Search Results (10,628)

This study is grounded in the macro-context of smart agriculture and global food security. Due to population growth and climate change, precise and efficient monitoring of crop distribution and growth is vital for stable production and optimal resource use. Remote sensing combined with deep learning enables multi-scale agricultural monitoring from field identification to disease diagnosis. However, current models face three deployment bottlenecks: high complexity hinders operation on edge devices; scarce labeled data causes overfitting in small-sample cases; and there is insufficient generalization across regions, crops, and imaging conditions. These issues limit the large-scale adoption of intelligent agricultural technologies. To tackle them, this paper proposes a lightweight crop recognition model, MAF-RecNet. It aims to achieve high accuracy, efficiency, and strong generalization with limited data through structural optimization and attention mechanism fusion, offering a viable path for deployable intelligent monitoring systems. Built on a U-Net with a pre-trained ResNet18 backbone, MAF-RecNet integrates multiple attention mechanisms (Coordinate, External, Pyramid Split, and Efficient Channel Attention) into a hybrid attention module, improving multi-scale feature discrimination. On the Southern Hebei Farmland dataset, it achieves 87.57% mIoU and 95.42% mAP, outperforming models like SegNeXt and FastSAM, while maintaining a balance of efficiency (15.25 M parameters, 21.81 GFLOPs). The model also shows strong cross-task generalization, with mIoU scores of 80.56% (Wheat Health Status Dataset in Southern Hebei), 90.20% (Global Wheat Health Dataset), and 84.07% (Corn Health Status Dataset). Ablation studies confirm the contribution of the attention-enhanced skip connections and decoder. This study not only provides an efficient and lightweight solution for few-shot agricultural image recognition but also offers valuable insights into the design of generalizable models for complex farmland environments. It contributes to promoting the scalable and practical application of artificial intelligence technologies in precision agriculture. 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

As the raw material for tea making, the quality of fresh tea leaves directly affects the quality of finished tea. Traditional manual sorting and machine sorting struggle to meet the requirements for high-quality tea processing. Based on machine vision and deep learning, intelligent grading technology has been applied to the automated sorting of fresh tea leaves. However, when faced with machine-picked tea leaves, the characteristics of complex morphology, small target recognition size, and dense spatial distribution can interfere with accurate category recognition, which in turn limits classification accuracy and consistency. Therefore, we propose an enhanced YOLOv12 detection framework that integrates three key modules—C3k2_EMA, A2C2f_DYT, and RFAConv—to strengthen the model's ability to capture delicate tea bud features, thereby improving detection accuracy and robustness. Experimental results demonstrate that the proposed method achieves precision, recall, and mAP@0.5 of 81.2%, 90.6%, and 92.7% in premium tea recognition, effectively supporting intelligent and efficient tea harvesting and sorting operations. This study addresses the challenges of subtle fine-grained differences, small object sizes, variable morphology, and complex background interference in premium tea bud images. The proposed model not only achieves high accuracy and robustness in fine-grained tea bud detection but also provides technical feasibility for intelligent fresh tea leaves classification and production monitoring. Full article

Accurate photovoltaic (PV) power forecasting under extreme weather conditions remains challenging due to the non-stationary and multi-modal nature of meteorological influences. This study proposes a novel four-stage learning framework integrating signal decomposition, hyperparameter optimization, temporal dependency learning, and residual compensation to enhance forecasting resilience during El Niño–Southern Oscillation (ENSO) climate transitions. The framework employs CEEMDAN for fluctuation mode decoupling, TOC for global hyperparameter optimization, Transformer model for spatiotemporal dependency learning, and EEMD-GRU for error correction. Experimental validation utilized a comprehensive dataset from Australia’s Yulara power station comprising 104,269 samples at 5 min resolution throughout 2024, covering a complete ENSO transition period. Compared against baseline Transformer model and CNN-BiLSTM models, the proposed framework achieved nRMSE of 1.08%, 7.04%, and 2.81% under sunny, rainy, and sandstorm conditions, respectively, with corresponding R² values of 0.99981, 0.99782, and 0.99947. Cross-year validation (2023 to 2025) demonstrated maintained performance with nRMSE ranging from 4.68% to 15.88% across different temporal splits. The framework’s modular architecture enables targeted handling of distinct physical processes governing different weather regimes, providing a structured approach for climate-resilient PV forecasting that maintains 2.56% energy consistency error while adapting to rapid meteorological shifts. Full article

