Search Results (3,311)

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables. Full article

The stable operation of electrical equipment is critical for industrial safety, yet traditional anomaly detection methods often suffer from limitations, such as high resource demands, dependency on expert knowledge, and lack of real-world capabilities. To address these challenges, this article proposes an unsupervised anomaly detection method for electrical equipment, utilizing audio latent representation and a parallel attention mechanism. The framework employs an autoencoder to extract low-dimensional features from audio signals and introduces a phase-aware parallel attention block to dynamically weight these features for an improved anomaly sensitivity. With adversarial training and a dual-encoding mechanism, the proposed method demonstrates robust performance in complex scenarios. Using public datasets (MIMII and ToyADMOS) and our collected real-world wind turbine data, it achieves high AUC scores, surpassing the best baselines, which demonstrates our framework design is suitable for industrial applications. Full article

Laser beam powder bed fusion of metals (PBF-LB/M, or simply L-PBF) has emerged as one of the most competitive additive manufacturing technologies for producing complex metallic components with high precision, design freedom, and minimal material waste. Among the various categories of additive manufacturing processes, L-PBF stands out, paving the way for the execution of part designs with geometries previously considered unfeasible. Despite offering several advantages, parts with overhang features require the use of support structures to provide dimensional stability of the part. Support structures achieve this by resisting residual stresses generated during processing and assisting heat dissipation. Although the scientific community acknowledges the role of support structures in the success of L-PBF manufacturing, they have remained relatively underexplored in the literature. In this context, the present work investigated the impact of laser power and scanning speed on the dimensioning, integrity and tensile strength of single-walled block type support structures manufactured in AISI 316L stainless steel. The method proposed in this work is divided in two stages: processing parameter exploration, and mechanical characterization. The results indicated that support structures become more robust and resistant as laser power increases, and the opposite effect is observed with an increment in scanning speed. In addition, defects were detected at the interfaces between the bulk and support regions, which were crucial for the failure of the tensile test specimens. For a layer thickness corresponding to 0.060 mm, it was verified that the combination of laser power and scanning speed of 150 W and 500 mm/s resulted in the highest tensile resistance while respecting the dimensional deviation requirement. Full article

Civil engineering structures with damage, defects, or subsurface utilities create a high-contrast exploration environment. These anomalies of interest exhibit different electromagnetic properties from the surrounding medium, and ground-penetrating radar (GPR) has the potential to accurately locate and map their three-dimensional (3D) distributions. However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. It balances the gradient updates of permittivity and conductivity models through performing total variation (TV) regularization and minimum support gradient (MSG) regularization on different parameters in the inversion process. Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. The TV+MSG composite model constraint strategy improves the accuracy and stability of dual-parameter inversion, providing a robust solution for the 3D imaging of subsurface anomalies with high-contrast features. These outcomes offer researchers theoretical insights and a valuable reference when investigating scenarios with high-contrast environments. Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of $0.82\pm 0.13$ was obtained with Method D versus $0.63\pm 0.09$ with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable the rapid measurement of large object surfaces. To enhance overall measurement accuracy, we propose an enhanced calibration method utilizing a multidimensional ball-based calibrator to simultaneously calibrate for hand-eye transformation and robot kinematic parameters. Firstly, a preliminary hand-eye calibration method is introduced to compensate for measurement errors at observation points, leveraging geometric-constraint-based optimization and a virtual single point derived via the barycentric calculation method. Subsequently, a distance-constrained calibration method is proposed to jointly estimate the hand-eye transformation and robot kinematic parameters, wherein a distance error model is constructed to link parameter errors with the measured deviations of a virtual single point. Finally, calibration and validation experiments were carried out, and the results indicate that the maximum and average measurement errors were reduced from 1.053 mm and 0.814 mm to 0.421 mm and 0.373 mm, respectively, thereby confirming the effectiveness of the proposed method. Full article

Due to the limited quantity and high cost of high-quality three-dimensional annotations, generalized zero-shot point cloud segmentation aims to transfer the knowledge of seen to unseen classes by leveraging semantic correlations to achieve generalization purposes. Existing generative point cloud semantic segmentation approaches rely on generators trained on seen classes to synthesize visual features for unseen classes in order to help the segmentation model gain the ability of generalization, but this often leads to a bias toward seen classes. To address this issue, we propose a semantic-guided adaptive bias calibration approach with a dual-branch network architecture. This network consists of a novel visual–semantic fusion branch alongside the primary segmentation branch to suppress the bias toward seen classes. Specifically, the visual–semantic branch exploits the visual–semantic relevance of the synthetic features of unseen classes to provide auxiliary predictions. Furthermore, we introduce an adaptive bias calibration module that dynamically integrates the predictions from both the main and auxiliary branches to achieve unbiased segmentation results. Extensive experiments conducted on standard benchmarks demonstrate that our approach significantly outperforms state-of-the-art methods on both seen and unseen classes, thereby validating the effectiveness of our approach. Full article

