Differentiable architecture search-based automatic modulation recognition for multi-carrier signals by LI Jie, LI Jing, LYU Lu, GONG Fengkui

“…Finally, a joint attention mechanism was introduced into the feature learning process. This mechanism spatially transforming distorted signal features to mitigate the impact of multipath interference, while also calculating and sorting the information weights for each channel of the feature maps to improve the recognition performance of the relevant feature map channels. …”
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Fault Diagnosis of Photovoltaic Grid-connected Inverter Based on Wavelet Analysis by FAN Jinglu, YI Yunlan

“…Aiming to the fault detecting of photovoltaic grid-connected inverter and its intelligent online diagnosis problem, it proposed a C3C3 inverter fault feature extraction method, which used a three-phase inverter output current as a result of judgments based on wavelet analysis, and combines the approximate component and detail component of failure signals as failure feature vector; then used the classification of neural network to complete the fault diagnosis of photovoltaic grid inverters. …”
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Study on the Preprocessing Method of Rolling Bearing Signal based on LFK and Entropy Difference Spectrum Criterion by Yu He, Li Hongru, Sun Jian, Xu Baohua

“…Aiming at the problem that the fault feature of rolling bearing can be easily overwhelmed by random noise,a novel denoising method on the basis of local characteristic- scale decomposition and Fast Kurtogram（ LFK） is presented. …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…A unique combination of convolution and pooling operations was used by the CPCA attention mechanism to perform global average pooling and maximum pooling on the input feature map, multi-dimensional feature information was deeply mined, and then attention weights for each channel and spatial position were accurately generated through nonlinear transformation, guiding the model to focus on the key feature areas of foreign objects and enhance feature extraction capabilities. …”
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DAM-Faster RCNN: few-shot defect detection method for wood based on dual attention mechanism by Xingyu Tong, Zhihong Liang, Mingming Qin, Fangrong Liu, Jiayu Yang, Hengjiang Xiao, Wei Dai

“…The model integrates cross-attention and spatial attention modules to enhance the expression of key region features, suppresses texture noise interference; the improved Wood-Region Proposal Network (WRPs) module utilizes feature mean pooling and cross-layer fusion strategies to significantly improve the quality and robustness of candidate box generation; in addition, the Wood-Feature Reconstruction Head (WFRH) module effectively enhances the adaptability to new classes and few-shot defects through multi-branch classification and weighted fusion mechanisms. …”
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Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System by Yifeng Zhao, Deyun Chen

“…Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. …”
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YOLO-WWBi: An Optimized YOLO11 Algorithm for PCB Defect Detection by Yi Zhao, Zhidi Jiang

“…Then, BiFPN is integrated into the neck to enhance the quality of fused features and deepen the interaction of feature information. …”
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FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA by MA WeiPing, HONG KunYue, AN Ning, SONG YuZhou

“…The maximum energy amplitude was obtained at the fault feature frequency, making it easy to identify fault features. …”
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Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach by Yanhan Zhu, Yong Li, Tianyi Wei

“…To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals—enabling targeted interference and degrading communication performance—this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). …”
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Subject-object asymmetries in the processing of European Portuguese cleft structures by Xinyi Li, Maria Lobo, Joana Teixeira

“…It is thus plausible that different theoretical perspectives on intervention effects (featural Relativized Minimality and similarity-based interference models) complement each other, predicting that morphosyntactic features trigger stronger effects than semantic features. …”
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Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach by James Skoric, Anna M Lomanowska, Tahir Janmohamed, Heather Lumsden-Ruegg, Joel Katz, Hance Clarke, Quazi Abidur Rahman

“…MethodsInformation entered into the MMP app by 160 Transitional Pain Service patients over a 1-month period, including profile information, pain records, daily reflections, and clinical questionnaire responses, was used to extract 245 relevant variables, referred to as features, for use in a machine learning model. The machine learning model was developed using logistic regression with recursive feature elimination to predict clinically significant improvements in pain-related pain interference, assessed by the PROMIS Pain Interference 8a v1.0 questionnaire. …”
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Approach to Semantic Visual SLAM for Bionic Robots Based on Loop Closure Detection with Combinatorial Graph Entropy in Complex Dynamic Scenes by Dazheng Wang, Jingwen Luo

“…In complex dynamic environments, the performance of SLAM systems on bionic robots is susceptible to interference from dynamic objects or structural changes in the environment. …”
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Involuntary and voluntary memory retrieval relies on distinct neural representations and oscillatory processes. by Malte Kobelt, Gerd T Waldhauser, Marie-Christin Fellner, Nikolai Axmacher

“…We show that involuntary retrieval reactivated sensory feature-dependent yet item-unspecific representations via temporally extended memory replay, accompanied by rapid mid-frontal theta-power increases, indicating memory interference. …”
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TCAINet an RGB T salient object detection model with cross modal fusion and adaptive decoding by Hong Peng, Yunfei Hu, Baocai Yu, Zhen Zhang

“…Although some feature decoding strategies attempt to mitigate noise interference, they often struggle in high-noise environments and lack flexible feature weighting, further restricting fusion capabilities. …”
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CEEMDAN-MRAL Transformer Vibration Signal Fault Diagnosis Method Based on FBG by Hong Jiang, Zhichao Wang, Lina Cui, Yihan Zhao

“…The unique MRAL-Net is proposed to extract the spatial features of the signal and analyze the time series dependence of the features to improve the richness of the signal feature scale. …”
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Fault detection and classification of bipolar DC system with dedicated metallic return based on TF-ENSR by Bo Ren, Niancheng Zhou, Qianggang Wang

“…To ensure that the feature set is sufficiently concise while fully describing the fault state, the Feature-selector is integrated to efficiently eliminate redundant features. …”
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Multi-Level Foreground Prompt for Incremental Object Detection by Jianwen Mo, Ronghua Zou, Hua Yuan

“…Second, the feature map output by the teacher model is used as a feature-level prompt, with a feature distillation loss guiding the student model to encode new class foreground information in less significant channels of the old feature map, reducing interference. …”
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基于峭度和小波包能量特征的齿轮箱早期故障诊断及抗噪研究 by 陈水宣, 邹俊, 易际明, 谢丹

“…It is difficult to dectect the gear fault signal in early stage because of weak intensity and strong interference.To solve this problem,a method for incipient fault diagnosis of gears is proposed based on vibration signals using kurtosis,wavelet packet energy features extraction and discriminative weighted probabilistic neural networks.The method uses the advantages of the kurtosis statistics on the impact load feature extraction method in feature extraction and reserves the merit of wavelet packet decomposition in extracting energy characteristics of various frequency bands.Meanwhile,the discriminative weight probabilistic neural network（DWPNN） is introduced to solve the problem of the scene noise pollution.The experimental results show that the method achieves a good identification of incipient faults of gears and has strong robustness against noise disturbance.…”
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An Intelligent Sample Selection Method for Space-Time Adaptive Processing in Heterogeneous Environment by Chongdi Duan, Yu Li, Wei Wei Wang

“…Based on the feature vector, the multi-dimensional feature projection based on the kernel function is carried out to achieve the offline training classifier, and the testing samples are classified with intelligence in the multi-dimensional feature space by utilizing the trained support vector machine. …”
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Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy by Xiang Wang, Yang Du, Xiaoting Ji

“…Subsequently, recursive feature elimination (RFE) is employed for feature selection. …”
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