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RetinexWT: Retinex-Based Low-Light Enhancement Method Combining Wavelet Transform 认领 引用
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作者 Hongji Chen Jianxun Zhang +2 位作者 Tianze Yu Yingzhu Zeng Huan Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第2期2113-2132,共20页
Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ... Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at http://gffzz188fe103f8f1460asnufu9uo0xc0k69bk.ffgz.tsg.suse.edu.cn/CHEN-hJ516/RetinexWT(accessed on 14 October 2025). 展开更多
关键词 Low-light image enhancement retinex algorithm wavelet transform vision transformer
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Explainable electroencephalography-based attentiondeficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform 认领 引用
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作者 Yavuz Atas Serkan Kırık +9 位作者 Kübra Yıldırım Burak Tasci Prabal Datta Barua Ferhat Balgetir Sengul Dogan Turker Tuncer Ru-San Tan Elizabeth Palmer Aruna Devi U Rajendra Acharya 《World Journal of Psychiatry》 SCIE 2026年第3期141-158,共18页
BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is... BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is timeconsuming and subjective.Electroencephalography(EEG)signals provide an objective alternative,and machine learning methods can improve their diagnostic utility.AIM To develop an explainable EEG-based model for ADHD detection by integrating a novel combination ternary pattern(CTP)feature extractor with twin wavelet transform(TWT)for multilevel signal analysis,and to evaluate its effectiveness in providing accurate,channel-wise,and fusion-based classification results for objective and rapid ADHD diagnosis.METHODS A new EEG dataset containing more than 7000 segments from 137 ADHD patients and 150 controls was studied.A novel feature engineering framework was developed,combining a new CTP extractor with statistical features.A multilevel feature extraction structure was designed using a newly proposed TWT for signal decomposition.Extracted features were reduced to the most informative 263 using neighborhood component analysis.Channelwise classification was performed with k-nearest neighbors,followed by iterative majority voting across 20 EEG channels.RESULTS Single-channel analysis achieved up to 99.12%accuracy.By applying majority voting,overall classification accuracy increased to 99.97%,with similarly high sensitivity and specificity.CONCLUSION Our study introduces a large ADHD EEG dataset and a novel model integrating TWT and CTP.The model provides highly accurate,channel-wise,and fusion-based results,offering a promising objective tool for rapid ADHD diagnosis. 展开更多
关键词 Attention-deficit/hyperactivity disorder detection Combination ternary pattern Electroencephalography signal classification Explainable feature engineering Twin wavelet transform
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Block-Wise Sliding Recursive Wavelet Transform and Its Application in Real-Time Vehicle-Induced Signal Separation 认领 引用
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作者 Jie Li Nan An Youliang Ding 《Structural Durability & Health Monitoring》 EI 2026年第1期1-22,共22页
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ... Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring. 展开更多
关键词 Wavelet transform vehicle-induced signal separation real-time structure monitoring
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基于CWT-MSTransformer的风电齿轮箱故障特征识别模型 认领 引用
