Search Results (834)

Deep learning–based pansharpening methods for remote sensing have advanced rapidly in recent years. However, current methods still face three limitations that directly affect reconstruction quality. Content adaptivity is often implemented as an isolated step, which prevents effective interaction across scales and feature domains. Dynamic multi-scale mechanisms also remain constrained, since their scale selection is usually guided by global statistics and ignores regional heterogeneity. Moreover, frequency and spatial cues are commonly fused in a static manner, leading to an imbalance between global structural enhancement and local texture preservation. To address these issues, we design three complementary modules. We utilize the Adaptive Convolution Unit (ACU) to generate content-aware kernels through local feature clustering, thereby achieving fine-grained adaptation to diverse ground structures. We also develop the Multi-Scale Receptive Field Selection Unit (MSRFU), a module providing flexible scale modeling by selecting informative branches at varying receptive fields. Meanwhile, we incorporate the Frequency–Spatial Attention Unit (FSAU), designed to dynamically fuse spatial representations with frequency information. This effectively strengthens detail reconstruction while minimizing spectral distortion. Specifically, we propose the Hybrid Attention Fusion Network (HAFNet), which employs the Hybrid Attention-Driven Residual Block (HARB) as the fundamental utility to dynamically integrate the above three specialized components. This design enables dynamic content adaptivity, multi-scale responsiveness, and cross-domain feature fusion within a unified framework. Experiments on public benchmarks confirm the effectiveness of each component and demonstrate HAFNet’s state-of-the-art performance. Full article

Surgical robots are increasingly utilized in medicine for their reliability and convenience. An accurate kinematic model is essential for precise robot control and enhanced surgical safety. However, the high nonlinearity and computational complexity of kinematics pose significant challenges to traditional numerical methods. This study designs a surgical robotic arm and establishes the motion mapping relationship between the joint space and the end-effector workspace. Subsequently, a hybrid kinematic estimation model based on deep pyramid convolutional neural network (DPCNN) is proposed, which integrates data sampling and an attention mechanism to improve computational accuracy. The Latin hypercube sampling technique is used to improve the uniformity of dataset sampling, and the triplet-fusion self-attention mechanism (TFSAM) is employed for multi-scale feature information. Experimental results show that the TFSAM-DPCNN model achieves coefficient of determination (R²) values exceeding 0.99 across all testing scenarios. Compared with other models, the proposed model reduced the root mean square error (RMSE) by up to 81.34%, exhibiting superior performance. Furthermore, the developed 3D simulation platform validates the effectiveness of the proposed model. This study offers a robust solution for multi-degree-of-freedom robot modeling, with potential applications across a range of robotic motion control systems. Full article

Wind energy is a core pillar of global green and sustainable energy transition. However, existing wind power prediction models face three key challenges: traditional long short-term memory (LSTM) models struggle to capture long-term temporal dependencies efficiently and have high training latency, while Transformer-based models exhibit excessive computational complexity and are prone to overfitting for short-term fluctuating data; meanwhile, few models integrate seasonal trend modeling with multi-scale temporal feature extraction, leading to large prediction errors in seasonal transitions. To address these issues, this paper proposes a hybrid prediction framework combining a novel T-LSTM recurrent unit with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The T-LSTM unit fuses a simplified Transformer module and an improved LSTM structure. Thus, the design can synergistically capture both short-term fluctuations and long-term dependencies in wind power data. Complementarily, SARIMA is integrated via weighted fusion to model seasonal trends, addressing the neglect of seasonal characteristics in existing deep learning models. A diverse set of benchmark methods for wind power prediction are selected for comparison, including LSTM, convolutional neural network-gated recurrent unit (CNN-GRU), ns_Transformer, Autoformer, Reformer and least squares support vector machine (LSSVM), with experiments conducted across various prediction horizons. The results show that the proposed T-LSTM model outperformed most benchmark methods in key evaluation metrics across multiple prediction horizons and exhibited no statistically significant difference from Autoformer only in the 90 min horizon, validating its superiority in handling complex wind power time series. Full article

