Underwater image enhancement is affected by light absorption, scattering, and non-uniform illumination, typically resulting in color shifts, contrast reduction, and blurred details. Existing diffusion-based underwater image enhancement methods posses...
Reliable visual inspection of photovoltaic modules is limited by the scarcity of diverse defect images and pixel-level annotations. This study proposes a defect-specific RGB image synthesis framework combining local feature disentanglement and physic...
Optical reflective markers are widely used in precision medicine, computer-assisted surgery, and robotic interventions. Nevertheless, intraoperative tracking still faces challenges such as sensor saturation, Point Spread Function (PSF) blooming, and...
Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering...
The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed...
Accurate echo classification is crucial for Millimeter-wave Cloud Radar (MMCR) data quality control. Existing approaches, however, often struggle to generalize across complex scenes or lack physical interpretability. Here we propose PhySNet, a physic...
Accurate cloud detection remains a significant challenge due to the spectral ambiguity between clouds and bright or heterogeneous surfaces (e.g., snow, desert). While multi-angle and polarization data offer rich information, the discriminative power...
Physics-guided machine learning (PGML) methods are emerging as valuable tools for modelling the constitutive relations of solids due to their ability to integrate both data and physical knowledge. While various PGML approaches have successfully model...
Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accur...
Accurate prediction of the internal temperature of mushroom bags is essential but remains challenging owing to the complex nonlinear coupling between ambient conditions and the bag’s thermal state. This study proposes a physics-guided machine l...
Dichromatic optical interferometry provides rich optical information for lubricant film-thickness measurement. However, experimental data are typically limited to a small number of discrete operating conditions, making it difficult to learn the nonli...
Substantial progress has been made in bearing-fault classification under same-condition settings, yet cross-condition diagnosis remains affected by three coupled issues: speed-induced temporal-scale perturbation, insufficient use of structured label...
Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the i...
Extended-range three-dimensional (3D) single-molecule localization microscopy (SMLM) and single-particle tracking (SPT) require precise emitter localization across cellular-scale axial distances. However, long-rangeengineered point-spread functions (...
Wearable neurotechnology depends critically on continuous movement monitoring to characterize motor impairment and recovery in real-world settings. While joint torque serves as a clinically essential kinetic marker, estimating it directly on-device f...
Atmospheric dust reduces the contrast and visibility of Mars rover images, while strictly aligned clean–dusty image pairs remain scarce. To address this problem, we propose a two-stage physics-guided diffusion framework. In the first stage, we...
The helicopter tail drive shaft is vulnerable to failure from projectile impacts during low-altitude flight. Stress wave-based inversion of impact parameters and quantitative damage assessment remain insufficiently explored. To address small-sample a...
Steel plate defect detection is confronted with problems such as weak features of small defects, disconnection between physical priors and detection tasks, and semantic inconsistency of multi-scale fusion, which can easily lead to the misdetection of...
Data loss caused by sensor malfunctions in bridge Structural Health Monitoring (SHM) systems poses a critical risk to structural safety assessment. Although deep learning has advanced data imputation, standard “black-box” models often fai...
Accurate prediction of slope displacement from high-frequency GNSS monitoring data is critical for early warning of landslides and tailings dam failures. However, existing deep learning approaches often neglect the spatial coordination imposed by geo...
23 September 2025
Underwater crack detection is critical for ensuring the safety and longevity of submerged infrastructures, yet it remains challenging due to water-induced image degradation, limited labeled data, and the poor generalization of existing models. This p...
Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-...
For unsupervised anomaly detection in aircraft engines, traditional methods predominantly focus on mining temporal features, often neglecting complex interdependencies among multiple variables. Although existing graph neural networks can model spatia...
Two-dimensional (2D) phase unwrapping (PU) in interferometric synthetic aperture radar (InSAR) remains a challenging ill-posed inverse problem, particularly in low-coherence regions where the Itoh condition is violated. Although deep-learning methods...
Coding metasurfaces provide an effective approach for manipulating electromagnetic scattering through discrete coding states. However, obtaining angular-domain scattering responses for a large number of coding configurations remains computationally e...
The brain is a biological system comprising nerve cells and orchestrates its embodied agent’s perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent,...
Neutron/gamma Pulse Shape Discrimination (PSD) is a critical task in radiation detection systems, where reliable classification becomes increasingly challenging under low Signal-to-Noise Ratio (SNR) conditions. Although Deep Convolutional Neural Netw...
Shape-from-Focus (SFF) is attractive for microscopic three-dimensional measurement, but high dynamic range (HDR) surfaces and weak-textured surfaces distort the focus curve through saturation, spurious peaks, and low signal-to-noise ratios. These dis...
In response to stochastic fluctuations in large-scale wind power integration and the resulting peak-shaving challenges, high-precision forecasting with physical consistency is essential for grid safety. To address the inefficiency of physical models...
Due to hardware limitations of Thermal InfraRed (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolutions (LRs) and blurred textures. Improving the spatial resolution of TIR images is of great significance for...
Radiation fields around collimated or shielded sources exhibit strong gradients whose accurate delineation is critical for worker protection and emergency response. Mobile robots can survey such fields, but they sample sparsely and irregularly along...
This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses th...
Accelerometers built on micro-electromechanical systems (MEMS) play a critical role in structural monitoring and machinery diagnostics; however, their accuracy suffers from intrinsic nonlinearities—dead zones, hysteresis, saturation, and colore...
Accurate photovoltaic (PV) energy forecasting is vital for grid stability and the global low-carbon transition. However, existing data-driven and channel-independent PV energy forecasting models struggle to capture nonlinear meteorological couplings,...
Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-...
Rapid and accurate recognition of radar active jamming is a prerequisite for cognitive electronic countermeasures. However, under complex electromagnetic environments with scarce training samples, existing deep learning models are prone to capturing...
Multi-agent path planning remains a challenging problem in deep reinforcement learning due to inefficient coordination, excessive exploration, and unstable training dynamics. Although Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been w...
Reliable underwater visual perception plays a critical role in marine exploration, autonomous underwater vehicles, and subsea inspection. Nevertheless, underwater images are frequently degraded by severe color distortion, contrast reduction, and stru...
Cross-speed bearing fault diagnosis faces a physical obstacle: bearing characteristic frequencies are proportional to shaft speed, so identical faults shift in the spectrum when source and target speeds differ, and source-trained feature mappings deg...
Accurate dynamic state reconstruction in complex multi-node coupled systems is critical for ensuring operational stability and reliability. However, this task is highly challenging due to spatially sparse measurement sensors, strong dynamic coupling...
Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent d...
Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect im...
Accurate prediction of quality indicators during juice pasteurization is challenged by the intricate coupling between temperature-dependent reaction kinetics and the limited availability of labeled data under laboratory-controlled pasteurization cond...
Foreign object detection during unmanned aerial vehicle (UAV) grid inspection suffers from severe visual degradation under adverse weather conditions, such as haze and heavy rain. Existing approaches often struggle to distinguish target textures from...
Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics...
During high-altitude simulation tests, the accurate reproduction of environmental conditions directly affects the ability to reliably evaluate the engine’s performance under simulated high-altitude conditions. Traditional physical models, though inte...
Measured radar point-target detection under ground clutter is difficult because weak target echoes often compete with clutter peaks, and image-space detections do not directly represent physical range-Doppler coordinates. This paper proposes a wavefo...
Railway bearings experience speed- and load-dependent distribution shifts that complicate fault diagnosis. This study proposes a Physics-Guided Multi-View Transformer with Block Attention Residuals (PGMV-BAR). Characteristic-order prior tokens sequen...
Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum infor...
The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of da...
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