Search Results (159)

The problem of performance degradation in current sound source localization algorithms under reverberant and noisy environments remains a critical challenge. Consequently, this paper introduces a novel approach to estimate the 3-D position of sound sources directly from raw audio signals using an artificial neural network (ANN), which improves the performance of sound source localization algorithms under reverberant and noisy environments. Instead of relying on handcrafted features, raw audio signals recorded by a tetrahedral stereo microphone array are fed directly into the ANN. This design eliminates spatial symmetry issues found in 2-D microphone arrays and enhances 3-D localization accuracy. Inspired by human auditory systems, a convolutional layer is added after the input layer to simulate frequency analysis to search localization cues in different frequency bands. Furthermore, the proposed algorithm incorporates residual connections (RC) and squeeze-and-excitation (SE: an attention mechanisms). Residual connections introduce raw features into deeper network layers to prevent localized information loss caused by excessive network depth, while also enabling improved model training stability. The attention mechanism dynamically adjusts weights across and within channels, suppressing interference while enhancing localization-critical cues, thereby playing a pivotal role in boosting the algorithm’s reverberation and noise resistance. Experimental results demonstrate significant improvements: in semi-anechoic chambers, the method reduces localization errors by 0.2 m and increases accuracy by 10%; in conference rooms, errors decrease by 0.26 m with a 21% accuracy gain. These outcomes conclusively validate the effectiveness of the proposed approach in enhancing robustness against reverberation and noise in sound source localization systems. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

Weight reduction and dynamic performance optimization are critical for airborne direct-drive VICTS satellite communication antennas, which require lightweight, high-speed, and high-precision rotation. Traditional vibration suppression methods, such as uniform support layout and added damping, rely heavily on empirical trial and error, lack targeted modal control, and cannot balance lightweight design with dynamic stiffness. To address these issues, this paper proposes a wave-theory-based dynamic modeling and rapid optimization method for multi-layer rotating components in direct-drive VICTS antennas. The kinematic model of the rotating ring and ball revolution excitation are derived using the annular wave equation and bearing kinematics. A Modal Blocking Mechanism is established: placing support balls at positions satisfying the half-wavelength constraint suppresses target mode shapes via wave interference, achieving vibration attenuation at the source. A homogenization equivalent method based on RVE is developed for irregular cross-section rings, yielding analytical expressions for in-plane equivalent elastic modulus and out-of-plane equivalent shear modulus. These parameters are integrated into the wave equation to analytically solve vibration modes, avoiding iterative finite element computations. A rapid multi-objective optimization framework is then constructed, minimizing the structural weight and maximizing the modal separation interval under dynamic stiffness and excitation frequency constraints. Numerical simulations, FE analysis, and prototype tests validate the method: the maximum analytical error is only 3.1%. Compared with uniform support designs, the optimized structure achieves a 40% weight reduction, a 40% increase in minimum modal separation, and a 65% reduction in the RMS tracking error. This work provides an efficient, deterministic dynamic design method for large-diameter ring structures, transforming vibration control from empirical adjustment into a precise, physics-informed optimization. Full article

Battery datasets, whether gathered experimentally or through simulation, are typically high-dimensional and complex, which complicates the direct interpretation of degradation behavior or anomaly detection. To overcome these limitations, this study introduces a framework that compresses battery signals into a low-dimensional representation using an autoencoder, enabling the extraction of informative features for state analysis. A central component of this work is the systematic comparison of latent representations obtained from two fundamentally different data sources: frequency-domain impedance data and time-domain voltage-current data. The close agreement of aging trajectories in both representations suggests that information traditionally derived from impedance analysis can also be captured directly from raw time-series signals. To better approximate real operating conditions, synthetic datasets are augmented with stochastic perturbations. In this context, latent spaces learned from idealized periodic inputs are contrasted with those derived from permuted and noise-contaminated signals. The resulting low-dimensional features are subsequently evaluated through a support vector machine with both linear and nonlinear kernel functions, allowing the categorization of battery states into fresh, aged and damaged conditions. The results demonstrate that the progression of battery degradation is consistently reflected in the latent space, independent of the input domain or signal quality. This robustness indicates that the proposed approach can effectively capture essential aging characteristics even under non-ideal conditions. Consequently, this framework provides a basis for developing advanced diagnostic strategies, including the design of pseudo-random excitation profiles for improved battery state assessment and optimized operational control. Full article