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作者 张雪莹 伍轶明 《机电工程》 CAS 北大核心 2026年第5期945-956,共12页
针对风电齿轮箱受强噪声环境影响,导致智能诊断模型诊断准确率严重下降的问题,提出了一种基于连续小波变换和多尺度Transformer(CWT-MSTransformer)的风电齿轮箱故障诊断方法。首先,采用了离散小波变换将风电齿轮的振动信号分解为高频... 针对风电齿轮箱受强噪声环境影响,导致智能诊断模型诊断准确率严重下降的问题,提出了一种基于连续小波变换和多尺度Transformer(CWT-MSTransformer)的风电齿轮箱故障诊断方法。首先,采用了离散小波变换将风电齿轮的振动信号分解为高频分量和低频分量,并利用CWT将其转换为高频和低频时频图;然后,设计了一个多尺度分层特征提取模块,该模块通过不同尺度的动态卷积进行多尺度局部特征提取,并利用注意力机制关注重要的故障特征,剔除了冗余信息;最后,采用高效注意力机制改进了Swin-Transformer,并利用其进行全局特征提取,以挖掘细微的故障特征;采用两个齿轮箱数据集进行了噪声环境下的实验验证。研究结果表明:该方法在强噪声环境中的平均诊断准确率为93.13%,均高于对比方法,这说明CWT-MSTransformer具有良好的抗噪性能;此外,通过梯度类激活映射(Grad-CAM)进行了特征学习可视化处理,结果表明CWT-MSTransformer能够聚焦关键的故障特征。这意味着在噪声环境下,该方法不仅能够有效地识别故障特征,而且能够增强模型的可解释性,在现实故障诊断中具有可行性。 展开更多
关键词 齿轮传动 故障诊断方法 风电机组 基于连续小波变换和多尺度Transformer 模型可解释性增强 梯度类激活映射
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Prediction of wastewater treatment plant influent quality based on discrete wavelet transform and convolutional enhanced transformer 认领 引用
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作者 Lili Ma Danxia Li +2 位作者 Jinrong He Zhirui Niu Zhihua Feng 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第11期405-417,共13页
Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise ge... Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH3-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH3-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH3-N by 78.4%,54.8%,and 53.2%. 展开更多
关键词 Wastewater treatment plant Influent quality prediction Discrete wavelet transform Transformer Local feature Long-term dependencies
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Wavelet Transform Convolution and Transformer-Based Learning Approach for Wind Power Prediction in Extreme Scenarios 认领 引用
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作者 Jifeng Liang Qiang Wang +4 位作者 Leibao Wang Ziwei Zhang Yonghui Sun Hongzhu Tao Xiaofei Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期945-965,共21页
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr... Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R2)as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions. 展开更多
关键词 Extreme scenarios conditional generative adversarial network wavelet transform Transformer wind power prediction
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Fluorescence microscopy image denoising via a wavelet-enhanced transformer based on DnCNN network 认领 引用
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作者 Shuhao Shen Mingxuan Cao +2 位作者 Weikai Tan E Du Xueli Chen 《Advanced Photonics Nexus》 CSCD 2025年第6期1-11,共11页
Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN th... Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer-convolutional neural network(CNN)architecture.Wavelet decomposition separates highand low-frequency components for targeted noise reduction;the CNN branch restores local details,whereas the transformer branch captures global context;and an adaptive loss balances quantitative fidelity with perceptual quality.On the fluorescence microscopy denoising benchmark,our method surpasses leading CNNand transformer-based approaches,improving peak signal-to-noise ratio by 2.34%and 0.88%and structural similarity index measure by 0.53%and 1.07%,respectively.This framework offers enhanced generalization and practical gains for fluorescence image denoising. 展开更多
关键词 fluorescence microscopy denoising deep learning wavelet transform vision transformer convolutional neural network.
