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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition 认领 引用 被引量：1
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作者 Miao Shi Chao Wang +3 位作者 Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie 《China Communications》 SCIE CSCD 2022年第2期47-61,共15页
Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing metho... Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30. 展开更多
关键词 ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning
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A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA 认领 引用 被引量：1
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作者 DONG Hanchuan LIU Shuang +4 位作者 PANG Lili LIU Dunlong DENG Longsheng FANG Lide ZHANG Zhonghua 《Journal of Mountain Science》 SCIE CSCD 2024年第12期4019-4032,共14页
Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based v... Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals. 展开更多
关键词 Debris flow infrasound Variational Mode Decomposition Sparrow search algorithm ARIMA model Hilbert transform
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基于AVMD-BiLSTM模型的飞机动态RCS预测方法 认领 引用
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作者 任艳 张永峥 +2 位作者 孙旭 傅莉 刘昕 《航空发动机》 北大核心 2026年第3期165-174,共10页
为了提高动态飞行条件下飞机雷达散射截面(RCS)序列预测的准确性,针对性地建立了一种结合自适应变分模态分解(AVMD)与双向长短期记忆(BiLSTM)网络的动态RCS序列预测模型(AVMD-BiLSTM)。通过AVMD技术对动态RCS序列进行自适应尺度的变分... 为了提高动态飞行条件下飞机雷达散射截面(RCS)序列预测的准确性,针对性地建立了一种结合自适应变分模态分解(AVMD)与双向长短期记忆(BiLSTM)网络的动态RCS序列预测模型(AVMD-BiLSTM)。通过AVMD技术对动态RCS序列进行自适应尺度的变分模态分解,提取序列的多尺度特征;利用BiLSTM神经网络对分解得到的各模态分量进行精确预测,并将预测值进行加和重构,得到飞机动态RCS的最终预测结果。选取了2种典型低可探测性飞机在水平(HH)和垂直(VV)极化状态下的动态RCS数据,对所提出模型进行有效性验证。结果表明:与传统的自回归积分移动平均(ARIMA)模型、BiLSTM模型及变分模态分解与BiLSTM结合(VMD-BiLSTM)模型相比,AVMD-BiLSTM模型在预测精度上表现出显著优势,预测的平均绝对误差、均方根误差及平均绝对误差百分比降低幅度均超过20%。 展开更多
关键词 雷达散射截面 动态雷达散射截面预测 自适应变分模态分解 双向长短期记忆网络
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基于融合AVMD与改进灰狼优化ELM光纤陀螺温度补偿技术 认领 引用
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作者 仇海涛 赵浩阳 《半导体光电》 CAS 北大核心 2026年第4期630-637,共8页
为抑制环境温度引起的光纤陀螺零偏漂移与精度退化,提出一种融合自适应变分模态分解(AVMD)和改进灰狼算法优化极限学习机(IGWO-ELM)的温度误差补偿策略。该算法首先以最小化平均包络熵为准则,利用AVMD自适应确定变分模态分解的最优模态... 为抑制环境温度引起的光纤陀螺零偏漂移与精度退化,提出一种融合自适应变分模态分解(AVMD)和改进灰狼算法优化极限学习机(IGWO-ELM)的温度误差补偿策略。该算法首先以最小化平均包络熵为准则,利用AVMD自适应确定变分模态分解的最优模态数K和惩罚因子α,将温度信号分解为趋势项、波动项和噪声项,同时采用相同的分解参数对陀螺输出信号进行分解,实现信号的多尺度分离。然后,通过采集光纤陀螺在-40°C~+60°C范围内的温度与输出数据,分别对温度的趋势项和波动项建立IGWO-ELM子模型,用以预测陀螺输出中对应的分量;对噪声项则采用小波阈值去噪技术抑制高频干扰。最后,将三个分量的预测结果重构,得到最终的补偿输出。实验结果表明,该方法能有效分离温度变化中的多尺度特征,显著抑制零偏漂移,使补偿后陀螺的精度和稳定性均得到大幅提升。 展开更多
关键词 光纤陀螺 零偏漂移 自适应变分模态分解 改进灰狼算法 极限学习机
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A noise suppression method for interferometric fiber optic sensor based on ameliorated EFA and adaptive SVMD 认领 引用
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作者 PENG Meng-fan ZHOU Ci-ming +5 位作者 PAN Zhen JIANG Han LI Ao WANG Tian-yi LIU Han-jie FAN Dian 《中国光学（中英文）》 EI CAS CSCD 北大核心 2026年第2期395-406,共12页
Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th... Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems. 展开更多
关键词 interferometric fiber optic vibration sensor ellipse fitting algorithm successive variational mode decomposition noise suppression
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Optimized VMD algorithm for noise reduction of absorption spectra of CO2 认领 引用