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

The deep integration of machine learning technology with geological prospecting has brought to the forefront a key challenge: how to construct geological-mineralization models by fusing multi-source data, select model features with guidance from metallogenic factors, build multi-source metallogenic prediction models with geological constraints, and ultimately achieve a thorough integration of domain knowledge and machine intelligence. The Eastern-Central Tianshan region is one of China’s most important copper–nickel mineral resource bases, predominantly hosting magmatic copper–nickel sulfide deposits with significant resource potential. In this context, this paper proposes a metallogenic prediction model based on multi-modal feature fusion technology. The model employs a Residual Neural Network (ResNet) incorporating a Squeeze-and-Excitation (SE) attention mechanism and a Multi-Layer Perceptron (MLP) to extract features from different modalities. It integrates multi-source data, including geochemical information, geological metallogenic factors, and aeromagnetic data. A cross-modal feature interaction module, constructed using attention weighting and a gating mechanism, enables deep fusion of the features. After training, the model achieved a prediction accuracy of 97% on the test set. Compared to a unimodal model constructed using Random Forest, the confidence and discriminative capability of the training results were significantly enhanced, validating the effectiveness of multi-modal feature fusion. Applying the trained model to the study area, a total of 11 prospective metallogenic zones were delineated. These include 4 zones in the peripheries of known deposits and 7 zones in previously unexplored (blank) areas. Notably, some known mineral occurrences fall within the predicted blank-area targets, validating the feasibility and significant value of multi-modal feature fusion in mineral prediction. This work provides a novel methodology for the subsequent integrated processing of multi-source data. Full article

To address the problem of fault diagnosis for rotating machinery under complex operating conditions in real industrial systems, most existing domain generalization methods fail to sufficiently consider inter-class feature structures when learning domain-invariant representations. This limitation often leads to degraded diagnostic performance in cross-domain scenarios, particularly under class imbalance or significant operating condition variations. Moreover, existing feature extraction networks specifically designed for rotating machinery are often inadequate for fault diagnosis tasks under variable operating conditions. To overcome these challenges, this paper proposes a domain-invariant fault feature representation learning framework for multi-source domain generalization. Specifically, we design a mechanism-aware multi-branch feature extraction network inspired by excitation–modulation mechanisms of fault generation, which captures fault-sensitive characteristics from both time-domain and frequency-domain perspectives. In addition, a class-conditional feature alignment strategy based on ICM (Independent Causal Mechanism) mixing is introduced to enhance cross-domain consistency. Through feature structure regularization, discriminative information across categories is effectively preserved under domain shifts. Extensive experimental results demonstrate that the proposed method significantly improves diagnostic performance and generalization ability on the CWRU bearing dataset as well as the HUST bearing and gearbox datasets. Notably, when the number of source domains increases, the proposed framework exhibits superior training efficiency. Full article

Background/Objectives: Traumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide. Electrolyte disturbances are common in this patient cohort, with serum chloride frequently elevated. Chloride dysregulation may be associated with poor neurological outcomes through mechanisms including paradoxical gamma amino butyric acid receptor excitation, cytotoxic edema, and ferroptosis. The aim of this review was to evaluate the relationship between serum chloride levels and outcomes in patients with TBI. Methods: A literature review was performed to identify all potential studies that reported on serum chloride levels and TBI. All study types and patient groups were included. Studies were included if they reported on serum chloride measurements as well as outcomes such as mortality, surgical intervention, intracranial pressure, and neurological/functional outcome scores in patients with TBI. References and citations were also reviewed. Results: A small number of mostly retrospective studies with modest patient numbers demonstrate an association between high chloride levels and increased mortality in patients with TBI, with this relationship persisting independent of hypernatremia. Recent large, randomized trials showed that balanced crystalloid solutions, despite lower chloride content, may be associated with worse outcomes in TBI patients compared to saline. No studies directly correlated chloride levels with intracranial pressure measurements. Chloride level rather than total chloride load appears more strongly associated with adverse outcomes, with non-hypertonic saline sources contributing substantially to chloride burden. Mechanistic evidence links chloride channel dysregulation to ferroptosis and cytotoxic edema, with sex-specific patterns of transporter expression. Conclusions: Limited available evidence suggests that hyperchloremia is independently associated with increased mortality in TBI though causality remains unestablished. The findings regarding balanced solutions challenge conventional fluid management assumptions and highlight the complexity of chloride’s role in TBI pathophysiology. The absence of studies directly correlating chloride with intracranial pressure represents a critical evidence gap. Future studies with larger patient numbers, prospective designs, and multimodal neuromonitoring should further define these relationships to inform evidence-based chloride management strategies. Full article