Image deraining is a crucial preprocessing task for enhancing the robustness of high-level vision systems under adverse weather conditions. However, most of the existing methods are limited to a single RGB color space, and it is difficult to effectively separate high-frequency rain streaks from low-frequency backgrounds, resulting in color distortion and detail loss in the restored image. Therefore, a rain removal network that combines dual-color space and frequency domain priors is proposed. Specifically, the devised network employs a dual-branch Transformer architecture to extract color and structural features from the RGB and YCbCr color spaces, respectively. Meanwhile, a Hybrid Attention Feedforward Block (HAFB) is constructed. HAFB achieves feature enhancement and regional focus through a progressive perception selection mechanism and a multi-scale feature extraction architecture, thereby effectively separating rain streaks from the background. Furthermore, a Wavelet-Gated Cross-Attention module is designed, including a Wavelet-Enhanced Attention Block (WEAB) and a Dual Cross-Attention module (DCA). This design enhances the complementary fusion of structural information and color features through frequency-domain guidance and bidirectional semantic interaction. Finally, experimental results on multiple datasets (i.e., Rain100L, Rain100H, Rain800, Rain12, and SPA-Data) demonstrate that the proposed method outperforms other approaches. Full article

Forest structural parameters, such as canopy closure, stand density, diameter at breast height, tree height, leaf area index, stand age, and biomass, are fundamental for quantifying forest ecosystem functioning and supporting sustainable forest management. Remote sensing has become an indispensable tool for forest structural parameter estimation. Commonly used data sources include optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs), and multisource data fusion. Correspondingly, modeling approaches have evolved from empirical and statistical methods to machine learning, deep learning, and hybrid physical-data-driven models, enabling improved characterization of nonlinear and complex forest structures. Each data source and modeling strategy offers unique strengths and limitations with respect to accuracy, scalability, interpretability, and transferability. This review provides a concise synthesis of recent advances in remote sensing data sources and model algorithms for forest structural parameter estimation, evaluates the strengths and limitations of different sensors and algorithms, and highlights key challenges related to uncertainty, scalability, transferability, and model interpretability. Finally, future research directions are discussed, emphasizing cross-scale integration, multisource data fusion, and physically informed deep learning frameworks as promising pathways toward more accurate, robust, and ecologically interpretable forest structural parameter modeling at regional to global scales. Full article

Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and elevation features. In this work, we propose a Hybrid Mamba–Graph Fusion Network (HMGF-Net) with Multi-Stage Pseudo-Label Refinement (MS-PLR) for semi-supervised hyperspectral–LiDAR classification. The framework employs a spectral–spatial HSI backbone combining 3D–2D convolutions, a compact LiDAR CNN encoder, Mamba-style state-space sequence blocks for long-range spectral and cross-modal dependency modeling, and a graph fusion module that propagates information over a heterogeneous pixel graph. Semi-supervised learning is realized via a three-stage pseudolabeling pipeline that progressively filters, smooths, and re-weights pseudolabels based on prediction confidence, spatial–spectral consistency, and graph neighborhood agreement. We validate HMGF-Net on three benchmark hyperspectral–LiDAR datasets. Compared with a set of eight state-of-the-art (SOTA) baselines, including 3D-CNNs, SSRN, HybridSN, transformer-based models such as SpectralFormer, multimodal CNN–GCN fusion networks, and recent semi-supervised methods, the proposed approach delivers consistent gains in overall accuracy, average accuracy, and Cohen’s kappa, especially in low-label regimes (10% labeled pixels). The results highlight that the synergy between sequence modeling and graph reasoning in combination with carefully designed pseudolabel refinement is essential to maximizing the benefit of abundant unlabeled samples in multimodal remote sensing scenarios. Full article

