Search Results (223)

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems. Full article

Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring. Full article

Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geographic scenes, low target-background discriminability in overhead views (e.g., indistinct boundaries, ambiguous textures, and low contrast) significantly complicates local–global information modeling and results in blurred boundaries and classification errors in model predictions. To address this issue, in this paper, we proposed a novel Multi-Scale Local–Global Mamba Feature Pyramid Network (MLMFPN) through designing a local–global information synergy modeling strategy, and guided and enhanced the cross-scale contextual information interaction in the feature fusion process to obtain quality semantic features to be used as cues for precise semantic reasoning. The proposed MLMFPN comprises two core components: Local–Global Align Mamba Fusion (LGAMF) and Context-Aware Cross-attention Interaction Module (CCIM). Specifically, LGAMF designs a local-enhanced global information modeling through asymmetric convolution for synergistic modeling of the receptive fields in vertical and horizontal directions, and further introduces the Vision Mamba structure to facilitate local–global information fusion. CCIM introduces positional encoding and cross-attention mechanisms to enrich the global-spatial semantics representation during multi-scale context information interaction, thereby achieving refined segmentation. The proposed methods are evaluated on the ISPRS Potsdam and Vaihingen datasets and the outperformance in the results verifies the effectiveness of the proposed method. Full article

The Jumonji C (JMJ-C) domain-containing gene family regulates epigenetic and developmental processes in plants. We identified 55 JMJ-C genes in Populus alba × Populus glandulosa using HMM and BLASTp analyses. Chromosomal mapping revealed an asymmetric distribution with conserved synteny. Phylogenetic reconstruction revealed that PagJMJ genes segregate into five evolutionarily conserved subfamilies, exhibiting classification patterns identical to those of Arabidopsis thaliana and Populus trichocarpa. Synteny analysis indicated a closer relationship with P. trichocarpa than with A. thaliana. Motif and promoter analyses highlighted subfamily-specific features and diverse cis-elements, particularly light-responsive motifs. Expression profiling revealed tissue-specific patterns, with key genes enriched in roots, vascular tissues, and leaves. Developmental analysis in cambium and xylem identified four expression clusters related to wood formation. Co-expression analysis identified six key PagJMJ genes (PagJMJ6, 29, 34, 39, 53, and 55) strongly associated with wood formation-related transcription factors. ChIP-qPCR analysis revealed that key genes co-expressed with PagJMJ genes were marked by H3K4me3 and H3K9me2 modifications. These findings provide insights into the evolutionary and functional roles of PagJMJ genes in poplar vascular development and wood formation. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article

The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-based growth stage classification of butterhead lettuce (Lactuca sativa L.) using two data types: raw images and images processed through GrabCut–Watershed segmentation. A ResNet50-based transfer learning model was trained and evaluated on each dataset, and cross-domain performance was assessed to understand generalization capability. Models trained and tested within the same domain achieved high accuracy (Model 1: 99.65%; Model 2: 97.75%). However, cross-domain evaluations revealed asymmetric performance degradation—Model 1-CDE (trained on raw images, tested on preprocessed images) achieved 82.77% accuracy, while Model 2-CDE (trained on preprocessed images, tested on raw images) dropped to 34.15%. Although GrabCut–Watershed offered clearer visual inputs, it limited the model’s ability to generalize due to reduced contextual richness and oversimplification. In terms of inference efficiency, Model 2 recorded the fastest model-only inference time (0.037 s/image), but this excluded the segmentation step. In contrast, Model 1 achieved 0.055 s/image without any additional preprocessing, making it more viable for real-time deployment. Notably, Model 1-CDE combined the fastest inference speed (0.040 s/image) with stable cross-domain performance, while Model 2-CDE was both the slowest (0.053 s/image) and least accurate. Grad-CAM visualizations further confirmed that raw image-trained models consistently attended to meaningful plant structures, whereas segmentation-trained models often failed to localize correctly in cross-domain tests. These findings demonstrate that training with raw images yields more robust, generalizable, and deployable models. The study highlights the importance of domain consistency and preprocessing trade-offs in vision-based agricultural systems and lays the groundwork for lightweight, real-time AI applications in smart farming. Full article

Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant challenges to conventional CNN-based methods in real-world environments. Specifically, the proposed ETAFHrNet focuses on two predominant pavement-distress morphologies—linear cracks (transverse and longitudinal) and alligator cracks—and has been empirically validated on their intersections and branching patterns over both asphalt and concrete road surfaces. In this work, we present ETAFHrNet, a novel attention-guided segmentation network designed to address the limitations of traditional architectures in detecting fine-grained and asymmetric patterns. ETAFHrNet integrates Transformer-based global attention and multi-scale hybrid feature fusion, enhancing both contextual perception and detail sensitivity. The network introduces two key modules: the Efficient Hybrid Attention Transformer (EHAT), which captures long-range dependencies, and the Cross-Scale Hybrid Attention Module (CSHAM), which adaptively fuses features across spatial resolutions. To support model training and benchmarking, we also propose QD-Crack, a high-resolution, pixel-level annotated dataset collected from real-world road inspection scenarios. Experimental results show that ETAFHrNet significantly outperforms existing methods—including U-Net, DeepLabv3+, and HRNet—in both segmentation accuracy and generalization ability. These findings demonstrate the effectiveness of interpretable, multi-scale attention architectures in complex object detection and image classification tasks, making our approach relevant for broader applications, such as autonomous driving, remote sensing, and smart infrastructure systems. Full article

Hyperspectral imagery (HSI), with its rich spectral information across continuous wavelength bands, has become indispensable for fine-grained land cover classification in remote sensing applications. Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. Experimental results from four common hyperspectral data sets validate the effectiveness of the proposed method. Specifically, when 10, 10, 10 and 25 samples from each class are selected for training, ASLNN achieves the highest overall accuracy (OA) of 81.12%, 85.88%, 80.62%, and 97.97% on the four data sets, outperforming other methods with increases of more than 1.70%, 3.21%, 3.78%, and 2.70% in OA, respectively. Full article

Clinical text classification presents significant challenges in healthcare informatics due to inherent asymmetries in domain-specific terminology, knowledge distribution across specialties, and imbalanced data availability. We introduce MTTL-ClinicalBERT, a symmetrical multi-task transfer learning framework that harmonizes knowledge sharing across diverse medical specialties while maintaining balanced performance. Our approach addresses the fundamental problem of symmetry in knowledge transfer through three innovative components: (1) an adaptive knowledge distillation mechanism that creates symmetrical information flow between related medical domains while preventing negative transfer; (2) a bidirectional hierarchical attention architecture that establishes symmetry between local terminology analysis and global contextual understanding; and (3) a dynamic task-weighting strategy that maintains equilibrium in the learning process across asymmetrically distributed medical specialties. Extensive experiments on the MTSamples dataset demonstrate that our symmetrical approach consistently outperforms asymmetric baselines, achieving average improvements of 7.2% in accuracy and 6.8% in F1-score across five major specialties. The framework’s knowledge transfer patterns reveal a symmetric similarity matrix between specialties, with strongest bidirectional connections between cardiovascular/pulmonary and surgical domains (similarity score 0.83). Our model demonstrates remarkable stability and balance in low-resource scenarios, maintaining over 85% classification accuracy with only 30% of training data. The proposed framework not only advances clinical text classification through its symmetrical design but also provides valuable insights into balanced information sharing between different medical domains, with broader implications for symmetrical knowledge transfer in multi-domain machine learning systems. Full article

The latest research on the governance of construction projects has centered on the cooperation efficiency among project participants and the enhancement of project performance. Trust networks can exert a remarkable role in this aspect, facilitating the cooperative behaviors of all project participants and achieving project value. Nevertheless, the facilitating factors of trust networks remain unclear, and the traditional classification factors of trust relationships between binary subjects have not been verified from the perspective of relationship networks. Hence, this study carried out a structural equation model analysis on 139 valid questionnaires to disclose the influence mechanism of trust types and trust networks, while taking into account the moderating effect produced by the objective situation of asymmetric dependence. The research indicates that cognition-based and affect-based trust positively affect the trust networks. Affect-based trust on trust networks is positively moderated by asymmetric dependence. Based on this, the trust networks research in construction project governance has established a distinct set of influence paths, and relevant governance measures will be systematically implemented. Full article

Unsupervised sentence embedding, vital for numerous NLP tasks, struggles with the inherent asymmetry of semantic relationships within contrastive learning (CL). This paper proposes Label Smoothing-based Ranking Negative Sampling (LS-RNS), a novel framework that directly tackles the semantic asymmetry between anchor and negative samples in CL. LS-RNS utilizes a Large Language Model (LLM) to assess fine-grained asymmetric similarity scores between sentences, constructing a ranking-aware negative sampling strategy combined with adaptive label smoothing. This design encourages the model to learn more effectively from informative negatives that are semantically closer to the anchor, leading to asymmetry-aware sentence embeddings. Experiments on standard Semantic Textual Similarity (STS) benchmarks (STS12–STS16, STS-B, SICK-R) show that LS-RNS achieves state-of-the-art performance. We adopt Spearman’s rank correlation coefficient as the primary evaluation metric for semantic similarity tasks, and we use classification accuracy for downstream and transfer tasks. LS-RNS achieves 79.87 on STS tasks with BERT-base (vs. 76.25 for SimCSE, +3.62) and 80.41 with RoBERTa-base (vs. 79.18 for DiffCSE). On transfer tasks, it attains 88.82 (BERT) and 87.68 (RoBERTa), consistently outperforming PromptBERT and SNCSE. On STL-10, LS-RNS improves SimCLR top-one accuracy from 79.50% to 80.52% with ResNet-18 and from 68.91% to 72.19% with VGG-16, even enabling a shallow ResNet-18 to surpass a deeper ResNet-34 baseline. These results confirm the modality-agnostic effectiveness of LS-RNS and its potential to redefine contrastive learning objectives by modeling semantic asymmetry, rather than relying solely on encoder depth or pre-training objectives. Full article