Precisely recognizing diseases in tomato leaves plays a crucial role in enhancing the health, productivity, and quality of tomato crops. However, disease identification methods that rely on single-mode information often face the problems of insufficient accuracy and weak generalization ability. Therefore, this paper proposes a tomato leaf disease recognition framework FCMNet based on multimodal fusion, which combines tomato leaf disease image and text description to enhance the ability to capture disease characteristics. In this paper, the Fourier-guided Attention Mechanism (FGAM) is designed, which systematically embeds the Fourier frequency-domain information into the spatial-channel attention structure for the first time, enhances the stability and noise resistance of feature expression through spectral transform, and realizes more accurate lesion location by means of multi-scale fusion of local and global features. In order to realize the deep semantic interaction between image and text modality, a Cross Vision–Language Alignment module (CVLA) is further proposed. This module generates visual representations compatible with Bert embeddings by utilizing block segmentation and feature mapping techniques. Additionally, it incorporates a probability-based weighting mechanism to achieve enhanced multimodal fusion, significantly strengthening the model’s comprehension of semantic relationships across different modalities. Furthermore, to enhance both training efficiency and parameter optimization capabilities of the model, we introduce a Multi-strategy Improved Coati Optimization Algorithm (MSCOA). This algorithm integrates Good Point Set initialization with a Golden Sine search strategy, thereby boosting global exploration, accelerating convergence, and effectively preventing entrapment in local optima. Consequently, it exhibits robust adaptability and stable performance within high-dimensional search spaces. The experimental results show that the FCMNet model has increased the accuracy and precision by 2.61% and 2.85%, respectively, compared with the baseline model on the self-built dataset of tomato leaf diseases, and the recall and F1 score have increased by 3.03% and 3.06%, respectively, which is significantly superior to the existing methods. This research provides a new solution for the identification of tomato leaf diseases and has broad potential for agricultural applications. Full article

Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels. Full article

The propagation of interface acoustic waves (IAWs) along rotated YX-LiNbO₃/SU-8/ZX-Si structures is theoretically investigated to identify the Y-rotation angles that support the efficient propagation of low-loss modes guided along the structure’s interface. A three-dimensional finite element analysis was performed to simulate IAW propagation in the layered structure and to optimize design parameters, specifically the thicknesses of the platinum (Pt) interdigital transducers (IDTs) and the SU-8 adhesive layer. The simulations revealed the existence of two types of IAWs travelling at different velocities under specific Y-rotated cuts of the LiNbO₃ half-space. These IAWs are faster than the surface acoustic wave (SAW) and slower than the leaky SAW (LSAW) propagating on the surface of the bare LiNbO₃ half-space. The mechanical displacement fields of both IAWs exhibit a rapid decay to zero within a few wavelengths from the LiNbO₃ surface. The piezoelectric coupling coefficients of the IAWs were found to be as high as approximately 7% and 31%, depending on the Y-rotation angle. The theoretical results were experimentally validated by measuring the velocities of the SAW and LSAW on a bare 90° YX-LiNbO₃ substrate, and the velocities of the IAWs in a 90° YX-LiNbO₃/SU-8/Si structure featuring 330 nm thick Pt IDTs, a 200 µm wavelength, and a 15 µm thick SU-8 layer. The experimental data showed good agreement with the theoretical predictions. These combined theoretical and experimental findings establish design principles for exciting two interface waves with elliptical and quasi-shear polarization, offering enhanced flexibility for fluidic manipulation and the integration of sensing functionalities. Full article

The evolution of sixth-generation (6G) wireless networks introduces a complex landscape of cybersecurity challenges due to advanced infrastructure, massive device connectivity, and the integration of emerging technologies. Traditional intrusion detection systems (IDSs) struggle to keep pace with such dynamic environments, often yielding high false alarm rates and poor generalization. This study proposes a novel and adaptive IDS that integrates statistical feature engineering with a deep autoencoder (DAE) to effectively detect a wide range of modern threats in 6G environments. Unlike prior approaches, the proposed system leverages the DAE’s unsupervised capability to extract meaningful latent representations from high-dimensional traffic data, followed by supervised classification for precise threat detection. Evaluated using the CSE-CIC-IDS2018 dataset, the system achieved an accuracy of 86%, surpassing conventional ML and DL baselines. The results demonstrate the model’s potential as a scalable and upgradable solution for securing next-generation wireless networks. Full article

The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice. Full article