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Image Watermarking Algorithm Base on the Second Order Derivative and Discrete Wavelet Transform 认领 引用
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作者 Maazen Alsabaan Zaid Bin Faheem +1 位作者 Yuanyuan Zhu Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2025年第7期491-512,共22页
Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embe... Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content.Image watermarking can also be used to verify the authenticity of digital media,such as images or videos,by ascertaining the watermark information.In this paper,a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform(DWT),chaotic map,and Laplacian operator.The DWT can be used to decompose the image into its frequency components,chaos is used to provide extra security defense by encrypting the watermark signal,and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image.These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands.The mid-sub-band maintains the invisible property of the watermark,and chaos combined with the second-order derivative Laplacian is vulnerable to attacks.Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks,i.e.,compression,noise addition,and filtering.Moreover,this approach also maintains image quality through peak signal-to-noise ratio(PSNR)and structural similarity index metrics(SSIM).The highest achieved PSNR and SSIM values are 55.4 dB and 1.In the same way,normalized correlation(NC)values are almost 10%–20%higher than comparative research.These results support assistance in copyright protection in multimedia content. 展开更多
关键词 Discrete wavelet transform laplacian image watermarking chaos multimedia security
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基于VMD-CWT和Swin Transformer的滚动轴承故障诊断方法 认领 引用 被引量：4
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作者 曾信凌 龙江 +1 位作者 魏友 吴云飞 《机械制造与自动化》 2025年第6期18-23,34,共6页
针对滚动轴承故障信号存在噪声干扰且故障特征提取不精确的问题,提出一种基于变分模态分解(VMD)、连续小波变换(CWT)和Swin Transformer网络相结合的滚动轴承智能故障诊断方法。利用变分模态分解对信号进行降噪,通过CWT将重构后的信号... 针对滚动轴承故障信号存在噪声干扰且故障特征提取不精确的问题,提出一种基于变分模态分解(VMD)、连续小波变换(CWT)和Swin Transformer网络相结合的滚动轴承智能故障诊断方法。利用变分模态分解对信号进行降噪,通过CWT将重构后的信号转换为时频图;以二维特征图像作为输入训练Swin Transformer模型,实现滚动轴承的智能故障诊断。试验结果表明:VMD-CWT结合Swin Transformer网络的方法具有更高的故障诊断精度,实测数据中测试集准确率高达99.79%。 展开更多
关键词 滚动轴承 变分模态分解 连续小波变换 Swin Transformer 故障诊断
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A Deep Learning Approach for Fault Diagnosis in Centrifugal Pumps through Wavelet Coherent Analysis and S-Transform Scalograms with CNN-KAN 认领 引用
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作者 Muhammad Farooq Siddique Saif Ullah Jong-Myon Kim 《Computers, Materials & Continua》 SCIE EI 2025年第8期3577-3603,共27页
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ... Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability. 展开更多
关键词 Fault diagnosis centrifugal pump wavelet coherent analysis stockwell transform convolutional neural network Kolmogorov-Arnold network
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MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection 认领 引用
