and enhance calibration, stability, and expressiveness. For the generative backbone, we introduced a time-series-specific diffusion Transformer (TimeDiT) that incorporates modular conditioning to separately fuse numerical weather prediction (NWP) inputs, noise, and temporal features. The proposed method was evaluated using the public database from ten wind farms in the Global Energy Forecasting Competition 2014 (GEFCom2014). We further compared our approach with two popular baseline models, i.e., a distribution parameter regression model and a generative adversarial network (GAN)-based model. Results showed that our method consistently achieves superior performance in both deterministic metrics and probabilistic accuracy, offering better forecast calibration and sharper distributions. Full article

Background: The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features. Methods: We developed a specialized denoising diffusion probabilistic model incorporating an attention-enhanced UNet architecture with strategically placed attention blocks across five hierarchical levels. The model was trained and evaluated on the OCMR dataset and compared against state-of-the-art generative approaches including StyleGAN2-ADA, WGAN-GP, and VAE baselines. Results: Our approach achieved superior image quality with a Fréchet Inception Distance of 77.78, significantly outperforming StyleGAN2-ADA (117.70), WGAN-GP (227.98), and VAE (325.26). Structural similarity metrics demonstrated excellent performance (SSIM: 0.720 ± 0.143; MS-SSIM: 0.925 ± 0.069). Clinical validation by cardiac radiologists yielded discrimination accuracy of only 60.0%, indicating near-realistic image quality that is challenging for experts to distinguish from real images. Comprehensive anatomical analysis revealed that 13 of 20 cardiac metrics showed no significant differences between real and synthetic images, with particularly strong preservation of left ventricular features. Discussion: The generated synthetic images demonstrate high anatomical fidelity with expert-level quality, as evidenced by the difficulty radiologists experienced in distinguishing synthetic from real images. The strong preservation of cardiac anatomical features, particularly left ventricular characteristics, indicates the model’s potential for medical image analysis applications. Conclusions: This work establishes diffusion models as a robust solution for cardiac MRI data augmentation, successfully generating anatomically accurate synthetic images that enhance downstream clinical applications while maintaining diagnostic fidelity. Full article

Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework’s adaptability and practical applicability in real-world intrusion detection and response systems. Full article

With the growing demand for ecological monitoring and geological exploration, high-quality satellite remote-sensing imagery has become indispensable for accurate information extraction and automated analysis. However, haze reduces image contrast and sharpness, significantly impairing quality. Existing dehazing methods, primarily designed for natural images, struggle with remote-sensing images due to their complex imaging conditions and scale diversity. Given this, we propose a novel Multi-Scale Contextual Attention Generative Adversarial Network (MCA-GAN), specifically designed for satellite image dehazing. Our method integrates multi-scale feature extraction with global contextual guidance to enhance the network’s comprehension of complex remote-sensing scenes and its sensitivity to fine details. MCA-GAN incorporates two self-designed key modules: (1) a Multi-Scale Feature Aggregation Block, which employs multi-directional global pooling and multi-scale convolutional branches to bolster the model’s ability to capture land-cover details across varying spatial scales; (2) a Dynamic Contextual Attention Block, which uses a gated mechanism to fuse three-dimensional attention weights with contextual cues, thereby preserving global structural and chromatic consistency while retaining intricate local textures. Extensive qualitative and quantitative experiments on public benchmarks demonstrate that MCA-GAN outperforms other existing methods in both visual fidelity and objective metrics, offering a robust and practical solution for remote-sensing image dehazing. Full article