Background: Deep vein thrombosis (DVT) is one of the most common cardiovascular diseases and is especially prevalent in areas with environmental pollution. Bioaccumulation of toxic heavy metals may lead to deterioration of homeostasis with cellular change, endothelial dysfunction, DNA impairment and cellular signaling. The reason for this is usually the accumulation of thrombogenic toxins in the body as a result of long-term exposure or a lack of regulatory gene expression. In this study, we aimed to measure the minerals that potentially accumulate in the nail. The measurement method was laser-induced breakdown spectroscopy (LIBS), which is a form of atomic emission spectroscopy. It uses a highly energetic laser source to form a plasma of excited atoms emitting light of characteristic wavelengths. It provides accurate quantification and reveals the relationship between tissue accumulation of toxic heavy metals and DVT formation. Methods: Between January 2020 and December 2021, 100 patients diagnosed with lower-extremity deep vein thrombosis were screened in a single tertiary healthcare center. Among them, 50 patients who met the eligibility criteria and consented to participate were included in the study. An additional 50 age-matched healthy volunteers were enrolled as controls. Demographic and clinical characteristics were recorded. Nail samples were obtained from each participant, and elemental emission intensities were quantitatively analyzed using laser-induced breakdown spectroscopy (LIBS). Results: No difference in clinical characteristics was detected between the groups. While iron, calcium and silicon were found to be high in DVT patients, magnesium was found to be low. Regarding the magnesium emission, ROC analysis showed 76–90% specificity and 69–82% sensitivity, respectively. Conclusions: LIBS is a useful method because it is easy to use and can be used with a small sample. According to the results of our study, information about the pathogenesis of DVT was obtained through nail analysis. Therefore, we believe that LIBS analysis is a method that may be useful in determining the causes and predisposing factors for DVT. Full article

In complex maritime environments and scenarios with severe signal interference, unmanned surface vehicle (USV) swarms face dual challenges: unreliable GNSS signals due to interference and difficulties in accurately calibrating multi-sensor installation errors. These issues severely constrain the capability for high-precision cooperative formation operations. To address these problems, this paper proposes a cooperative localization and all-source online calibration algorithm based on a unified factor graph optimization framework. First, a tightly coupled all-source graph framework is established, integrating navigation radar, electro-optical systems (EOSs) with laser rangefinders, IMU, and GNSS into a sliding window. By leveraging high-precision mutual observations among the swarm, strong geometric constraints are constructed to mitigate the drift of individual inertial navigation systems. Second, an adaptive GNSS weighting mechanism based on signal quality and a degradation detection strategy based on eigenvalue analysis of the Fisher Information Matrix (FIM) are designed. These mechanisms enable online identification and robust estimation of extrinsic parameters, effectively resolving calibration divergence under weak excitation conditions such as straight-line sailing. Finally, the proposed algorithm is validated using field data from three USVs combined with simulated interference experiments. Results demonstrate that the algorithm can rapidly converge to high-precision calibration parameters without artificial targets (radar translation error < 0.2 m, EOS rotation error < 0.05°). During periods of simulated GNSS interference, the cooperative localization root mean square error (RMSE) is reduced to 2.85 m, representing an accuracy improvement of approximately 84.5% compared to traditional methods. This study achieves a “more accurate as it runs” cooperative navigation effect, providing reliable technical support for USV swarm applications in GNSS-denied environments. Full article

We present a theoretical and numerical investigation of a far-field super-resolution dark-field microscopy technique based on longitudinal nano-optical field excitation and detection. This method is implemented by integrating vector optical field modulation into a back-scattering confocal laser scanning microscope. A complete forward theoretical imaging framework that rigorously accounts for light–matter interactions is adopted and validated. The weak interaction model and general model are both considered. For the weak interaction model, e.g., multiple discrete dipole sources with a uniform or modulated responding intensity are utilized to fundamentally demonstrate the relationship between the sample and the imaging information. For continuous nanostructures, the finite-difference time-domain simulation results of the interaction-induced optical fields in the imaging model show that the captured image information is not determined solely by system resolution and sample geometry, but also arises from a combination of sample-dependent factors, including material composition, the local density of optical states, and intrinsic physical properties such as the complex refractive index. Unlike existing studies, which predominantly focus on system design or rely on simplified assumptions of weak interactions, this paper achieves quantitative characterization and precise regulation of nanoscale vector optical fields and samples under strong interactions through a comprehensive analytical–numerical imaging model based on rigorous vector diffraction theory and strong near-field coupling interactions, thereby overcoming the limitations of traditional methods. Full article