Pansharpening is a fundamental image fusion technique used to enhance the spatial resolution of remote sensing imagery; however, it inevitably introduces spectral distortions that compromise the reliability of downstream analyses. Existing no-reference (NR) quality assessment methods often fail to exclusively isolate these spectral errors from spatial artifacts or lack sensitivity to specific radiometric inconsistencies. To address this gap, this paper proposes a novel No-Reference Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI) specifically designed for pansharpened images. The methodology extracts a hybrid feature set, combining First Digit Distribution (FDD) features derived from Benford’s Law in the hyperspherical color space (HCS) and Color Moment (CM) features. These features are then used to fit Multivariate Gaussian (MVG) models to both the original multispectral and fused images, with spectral distortion quantified via the Mahalanobis distance between their statistical parameters. Experiments on the NBU dataset showed that the MVG-SDI correlates more strongly with standard full-reference benchmarks (such as SAM and CC) than existing NR methods like QNR. Tests with simulated distortions confirmed that the proposed index remains stable and accurate even when facing specific spectral degradations like hue shifts or saturation changes. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

In high-frequency circuit design, parameters such as the characteristic impedance and propagation constant of transmission lines directly affect key performance metrics, including signal integrity and power transmission efficiency. To address the challenge of optimizing impedance matching for high-frequency PCB transmission lines, this study applies a hybrid deep Q-network—genetic algorithm (DQN-GA) that integrates deep reinforcement learning with a genetic algorithm (GA). Unlike existing methods that primarily focus on predictive modeling or single-algorithm optimization, the proposed approach introduces a bidirectional interaction mechanism for algorithm fusion: transmission line structures learned by the deep Q-network (DQN) are encoded as chromosomes to enhance the diversity of the genetic algorithm population; simultaneously, high-fitness individuals from the genetic algorithm are decoded and stored in the experience replay pool of the DQN to accelerate its convergence. Simulation results demonstrate that the DQN-GA algorithm significantly outperforms both unoptimized structures and standalone GA methods, achieving substantial improvements in fitness scores and S11 transmission coefficients. This algorithm effectively overcomes the limitations of conventional approaches in addressing complex reflected wave compensation problems in high-frequency applications, providing a robust solution for signal integrity optimization in high-speed circuit design. This study not only advances the field of intelligent circuit optimization but also establishes a valuable framework for the application of hybrid algorithms to complex engineering challenges. Full article

Drone localization is essential for various purposes such as navigation, autonomous flight, and object tracking. However, this task is challenging when satellite signals are unavailable. This paper addresses database-free vision-only localization of flying drones using optimal window template matching and velocity fusion. Assuming the ground is flat, multiple optimal windows are derived from a piecewise linear segment (regression) model of the image-to-real world conversion function. The optimal window is used as a fixed region template to estimate the instantaneous velocity of the drone. The multiple velocities obtained from multiple optimal windows are integrated by a hybrid fusion rule: a weighted average for lateral (sideways) velocities, and a winner-take-all decision for longitudinal velocities. In the experiments, a drone performed a total of six medium-range (800 m to 2 km round trip) and high-speed (up to 14 m/s) maneuvering flights in rural and urban areas. The flight maneuvers include forward-backward, zigzags, and banked turns. Performance was evaluated by root mean squared error (RMSE) and drift error of the GNSS-derived ground-truth trajectories and rigid-body rotated vision-only trajectories. Four fusion rules (simple average, weighted average, winner-take-all, hybrid fusion) were evaluated, and the hybrid fusion rule performed the best. The proposed video stream-based method has been shown to achieve flight errors ranging from a few meters to tens of meters, which corresponds to a few percent of the flight length. Full article

Insulation defects are the main cause of transformer faults, and the partial discharge phenomenon generated by defects under high-voltage excitation can reflect the internal characteristics of the defects. Therefore, studying the characteristics of partial discharge signals can provide an important basis for the analysis of transformer partial discharge problems. This article proposes a transformer partial discharge ultra-high-frequency signal classification and recognition method based on the Adaptive Hybrid Attention Fusion Network. The feature extraction of partial discharge waveform is carried out through a dual flow network structure, where the ResNet branch focuses on extracting local features and the Swin Transformer branch focuses on extracting global features. Then, a new Adaptive Hybrid Attention Fusion Network fusion model is used to weight the extracted features according to adaptive allocation weights, ultimately achieving the classification and recognition of transformer partial discharge ultra-high-frequency signals. The experiment shows that this method achieves a fault detection accuracy of 99.58%, with a loss rate of only 0.73%. Compared to various existing network models, the accuracy of the proposed model reached 99.58%, the recall was 99.58%, and the F1 score was 99.58%, which is significantly better than other model methods, indicating that the model has significant advantages in detection performance. Full article