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作者 Jia Liu Hao Chen +5 位作者 Hang Gu Yushan Pan Haoran Chen Erlin Tian Min Huang Zuhe Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期687-710,共24页
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra... Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability. 展开更多
关键词 Remote sensing change detection deep learning wavelet transform multi-scale
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Wavelet-based analysis of aeolian sand dynamics and adaptive mitigation strategies for desert highways 认领 引用
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作者 YANG Lingxiang CHENG Jianjun +3 位作者 YAO Bin WANG Yaqiang GAO Li WU Xiao 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1182-1200,共19页
Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate charact... Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales.Moreover,prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies.To address these issues,this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways.The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data(including ERA5 wind datasets,sand samples,ASTER GDEM,and multi-temporal remote sensing imagery)to enable a comprehensive aeolian environmental assessment.Concurrently,a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways.Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang,China,the framework exhibits high accuracy:predictions of annual aeolian sand transport activity show relative errors mostly below 7%against long-term reference sequences,and the calculated resultant drift direction exhibits a strong correlation with observed dune migration,yielding an R-squared value of 0.96.These findings confirm the framework’s reliability and provide a robust basis for designing adaptive,location-specific mitigation strategies,thereby enhancing the sustainability of desert highway infrastructure. 展开更多
关键词 Desert highway Aeolian sand environment Wavelet transform Drift potential Adaptive mitigation measures
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WaSA-Net:Wavelet-Guided Tokenization and Dynamic Sparse Attention for Histopathology Image Classification 认领 引用
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作者 Muhammad Zaheer Sajid Muhammad Fareed Hamid +1 位作者 Nauman Ali Khan Imran Qureshi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1259-1290,共32页
Digital pathology is rapidly transforming histopathological diagnosis,yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue,which limits ... Digital pathology is rapidly transforming histopathological diagnosis,yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue,which limits both diagnostic accuracy and computational efficiency.This paper proposes WaSA-Net,an end-to-end architecture that integrates three complementary modules for histopathological image analysis.First,the Wavelet-Guided Tokenization(WGT)module decomposes input images into frequency-aware representations using learnable wavelet-like filters,so that both global tissue structures and fine-grained cellular patterns are exposed to attention from the first layer.Second,the Dynamic Sparse Attention with Pathology Priors(DSA-PP)module adaptively selects diagnostically