Deep learning-based hyperspectral image (HSI) classification methods, such as Transformers and Mambas, have attracted considerable attention. However, several challenges persist, e.g., (1) Transformers suffer from quadratic computational complexity due to the self-attention mechanism; and (2) both the local and global feature extraction capabilities of large kernel convolutional neural networks (LKCNNs) need to be enhanced. To address these limitations, we introduce a multi-scale large kernel asymmetric CNN (MSLKACNN) with the large kernel sizes as large as $1\times 17$ and $17\times 1$ for HSI classification. MSLKACNN comprises a spectral feature extraction module (SFEM) and a multi-scale large kernel asymmetric convolution (MSLKAC). Specifically, the SFEM is first utilized to suppress noise, reduce spectral bands, and capture spectral features. Then, MSLKAC, with a large receptive field, joins two parallel multi-scale asymmetric convolution components to extract both local and global spatial features: (C1) a multi-scale large kernel asymmetric depthwise convolution (MLKADC) is designed to capture short-range, middle-range, and long-range spatial features; and (C2) a multi-scale asymmetric dilated depthwise convolution (MADDC) is proposed to aggregate the spatial features between pixels across diverse distances. Extensive experimental results on four widely used HSI datasets show that the proposed MSLKACNN significantly outperforms ten state-of-the-art methods, with overall accuracy (OA) gains ranging from 4.93% to 17.80% on Indian Pines, 2.09% to 15.86% on Botswana, 0.67% to 13.33% on Houston 2013, and 2.20% to 24.33% on LongKou. These results validate the effectiveness of the proposed MSLKACNN. Full article

This study introduces Ship-Yolo, a novel algorithm designed to tackle the challenges of detecting small targets against complex backgrounds in remote sensing imagery. Firstly, the proposed method integrates an efficient local attention mechanism into the C3 module of the neck network, forming the EDC module. This enhancement significantly improves the model’s capability to capture critical features, enabling robust performance in scenarios involving intricate backgrounds and multi-scale targets. Secondly, a Lightweight Asymmetric Decoupled Head (LADH-Head) is proposed to separate classification and regression tasks, reducing task conflicts, improving detection performance, and maintaining the model’s lightweight characteristics. Additionally, the LiteConv module is designed to replace the C3 module in the backbone network, leveraging partial convolution to ignore invalid information in occluded regions and avoid misjudgments. Finally, the Content-Aware Reassembly Upsampling Module (CARAFE) is employed to replace the original upsampling module, expanding the receptive field to better capture global information while preserving the lightweight nature of the model. Experiments on the ShipRSImageNet and DOTA datasets demonstrate that Ship-Yolo outperforms other YOLO variants and existing methods in terms of precision, recall, and average precision, exhibiting strong generalization capabilities. Ablation studies further validate the stable performance improvements contributed by the EDC, LADH-Head, LiteConv, and CARAFE modules. Full article

The spatial distribution characteristics of remote sensing scene imagery exhibit significant complexity, necessitating the extraction of critical semantic features and effective discrimination of feature information to improve classification accuracy. While the combination of traditional convolutional neural networks (CNNs) and Transformers has proven effective in extracting features from both local and global perspectives, the multilayer perceptron (MLP) within Transformers struggles with nonlinear problems and insufficient feature representation, leading to suboptimal performance in fused models. To address these limitations, we propose a Swin Kansformer network for remote sensing scene classification, which integrates the Kolmogorov–Arnold Network (KAN) and employs a window-based self-attention mechanism for global information extraction. By replacing the traditional MLP layer with the KAN module, the network approximates functions through the decomposition of complex multivariate functions into univariate functions, enhancing the extraction of complex features. Additionally, an asymmetric convolution group module is introduced to replace conventional convolutions, further improving local feature extraction capabilities. Experimental validation on the AID and NWPU-RESISC45 datasets demonstrates that the proposed method achieves classification accuracies of 97.78% and 94.90%, respectively, outperforming state-of-the-art models such as ViT + LCA and ViT + PA by 0.89%, 1.06%, 0.27%, and 0.66%. These results highlight the performance advantages of the Swin Kansformer, while the incorporation of the KAN offers a novel and promising approach for remote sensing scene classification tasks with broad application potential. Full article