Accurate dew intensity prediction is vital in multiple fields, such as agriculture, meteorology, industry, and transportation. This study addresses the cross-disciplinary demands for dew intensity prediction by proposing a hybrid deep learning model based on the improved hippopotamus optimization (IHO). Key influencing factors were selected through multidimensional meteorological data correlation analysis, and a fusion architecture of a Bidirectional Temporal Convolutional Network (BiTCN) and a Support Vector Machine (SVM) was constructed. The IHO algorithm is adopted to optimize model parameters and enhance prediction accuracy adaptively. Experiments were conducted using ten years of meteorological data to verify the prediction of twelve-hour dew intensity in three typical ecosystems in Northeast China: farmland, marsh wetland, and urban areas. The results show that the optimized IHO-BiTCN-SVM model achieved significant improvements in key indicators, including MAE, MAPE, MSE, RMSE, and R². For the farmland ecosystem, MAE was reduced by 72.2% (0.0016572 vs. 0.0059659), MSE decreased from 6.8552 × 10⁻⁵ to 6.7874 × 10⁻⁶, and R² increased by 12.5% (0.98791 vs. 0.87793). The IHO algorithm reduced the MAE of the farmland system by 39.6%, the MAPE by 41.6%, and the MSE by 60.2%, yet the R² increased by 1.8% compared with the benchmark model. This model effectively overcomes the subjectivity of traditional methods through an intelligent parameter optimization mechanism, providing reliable technical support for precise agricultural irrigation decisions, urban dew formation warnings, and wetland ecological protection. Full article

This study was conducted in order to enrich the safety evaluation system of vehicles on complex road sections and provide quantitative support for speed control and driving decision-making. To address the driving stability issue caused by trajectory uncertainty on curved roads, we analyzed lane-changing stability and found that trajectory variations induce a step change in centrifugal force, aggravating lateral instability. Secondly, we developed a variety of simulation schemes to determine the stability limit speed under multi-source information fusion and constructed the corresponding database. Finally, we established an interpretable driving stability evaluation method based on the Differential Evolution-Extended Belief Rule Base-Shapley Additive Explanations (DB-EBRB-SHAP) model. This model incorporates driving behavior as a qualitative variable into the hybrid framework, and its accuracy was further enhanced through parameter optimization. The results demonstrate that the model achieves high evaluation accuracy for driving stability on curved road sections (MAE = 0.0306 and RMSE = 0.0363). Interpretability analysis reveals that curve radius and lane-changing behavior are the key influencing parameters; the negative interaction effect between the two on driving stability will weaken as the curve radius increases. Full article

Hydrological drought poses a significant threat to water security and ecosystems globally. While remote sensing offers vast spatial data, advanced analytical methods are required to translate this data into actionable insights. This review addresses this need by systematically synthesizing the state-of-the-art in using convolutional neural networks (CNNs) and satellite-derived vegetation indices for hydrological drought detection. Following PRISMA guidelines, a systematic search of studies published between 1 January 2018 and August 2025 was conducted, resulting in 138 studies for inclusion. A narrative synthesis approach was adopted. Among the 138 studies included, 58% focused on hybrid CNN-LSTM models, with a marked increase in publications observed after 2020. The analysis reveals that hybrid spatiotemporal models are the most effective, demonstrating superior forecasting skill and in some cases achieving 10–20% higher accuracy than standalone CNNs. The most robust models employ multi-modal data fusion, integrating vegetation indices (VIs) with complementary data like Land Surface Temperature (LST). Future research should focus on enhancing model transferability and incorporating explainable AI (XAI) to strengthen the operational utility of drought early warning systems. Full article