Search Results (21)

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. Full article

Fungal decay fundamentally alters moisture transport in wood through complex bio-physical coupling mechanisms that remain poorly understood. Brown-rot fungi such as Coniophora puteana (Schumach.: Fr.) P. Karst. degrade wood through chelator-mediated Fenton (CMF) chemistry, producing hydroxyl radicals that depolymerise cellulose and hemicellulose before significant mass loss. This diffusion-dependent process requires elevated moisture content and leads to structural degradation. However, existing models fail to capture the interaction between boundary-driven fungal colonization, decay-induced property changes, and multi-phase multi-Fickian moisture redistribution, particularly the separate evolution of bound- and free-water phases during decay. Here, we present a transport-response bio-hygrothermal finite element model that couples boundary-driven Monod-type fungal colonization kinetics with multi-phase moisture transport (free water, bound water, vapor) in decaying wood. Although fungal biomass evolution is simulated via a reaction–diffusion equation, decay progression is not derived from biomass–substrate interaction but prescribed independently as an experimentally informed input. The model incorporates decay-modified sorption isotherms, permeability evolution, and boundary-driven biomass influx, along with associated moisture transport, into the governing equations. The model is validated against low-field nuclear magnetic resonance (LF-NMR) measurements of C. puteana decay in Scots pine over 35 days. The model successfully reproduces the experimentally observed moisture evolution: a peak free-water content of 50%–70% during weeks 1–2, followed by a progressive decline, while bound water remains remarkably constant despite advancing decay. Monte Carlo uncertainty quantification demonstrates hierarchical parameter control: bound water is governed solely by thermodynamic factors, while free water responds to interacting biological and physical processes. Time-resolved correlation analysis shows a fundamental transition from colonization-dominated (weeks 1–2) to transport-dominated (weeks 3–5) moisture control, quantitatively explaining the experimentally observed shift from accumulation to depletion. This transport-response framework for analyzing moisture behavior under externally defined decay progression establishes quantitative parameter hierarchies that may inform the development of future substrate-coupled bio-hygrothermal models. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

This research presents a comprehensive multi-domain environmental assessment of Delos Island, a UNESCO World Heritage Site, through integration of long-term atmospheric and satellite remote sensing datasets. A significant methodological contribution of this research is the development of a cross-mission harmonization approach that enables the reconstruction of a continuous, multi-decadal atmospheric record. By implementing a hierarchical calibration pipeline to harmonise Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) observations, the study effectively eliminated a 6.61-fold systematic instrument offset, producing a 21-year time series (2004–2025) of tropospheric NO₂ concentrations. Simultaneously, a 24-year analysis (2000–2024) of coastline dynamics was conducted using the Landsat archive to quantify land area changes across the island and within a 1.03 km² Archaeological Area of Interest (AOI). Results indicate that atmospheric NO₂ concentrations stabilised following a 2015 peak, while coastal erosion represents a measurable risk to structural integrity. Net land loss of 18,400 m² was documented within the AOI, driven by localised geomorphological factors and exposure to Meltemi winds. The results indicate that these environmental processes are physically independent yet collectively require a multilayered conservation strategy to protect vulnerable archaeological heritage from atmospheric pollution and coastal retreat. Furthermore, the research highlights the value of long-term satellite datasets spanning more than two decades for supporting heritage monitoring and management, especially in remote or hard-to-reach locations. Through the analysis of the spatial and temporal characteristics of these sensors, the research enables the identification of hazard proxies that can inform risk-aware decision-making. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

The V/G criterion is a critical indicator for monitoring dynamic changes during Czochralski silicon single crystal (Cz-SSC) growth. However, the inability to measure it in real time forces reliance on offline feedback for process regulation, leading to imprecise control and compromised crystal quality. To overcome this limitation, this paper proposes a novel soft sensor modeling framework that integrates both mechanism-based knowledge and data-driven learning for the real-time prediction of the crystal quality parameter, specifically the V/G value (the ratio of growth rate to axial temperature gradient). The proposed approach constructs a hybrid prediction model by combining a data-driven sub-model with a physics-informed mechanism sub-model. The data-driven component is developed using an attention-based dynamic stacked enhanced autoencoder (AD-SEAE) network, where the SEAE structure introduces layer-wise reconstruction operations to mitigate information loss during hierarchical feature extraction. Furthermore, an attention mechanism is incorporated to dynamically weigh historical and current samples, thereby enhancing the temporal representation of process dynamics. In addition, a robust ensemble approach is achieved by fusing the outputs of two subsidiary models using an adaptive weighting strategy based on prediction accuracy, thereby enabling more reliable V/G predictions under varying operational conditions. Experimental validation using actual industrial Cz-SSC production data demonstrates that the proposed method achieves high-prediction accuracy and effectively supports real-time process optimization and quality monitoring. Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