informative tokens through a lightweight gating mechanism and incorporates learnable pathology prior tokens that embed domain-specific inductive biases,reducing attention complexity while preserving critical contextual information.Third,the Cross-Frequency Feature Pyramid Fusion(CFFPF)module performs bidirectional cross-attention across frequency bands and applies adaptive per-sample frequency weighting to identify the most discriminative frequency components for each tissue type.The proposed architecture is evaluated on three widely used histopathology benchmarks:PatchCamelyon for metastasis detection,PathMNIST for multi-class colorectal tissue classification,and BreakHis for breast cancer diagnosis.WaSA-Net achieves strong performance with only 4.8 M parameters,reaching 95.91%accuracy(AUC 0.9981)on PathMNIST,93.47%accuracy(AUC 0.9812)on PatchCamelyon,and 96.72%accuracy(AUC 0.9923)on BreakHis.Despite its compact design,WaSA-Net matches or surpasses larger models while requiring no external pretraining data.These results indicate that frequency-aware representations and dynamic sparse attention can improve both efficiency and diagnostic performance in digital pathology. 展开更多
关键词 Digital pathology wavelet transform sparse attention frequency-guided tokenization telepathology computational pathology deep learning
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基于改进SVD-EWT的环网柜局放信号自适应去噪方法 认领 引用 被引量：1
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作者 刘国伟 廖晓青 +3 位作者 陈历 梁汇 谭达禹 刘俊峰 《南方能源建设》 2026年第1期147-156,共10页
[目的]在电气设备的健康监测中,局部放电(Partial Discharge,PD)信号常受到各种噪声源的干扰,这些干扰主要来自设备自身的运行噪声或外部环境的干扰。[方法]为有效解决噪声干扰问题,提高局放检测的准确性和可靠性,提出一种基于频谱分析... [目的]在电气设备的健康监测中,局部放电(Partial Discharge,PD)信号常受到各种噪声源的干扰,这些干扰主要来自设备自身的运行噪声或外部环境的干扰。[方法]为有效解决噪声干扰问题,提高局放检测的准确性和可靠性,提出一种基于频谱分析的自适应奇异值分解(Singular Value Decomposition,SVD)和经验小波变换(Empirical Wavelet Transform,EWT)相结合的去噪算法。首先,对含噪PD信号进行快速傅里叶变换(Fast Fourier Transform,FFT)频谱分析,提出改进经典阈值和频谱幅值行向量峭度判别相结合的窄带干扰数量确定方法,重构并去除周期性窄带干扰噪声。随后,采用EWT算法对残留白噪声的PD信号进行自适应分解,筛选满足峭度条件的模态分量重构PD信号。最后,利用改进阈值方法去除重构信号中的少量白噪声,得到去噪后的PD信号。[结果]仿真及实测去噪处理结果表明,所提方法分别在信噪比、均方根误差、相关系数以及降噪率指标上达到7.02、0.0112、0.9003和33.0057。[结论]该方法能够有效去除窄带干扰及白噪声,相比于其他去噪方法,所提方法在多个评价指标上均有所改善,具有良好的去噪效果。 展开更多
关键词 电气环网柜 局部放电 频谱分析 奇异值分解 经验小波变换 改进阈值法
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基于EWT-SVD改进LSSVM的隔离开关振动信号识别 认领 引用 被引量：1
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作者 曾华荣 马晓红 +2 位作者 许逵 杨旗 殷蔚翎 《微型电脑应用》 2026年第2期294-298,共5页
为提高隔离开关振动信号识别的准确率,提出一种基于改进最小二乘支持向量机(LSSVM)的识别方法。所提出的方法采用经验小波变换(EWT)分解提取隔离开关振动信号的时频特征,采用奇异值分解(SVD)提取隔离开关振动信号的奇异值特征,并将时频... 为提高隔离开关振动信号识别的准确率,提出一种基于改进最小二乘支持向量机(LSSVM)的识别方法。所提出的方法采用经验小波变换(EWT)分解提取隔离开关振动信号的时频特征,采用奇异值分解(SVD)提取隔离开关振动信号的奇异值特征,并将时频特征与奇异值特征结合,输入经过粒子群优化(PSO)算法优化正则化参数和核函数宽度参数的LSSVM模型,从而实现对隔离开关不同振动信号的识别。仿真结果表明,所提出的方法可有效识别隔离开关的不同振动信号,且识别的准确率、平均识别准确率分别达98.31%、97.15%,识别准确率标准差为6.13,优于标准LSSVM模型、三维卷积长短期记忆(C3D-LSTM)网络模型和广义回归神经网络(GRNN)模型,可用于电力隔离开关振动状态的识别。 展开更多
关键词 隔离开关振动 振动信号识别 粒子群优化算法 经验小波变换 最小二乘支持向量
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多尺度非对称注意力遥感去雾Transformer 认领 引用 被引量：1
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作者 王旭阳 梁宇航 《广西师范大学学报（自然科学版）》 CAS 北大核心 2026年第2期77-89,共13页