White matter injury (WMI) and blood–brain barrier (BBB) disruption contribute to neurological and cognitive deficits in intracerebral hemorrhage (ICH), with no effective pharmacological treatments available. Puerarin, with anti-inflammatory, anti-apoptotic, and antioxidant properties, exhibits neuroprotective potential. Here, mice subjected to ICH were treated with puerarin for 14 days. Neurological function, cerebral perfusion, and BBB integrity were assessed using behavioral tests, laser speckle imaging, Evans blue assays, immunofluorescence, Western blotting, and MRI. Integrated transcriptomics, machine learning, network pharmacology, molecular docking, and dynamics simulations were used to identify key targets. Puerarin improved neurological outcomes, reduced BBB permeability, enhanced microvascular perfusion, and attenuated WMI. Twenty-six hub genes were identified, with PARP1 and AKT1 correlated with OLIG2 and MBP, enriched in the cGAS-STING and AKT1-mTOR pathways. Molecular simulations indicated stable puerarin–cGAS interactions, validated experimentally: puerarin suppressed cGAS-STING activation, reduced oligodendrocyte apoptosis, and promoted remyelination. These results provide new insights into ICH pathogenesis and support puerarin as a potential therapeutic agent for BBB disruption and WMI. Full article

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article

The Thailand 4.0 agenda elevates entrepreneurship education (EE) as a lever to escape the middle-income, inequality, and imbalance traps, yet EE remains weakly embedded in basic education—especially in Thai language. We designed and piloted a community-economy context-based learning model integrating EE (CEC-EE) for Grade 12 Thai via a two-cycle R&D process: needs analysis (surveys and focus groups with teachers and students) and prototype development. The model operationalizes six instructional steps (6Cs: connect, comprehend, clarify, construct, carry over, and conclude) anchored in Mae Chan’s community economy and targets entrepreneurial language skills (ELSs) consisting of analytical reading and creative writing. In a one-group pretest–posttest with Grade 12 students (n = 32), academic achievement and ELSs—analytical reading and creative writing—improved markedly. Posttest means exceeded pretests with very large effect. Experts rated the model appropriate, feasible, and useful; teachers and students reported high perceived value alongside concerns about implementation cost, support capacity, and student readiness. The CEC-EE model offers a context-responsive pathway for embedding EE in Thai-language instruction; future work should employ comparative designs, multi-site samples, and cost-effectiveness analyses to assess scalability and sustained impact. Full article

Rapid and accurate diagnosis of nitrogen (N) and phosphorus (P) is crucial for Hydrangea macrophylla nursery management. Traditional methods are time-consuming, and existing non-destructive studies rarely target ornamental plants or support joint N-P diagnosis at the early growth stage. A total of 339 RGB images were captured from potted hydrangeas grown under varying N and P levels at the seedling stage, with 65 phenotypic traits (color, texture, and morphology) extracted. Nutritional status (deficient, optimal, and surplus) was categorized with reference to plant nutrition indices. Discriminant models were then developed using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest (RF), and probabilistic neural network (PNN). The model performances were evaluated using overall accuracy, precision, recall, F1-score, and Cohen’s Kappa coefficient (κ). As a result, CNN achieved 82.65% accuracy (κ = 0.7392) for N classification, and SVM reached 83.65% accuracy (κ = 0.7357) for P classification. Color-related traits dominated the top five contributing features, indicating a stronger correlation with N and P status. This work offers a practical solution for real-time, low-cost, and non-destructive nutrient diagnosis, supporting precision fertilization and enhancing environmental sustainability in nursery production. Full article

