Fine-grained image classification using the MogaNet network and a multi-level gating mechanism by Dahai Li, Su Chen

“…Meanwhile, a multi-level gating mechanism is introduced to obtain the saliency features of images. A feature elimination strategy is proposed to suppress the interference of fuzzy class features and background noise. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…MethodsThe MFCC is used to extract features from vibration signals contaminated by noise. …”
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RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8 by Yifan Wei, Jun Tao, Wenjun Wu, Donghua Yuan, Shunzhi Hou

“…Firstly, a combination of hybrid inflated convolution and RefConv is used to redesign the lightweight Ref-Dilated convolution block, which reduces the model computation. Second, a new feature pyramid network fusion module, the Hybrid Bridge Feature Pyramid Network (HBFPN), is designed to fuse the deep features with the high-level features, as well as the features of the current layer, to improve the feature extraction capability for fuzzy objects. …”
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CDBA-GAN: A Conditional Dual-Branch Attention Generative Adversarial Network for Robust Sonar Image Generation by Wanzeng Kong, Han Yang, Mingyang Jia, Zhe Chen

“…The framework comprises three key innovations: The conditional information fusion module, dual-branch attention feature fusion mechanism, and cross-layer feature reuse. …”
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A New Reliable Switched-Capacitor-Based High Step-Up Five-Level Inverter by Milad Ghavipanjeh Marangalu, Naser Vosoughi Kurdkandi, Kourosh Khalaj Monfared, Yousef Neyshabouri, Hani Vahedi

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IRSD-Net: An Adaptive Infrared Ship Detection Network for Small Targets in Complex Maritime Environments by Yitong Sun, Jie Lian

“…To further improve cross-scale fusion, we design the Dynamic Cross-Scale Feature Pyramid Network (DCSFPN), a bidirectional architecture that combines up- and downsampling to integrate low-level detail with high-level semantics. …”
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A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin by Haonan Jiang, Yanbo Zhao, Qiao Zhu, Yuanli Cai

“…Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition by Haodong Chi, Huiyuan Chen

“…To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm (MSSA) under local mean decomposition (LMD). …”
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PS-GAN: A Novel Pseudo-Siamese Generative Adversarial Network for Multimodal Remote Sensing Image Change Detection by Zhifu Zhu, Xiping Yuan, Shu Gan, Raobo Li, Rui Bi, Weidong Luo, Cheng Chen

“…In addition, we adopt U2Net+ as the CD model and develop a gated feature modulation mechanism and an enhanced squeeze–and-excitation module to reduce the interference of redundant features while enhancing the semantic representation of change features, further improving CD accuracy. …”
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Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification by Dandan Ma, Shijie Xu, Zhiyu Jiang, Yuan Yuan

“…To address these issues, we propose a central pixel-based dual-branch network (CPDB-Net) that synergistically integrates CNN and ViT for robust feature extraction. Specifically, the central spectral feature extraction branch based on CNN serves as a strong prior to reinforce the importance of central pixel features in classification. …”
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A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11 by Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu, Xia Fang

“…After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. …”
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Class-Discrepancy Dynamic Weighting for Cross-Domain Few-Shot Hyperspectral Image Classification by Chen Ding, Jiahao Yue, Sirui Zheng, Yizhuo Dong, Wenqiang Hua, Xueling Chen, Yu Xie, Song Yan, Wei Wei, Lei Zhang

“…It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. …”
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MC-ASFF-ShipYOLO: Improved Algorithm for Small-Target and Multi-Scale Ship Detection for Synthetic Aperture Radar (SAR) Images by Yubin Xu, Haiyan Pan, Lingqun Wang, Ran Zou

“…Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. …”
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Automatic picking method for ground penetrating radar wave groups at rough coal-rock interfaces by Ying TIAN, Chunzhi LI, Shuo CHEN, Zihao WANG, Fuyan LYU, Qiang ZHANG, Meng HAN, Chengjun HU

“…The method also employs a RANSAC iterative fitting algorithm and waveform feature matching to classify and identify interfering hyperbolas and coal-rock interface curves. …”
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Functions of Intrinsically Disordered Regions by Linhu Xiao, Kun Xia

“…IDRs harbor molecular recognition features (MoRFs) and post-translational modification sites (e.g., phosphorylation), enabling dynamic intermolecular interactions through conformational plasticity. …”
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Improving YOLOv11 for marine water quality monitoring and pollution source identification by Fang Wang

“…Additionally, Multi-scale Feature Fusion (MFF) combines Convolutional Neural Networks (CNN) and Transformer-based feature extraction to enhance robustness in complex environments. …”
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Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow by Shu Dai, Lei Liang, Ke Jiang, Hui Wang, Chengyi Zhong

“…It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. …”
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AAV Parameters Estimation Based on Improved Time-Frequency Ridge Extraction and Hough Transform by Yongji Yu, Yonghong Ruan, Junjie Zhong

“…However, existing methods are prone to noise interference and exhibit poor performance in extracting multi-rotor and multi-component signals. …”
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