The accuracy of numerical model outputs strongly depends on the quality of the initial wind field, yet ground observation data are typically sparse and provide incomplete spatial coverage. More importantly, many current mainstream correction models rely on reanalysis grid datasets like ERA5 as the true value, which relies on interpolation calculation, which directly affects the accuracy of the correction results. To address these issues, we propose a new deep learning model, PPWNet. The model directly uses sparse and discretely distributed observation data as the true value, which integrates observation point positions and a physical consistency term to achieve a high-precision corrected wind field. The model design is inspired by biological intelligence. First, observation point positions are encoded as input and observation values are included in the loss function. Second, a parallel dual-branch DenseInception network is employed to extract multi-scale grid features, simulating the hierarchical processing of the biological visual system. Meanwhile, PPWNet references the PointNet architecture and introduces an attention mechanism to efficiently extract features from sparse and irregular observation positions. This mechanism reflects the selective focus of cognitive functions. Furthermore, this paper incorporates physical knowledge into the model optimization process by adding a learned physical consistency term to the loss function, ensuring that the corrected results not only approximate the observations but also adhere to physical laws. Finally, hyperparameters are automatically tuned using the Bayesian TPE algorithm. Experiments demonstrate that PPWNet outperforms both traditional and existing deep learning methods. It reduces the MAE by 38.65% and the RMSE by 28.93%. The corrected wind field shows better agreement with observations in both wind speed and direction, confirming the effectiveness of incorporating position information and a physics-informed approach into deep learning-based wind field correction. Full article

The Hubble tension, a persistent discrepancy between early and late Universe measurements of $H_0$, poses a significant challenge to the standard cosmological model. In this work, we present a new Bayesian hierarchical framework designed to meticulously decompose this observed tension into its constituent parts: standard measurement errors, information loss arising from parameter-space projection, and genuine physical tension. Our approach, employing Fisher matrix analysis with MCMC-estimated loss coefficients and explicitly modeling information loss via variance inflation factors ($\lambda$), is particularly important in high-precision analysis where even seemingly small information losses can impact conclusions. We find that the real tension component ($T_{real}$) has a mean value of 5.94 km/s/Mpc (95% CI: [3.32, 8.64] km/s/Mpc). Quantitatively, approximately 78% of the observed tension variance is attributed to real tension, 13% to measurement error, and 9% to information loss. Despite this, our decomposition indicates that the observed ∼$6.39\sigma$ discrepancy is predominantly a real physical phenomenon, with real tension contributing ∼$5.64\sigma$. Our findings strongly suggest that the Hubble tension is robust and probably points toward new physics beyond the ΛCDM model. Full article

The gearbox, as an important transmission component in wind turbines, connects the blades to the generator and is responsible for converting wind energy into mechanical energy and transmitting it to the generator. Its ability to reduce vibrations directly affects the operational lifespan of the wind turbine. When designing the gearbox’s elastic support structure, it is essential to evaluate how the design parameters influence various performance metrics. Neural networks offer a powerful means of capturing and interpreting the intricate associations linking structural parameters with performance metrics. However, conventional neural networks are usually optimized for a single task, failing to fully account for task differences and shared information. This can lead to task conflicts or insufficient feature modeling, which in turn affects the learning efficiency of inter-task correlations. Furthermore, physical experiments are costly and provide limited training, making it difficult to meet the large-scale dataset requirements for neural network training. To address the high cost and limited scalability of traditional physical testing for gearbox rubber damping structures, in this study, we propose a low-cost performance prediction method that replaces expensive experiments with simulation-driven dataset generation. An optimal Latin hypercube sampling technique is employed to generate high-quality data at minimal cost. On this basis, a multi-task prediction model called multi-gate mixture-of-experts with LSTM (PLE-LSTM) is constructed. The adaptive gating mechanism, hierarchical nonlinear transformation, and effective capture of temporal dynamics in the LSTM significantly enhance the model’s ability to model complex nonlinear patterns. During training, a dynamic weighting strategy named GradNorm is utilized to counteract issues like the early stabilization in multi-task loss convergence and the uneven minimization of loss values. Finally, ablation experiments conducted on different datasets validate the effectiveness of this approach, with experimental results demonstrating its success. Full article

Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R² values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production. Full article

Image signal processing (ISP), a critical component in camera imaging, has traditionally relied on experience-driven parameter tuning. This approach suffers from inefficiency, fidelity issues, and conflicts with visual enhancement objectives. This paper introduces ReEn-GAN, an innovative staged ISP proxy tuning framework. ReEn-GAN decouples the ISP process into two distinct stages: reconstruction (physical signal recovery) and enhancement (visual quality and color optimization). By employing distinct network architectures and loss functions tailored to specific objectives, the two-stage proxy can effectively optimize both the reconstruction and enhancement modules within the ISP pipeline. Compared to tuning with an end-to-end proxy network, the proposed method’s proxy more effectively extracts hierarchical information from the ISP pipeline, thereby mitigating the significant changes in image color and texture that often result from parameter adjustments in an end-to-end proxy model. This paper conducts experiments on image denoising and object detection tuning tasks, and compares the performance of the two types of proxies. The results demonstrate that the proposed method outperforms end-to-end proxy methods on public datasets (SIDD, KITTI) and achieves over 21% improvement in performance metrics compared to hand-tuning methods. Full article

This paper proposes a physics-constrained 3D Swin Transformer (ST) for gravity inversion. By leveraging the self-attention mechanism in 3D ST, the method effectively models global dependencies within gravity data, enabling the network to reweight features globally and focus on critical anomalous regions. Additionally, prior gradient information is integrated into the loss function, and a hierarchical weight allocation strategy is adopted to guide the model in learning boundary information of density structures and deep-seated features more effectively. Synthetic experiments demonstrate that the proposed method achieves lower model errors, better boundary alignment, and higher inversion accuracy. The approach is further validated using gravity anomaly observations from the Gonghe Basin in Qinghai, yielding reliable and precise inversion results. Full article