Despite numerous conventional direction-of-arrival (DOA) methods, relationships between number of sources (NOS) and DOA are often ignored, which could yield meaningful estimation information. Therefore, a dual-branch Convolutional Neutral Network (CNN) integrated with squeeze-and-excitation (SE) blocks that can perform DOA and NOS estimation simultaneously is proposed to address such limitations. Extensive simulations demonstrate the superiority of the proposed model over several traditional algorithms, especially under low signal-to-noise (SNR) conditions, limited snapshots, and in closely spaced incident angle scenarios. Full article

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety judgment. However, existing behavior recognition methods have difficulty accurately identifying operational states in complex scenarios involving continuous actions, partial occlusions, and fine-grained manipulations. To address these challenges, this paper proposes a safety behavior recognition method for substation operations based on a dual-path spatiotemporal network. Personnel localization is achieved using YOLOv8, while behavior classification is performed through the SlowFast framework. In the Slow pathway, an ECA attention mechanism is integrated with residual structures to enhance the representation of sustained operational postures. In the Fast pathway, a multi-path excitation residual network is introduced to fuse temporal, channel, and motion information, improving the multi-scale representation of local action variations. Furthermore, to mitigate the issue of class imbalance in substation operation data, Focal Loss based on binary cross-entropy is incorporated to adaptively down-weight easily classified samples. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 87.77% and an F1-score of 85.56% across multiple operation scenarios. The results further indicate improved recognition stability and adaptability, supporting safe substation operation and maintenance in renewable energy-integrated power systems. Full article

Electromagnetic wave propagation in complex environments demands accurate yet efficient modeling techniques. This study introduces a physics-informed neural network (PINN) framework for two-dimensional transient electromagnetic analysis, where Helmholtz equations are directly incorporated into the loss function. The model learns spatiotemporal field evolution without relying on spatial discretization or labeled data. Various excitation and material conditions are examined, including single and dual Gaussian sources in both free space and inhomogeneous regions with dielectric and conducting inclusions. Through this formulation, the network captures key wave phenomena such as propagation, reflection, and scattering with high precision. Validations against finite-difference time-domain (FDTD) simulations confirm strong agreement in both temporal and spatial field distributions. The results demonstrate that the proposed PINN provides an effective, mesh-free alternative for modeling electromagnetic wave dynamics, offering scalability for complex and data-sparse scenarios. Full article

Special steel manufacturing involves both discrete processing events and continuous physical flows, forming a representative discrete–continuous hybrid production system. However, due to the visually homogeneous surfaces of steel products, the highly dynamic production environment, and frequent disturbances or anomalies, traditional single-source tracking approaches struggle to maintain accurate and consistent material identification. To address these challenges, this paper proposes a multi-source fusion-based material tracking method tailored for discrete–continuous hybrid scenarios. First, a state–event system (SES) is constructed based on process rules, enabling interpretable reasoning of material states through event streams and logical constraints. Second, on the visual perception side, a YOLOv8-SE detection network embedded with the squeeze-and-excitation (SE) channel attention mechanism is designed, while the DeepSORT tracking framework is improved to enhance weak feature extraction and dynamic matching for visually similar targets. Finally, to handle information conflicts and cooperation in multi-source fusion, an improved Dempster–Shafer (D-S) evidence fusion strategy is developed, integrating customized anomaly handling and fault-tolerance mechanisms to boost decision reliability in conflict-prone regions. Experiments conducted on real special steel production lines demonstrate that the proposed method significantly improves detection accuracy, ID consistency, and trajectory integrity under complex operating conditions, while enhancing robustness against modal conflicts and abnormal scenarios. This work provides an interpretable and engineering-feasible solution for end-to-end material tracking in hybrid manufacturing systems, offering theoretical and methodological insights for the practical deployment of multi-source collaborative perception in industrial environments. Full article