雾霾干扰会导致遥感图像结构模糊、细节丢失,严重影响下游视觉任务的准确性。为此,本文提出一种异构增强的遥感图像去雾网络,从空间结构建模与频率信息整合2个层面提升特征恢复能力。具体而言,设计多尺度非对称注意力Transformer模块,... 雾霾干扰会导致遥感图像结构模糊、细节丢失,严重影响下游视觉任务的准确性。为此,本文提出一种异构增强的遥感图像去雾网络,从空间结构建模与频率信息整合2个层面提升特征恢复能力。具体而言,设计多尺度非对称注意力Transformer模块,引入方向感知机制以增强模糊边缘与纹理细节的建模;同时构建基于小波变换高低频自适应增强模块,使用Haar小波分解分离频域信息,分别通过高频与低频子模块强化边缘轮廓与结构表达。2个模块分别嵌入特征提取与融合阶段,协同缓解传统方法方向性建模不足与高频特征易丢失等问题。在保持低计算开销的前提下,本文方法在HAZE1K与RICE数据集上的平均PSNR/SSIM性能分别达到24.9936/0.9099与33.1802/0.8942,在细节恢复方面表现出显著优势。 展开更多
关键词 遥感图像去雾 Transformer 非对称注意力 高低频特征增强 小波变换 方向感知建模 深度学习
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Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
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作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
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基于混合小波-Transformer框架的VSP数据耦合噪声压制方法 认领 引用
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作者 王腾宇 陈豪 +4 位作者 张振 易浩然 魏巍 麦尔旦·马合木提 蔡涵鹏 《科学技术与工程》 EI 北大核心 2026年第12期4948-4958,共11页
垂直地震剖面(vertical seismic profile,VSP)数据处理中,耦合噪声是主要的干扰信号,其复杂的传播特性和频率重叠使得传统去噪方法难以实现精准压制。为此,提出一种混合小波-Transformer(hybrid wavelet Transformer,HWT)框架,用于耦合... 垂直地震剖面(vertical seismic profile,VSP)数据处理中,耦合噪声是主要的干扰信号,其复杂的传播特性和频率重叠使得传统去噪方法难以实现精准压制。为此,提出一种混合小波-Transformer(hybrid wavelet Transformer,HWT)框架,用于耦合噪声的高效压制。该方法首先通过离散小波变换(discrete wavelet Transform,DWT)对信号进行多尺度频率分解,将原始信号分解到不同频段以减少频率混叠;随后利用双分支Transformer架构分别捕捉局部与全局特征,其中局部分支提取耦合噪声的高频干扰特性,全局分支建模目标信号的时间-频率全局依赖。此外,设计多级特征聚合模块(multi-level feature aggregation module,MFAM),通过整合DWT分解后的频域特征与双分支输出的局部和全局特性,实现了特征的深度融合。基于时频域掩码生成策略对时频表示进行约束,进一步提升了分离质量。在合成数据和真实VSP数据上的实验表明,该方法相比传统方法能够显著压制耦合噪声的同时保持目标信号完整性,为VSP数据的预处理提供了一种高效且鲁棒的解决方案。 展开更多
关键词 耦合噪声 垂直地震剖面(VSP)数据 混合小波-Transformer 深度学习
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基于MODWT-变点矫正的多粒度事件时序预测方法 认领 引用
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作者 李海波 赵振博 +3 位作者 郭春丽 蔡其航 郑黎晓 汤晓康 《计算机集成制造系统》 EI CSCD 北大核心 2026年第6期2177-2196,共20页
为识别时间序列中的事件并提高预测精度,利用多尺度关联特征,提出一种基于变点识别和矫正的多粒度事件预测方法。首先基于最大重叠离散小波变换平滑化时间序列,并识别和矫正偏差点;再通过构建事件树设计多粒度事件检测方法,具体过程包... 为识别时间序列中的事件并提高预测精度,利用多尺度关联特征,提出一种基于变点识别和矫正的多粒度事件预测方法。首先基于最大重叠离散小波变换平滑化时间序列,并识别和矫正偏差点;再通过构建事件树设计多粒度事件检测方法,具体过程包括聚类、符号化、事件树构建和维度对齐,实现跨尺度事件特征的协同表征;最后,基于XGBoost的预测模型,提升预测效果。通过在产品需求和零售库存两个公开数据集的实验,验证了方法的有效性,通过对比,所提方法优于其他ARIMA、K-means SVR和MGE-SP三种方法。这些结果表明,所提方法能够在不同领域的时间序列上提高预测精度。 展开更多
关键词 变点矫正 事件检测 多粒度 时间序列 预测 最大重叠离散小波变换
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基于参数优化的VMD和CWT结构密集模态参数识别 认领 引用
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作者 赵丽洁 孙子一 +2 位作者 王昊 解咏平 练继建 《振动与冲击》 EI CSCD 北大核心 2026年第4期51-60,共10页
针对变分模态分解的模态分解数K及二次惩罚因子α难以确定和连续小波变换对结构密集模态参数识别精度不高的问题,提出了一种基于参数优化变分模态分解(variational mode decomposition,VMD)与连续小波变换(continuous wavelet transform... 针对变分模态分解的模态分解数K及二次惩罚因子α难以确定和连续小波变换对结构密集模态参数识别精度不高的问题,提出了一种基于参数优化变分模态分解(variational mode decomposition,VMD)与连续小波变换(continuous wavelet transform,CWT)相结合的结构密集模态参数识别方法。以能量集中度与互信息构建全新综合目标函数,引入蜣螂优化算法自适应地搜寻最佳[K,α]参数组合;其次,基于最优[K,α]参数组合,对具有密集模态的振动响应信号进行VMD,结合皮尔逊相关系数指标筛选有效模态分量;最后,对有效模态分量进行CWT识别结构的模态频率和模态阻尼比。通过四自由度密集模态系统仿真算例表明,相比传统CWT算法,参数优化VMD结合CWT的方法,识别结构的密集模态参数精度更高,并具备一定的抗噪声性能;五层框架结构模型试验进一步验证了所提方法的实用性。 展开更多
关键词 模态参数识别 变分模态分解(VMD) 连续小波变换(CWT) 密集模态
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