The categorization of real-time defects in heterogeneous domains is a long-standing challenge in the field of industrial visual inspection systems, primarily due to significant visual variations and the lack of labelled information in real-world inspection settings. This work presents the Adaptive Attention DropBlock (AADB) framework, a lightweight deep learning framework that was developed to promote cross-domain defect detection using attention-guided regularization. The proposed architecture integrates the Convolutional Block Attention Module (CBAM) and an organized DropBlock-based regularization scheme, creating a unified and robust framework. Although CBAM-based approaches improve localization of defect-related areas and traditional DropBlock provides a generic spatial regularization, neither of them alone is specifically designed to reduce domain overfitting. To address this limitation, AADB combines attention-directed feature refinement with a progressive, transfer-aware dropout policy that promotes the learning of domain-invariant representations. The proposed model is built on a MobileNetV2 base and trained through a two-phase transfer learning regime, where the first phase consists of pretraining on a source domain and the second phase consists of adaptation to a visually dissimilar target domain with constrained supervision. The overall analysis of a metal surface defect dataset (source domain) and an aircraft surface defect dataset (target domain) shows that AADB outperforms CBAM-only, DropBlock-only, and conventional MobileNetV2 models, with an overall accuracy of 91.06%, a macro-F1 of 0.912, and a Cohen’s k of 0.866. Improved feature separability and localization of error are further described by qualitative analyses using Principal Component Analysis (PCA) and Grad-CAM. Overall, the framework provides a practical, interpretable, and edge-deployable solution to the classification of cross-domain defects in the industrial inspection setting. Full article

In practical applications of deep learning-based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems, new target categories emerge continuously. This requires the systems to learn incrementally—acquiring new knowledge while retaining previously learned information. To mitigate catastrophic forgetting in Class-Incremental Learning (CIL), this paper proposes a CIL method for SAR ATR named Multi-center Prototype Feature Distribution Reconstruction (MPFR). It has two core components. First, a Multi-scale Hybrid Attention feature extractor is designed. Trained via a feature space optimization strategy, it fuses and extracts discriminative features from both SAR amplitude images and Attribute Scattering Center data, while preserving feature space capacity for new classes. Second, each class is represented by multiple prototypes to capture complex feature distributions. Old class knowledge is retained by modeling their feature distributions through parameterized Gaussian diffusion, alleviating feature confusion in incremental phases. Experiments on public SAR datasets show MPFR achieves superior performance compared to existing approaches, including recent SAR-specific CIL methods. Ablation studies validate each component’s contribution, confirming MPFR’s effectiveness in addressing CIL for SAR ATR without storing historical raw data. Full article

We developed a hybrid system combining deep learning-based recognition with fuzzy inference to enhance the detection, recognition, and identification of maritime targets. In the system, deep learning provides strong feature extraction, while fuzzy logic mitigates uncertainty in low-visibility or occluded conditions. The system uses confidence score, screen ratio, and estimated distance as input and processes them through fuzzy inference with triangular membership functions and center of area defuzzification. This integration improves decision robustness and suppresses input noise. Experimental results demonstrate enhanced stability and reduced misjudgment in dynamic maritime environments, highlighting the applicability of a hybrid deep learning–fuzzy inference systems to intelligent ships and unmanned maritime vehicle sensing tasks. Full article

Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this study proposes a Policy-Regulated and Aligned Deep Q-Network (PRA-DQN) for cooperative multi-intersection signal control. A differentiable policy function is introduced and explicitly trained to align with the optimal Q-value-derived target distribution, yielding more stable and interpretable policy behavior. In addition, a cooperative reward structure integrating local delay, movement pressure, and upstream–downstream interactions enables agents to simultaneously optimize local efficiency and regional coordination. A parameter-sharing multi-agent framework further enhances scalability and learning consistency across intersections. Simulation experiments conducted on a 2 × 2 SUMO grid show that PRA-DQN consistently outperforms fixed-time, classical DQN, distributed DQN, and pressure/wave-based baselines. Compared with fixed-time control, PRA-DQN reduces maximum queue length by 21.17%, average queue length by 18.75%, and average waiting time by 17.71%. Moreover, relative to classical DQN coordination, PRA-DQN achieves an additional 7.53% reduction in average waiting time. These results confirm the effectiveness and superiority of the proposed method in suppressing congestion propagation and improving network-level traffic performance. The proposed PRA-DQN provides a practical and scalable basis for real-time deployment of coordinated signal control and can be readily extended to larger networks and time-varying demand conditions. Full article

The detection of defects in Printed Circuit Boards (PCBs) is a critical yet challenging task in industrial quality control, characterized by the prevalence of small targets and complex backgrounds. While deep learning models like YOLOv5 have shown promise, they often lack the ability to quantify predictive uncertainty, leading to overconfident errors in challenging scenarios—a major source of false alarms and reduced reliability in automated manufacturing inspection lines. From a Bayesian perspective, this overconfidence signifies a failure in probabilistic calibration, which is crucial for trustworthy automated inspection. To address this, we propose MFE-YOLO, a Bayesian-enhanced detection framework built upon YOLOv5 that systematically integrates uncertainty-aware mechanisms to improve both accuracy and operational reliability in real-world settings. First, we construct a multi-background PCB defect dataset with diverse substrate colors and shapes, enhancing the model’s ability to generalize beyond the single-background bias of existing data. Second, we integrate the Convolutional Block Attention Module (CBAM), reinterpreted through a Bayesian lens as a feature-wise uncertainty weighting mechanism, to suppress background interference and amplify salient defect features. Third, we propose a novel FIoU loss function, redesigned within a probabilistic framework to improve bounding box regression accuracy and implicitly capture localization uncertainty, particularly for small defects. Extensive experiments demonstrate that MFE-YOLO achieves state-of-the-art performance, with mAP@0.5 and mAP@0.5:0.95 values of 93.9% and 59.6%, respectively, outperforming existing detectors, including YOLOv8 and EfficientDet. More importantly, the proposed framework yields better-calibrated confidence scores, significantly reducing false alarms and enabling more reliable human-in-the-loop verification. This work provides a deployable, uncertainty-aware solution for high-throughput PCB inspection, advancing toward trustworthy and efficient quality control in modern manufacturing environments. Full article

Fracture–vuggy carbonate reservoirs exhibit strong heterogeneity, spatial discontinuity, and highly variable porosity, which limit the effectiveness of traditional seismic attributes and conventional inversion. Focusing on the XX well block in the Tarim Basin, this study develops a rock-physics-constrained Physics-Constrained TransUNet method for intelligent porosity prediction. A carbonate-specific rock-physics model is first established, considering mineral composition, pore type, and water saturation, ensuring physical consistency between porosity, elastic parameters, and seismic responses. On this basis, a deep-learning framework integrating U-Net multi-scale feature extraction and Transformer global modeling is constructed. By embedding rock-physics priors, regularization constraints, and log-derived porosity labels, the method forms a unified physics- and data-driven inversion scheme. Applications to multiple deep wells and 3D post-stack seismic data from the FI7 fault zone demonstrate stable training, rapid convergence, and strong capability in capturing nonlinear porosity–seismic relationships. Compared with conventional inversion, the proposed approach significantly improves prediction accuracy in cavern-dominated intervals, fractured zones, and areas with abrupt porosity changes, while maintaining robust lateral continuity. Inter-well sections and target-layer slices further verify its effectiveness in identifying fracture–dissolution–vug composite reservoirs. The method provides a practical and reliable workflow for porosity prediction in ultra-deep carbonate reservoirs, supporting fine reservoir characterization and sweet-spot evaluation. Full article

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article