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作者 Ru Zhang Junfen Wang +3 位作者 Xuemei Shi Mingliang Li Zhanmin Zhao Han Wang 《Optoelectronics Letters》 EI 2026年第1期23-28,共6页
The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds s... The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds significant importance in clinical practice.The measurements of EtCO2based on wavelength modulation-direct absorption spectroscopy(WM-DAS)had great advantages and the noise reduction of spectrum was very important.An optimized variational mode decomposition(VMD)algorithm improved by the dung beetle optimization algorithm and wavelet packet denoising algorithm was proposed to enhance the measurement accuracy of EtCO2concentration.The dung beetle optimization algorithm was used to obtain the optimal number of decomposition mode layers K and secondary penalty factorα.The optimal parameters were used to decompose the original transmitted light intensity signal with noise,and a series of intrinsic mode functions(IMFs)were obtained.Pearson correlation coefficient(R)was used to select the pure signal and the noisy signal,and the noisy signal was denoised by wavelet packet denoising algorithm.The transmitted light intensity signal was reconstructed by the signal processed by wavelet packet denoising algorithm and the pure signal.The results showed that the proposed algorithm could effectively remove the noise of signal of transmitted light intensity and improve the accuracy of concentration measurements of EtCO2. 展开更多
关键词 monitoring patients respiratory functionwhich Variational Mode Decomposition Wavelet Packet Denoising noise reduction spectrum end tidal carbon dioxide etco Dung Beetle Optimization Algorithm dung be lung ventilation
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基于QGA-AVMD的水电机组振动信号降噪方法 认领 引用
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作者 于姗 李珊珊 +2 位作者 李峥 杨帆 唐拥军 《中国工程机械学报》 北大核心 2026年第3期551-556,共6页
水电机组运行过程中,机械设备非平稳振动信号常受噪声干扰。针对传统变分模态分解(VMD)方法关键参数难以自适应选取,从而导致分解精度不足的问题,提出一种基于量子遗传算法(QGA)寻优的自适应变分模态分解(AVMD)方法。首先,为自适应寻找... 水电机组运行过程中,机械设备非平稳振动信号常受噪声干扰。针对传统变分模态分解(VMD)方法关键参数难以自适应选取,从而导致分解精度不足的问题,提出一种基于量子遗传算法(QGA)寻优的自适应变分模态分解(AVMD)方法。首先,为自适应寻找VMD最佳分解层数K和惩罚因子α,设定最小排列熵为QGA算法适应度函数;然后,通过设定相关系数阈值选取真实固有模态函数(IMF),舍弃含噪IMF;最后,重构剩余IMF信号,实现降噪目的。通过仿真信号和某水电站实测信号实际验证,与小波阈值降噪变分模态分解(VMD-WT)和小波阈值降噪经验模态分解(EMD-WT)方法相比,利用信噪比(SNR)、均方根误差(RMSE)及降噪抑制比(NRR)等评价指标对降噪效果进行综合评价,验证了该方法的有效性。 展开更多
关键词 量子遗传算法 变分模态分解 小波阈值降噪 水电机组
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Research on remaining useful life prediction of rolling bearings based on adaptive variational mode decomposition and dual-branch temporal neural network 认领 引用
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作者 Wuchu Tang Wenxin Dong +1 位作者 Jiawei Yang Guofu Wu 《Advances in Engineering Innovation》 2026年第7期12-30,共19页
To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction fr... To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction framework that combines Adaptive Variational Mode Decomposition(AVMD)with a SETCNBiGRU multi-head temporal attention mechanism for Remaining Useful Life(RUL)prediction.First,AVMD is used to decompose the bearing horizontal vibration signal into five Intrinsic Mode Functions(IMFs).Timedomain and frequency-domain statistics are extracted from each IMF and concatenated into a 115-dimensional degradation feature sequence.Subsequently,the model processes in parallel:a TCN-SENet branch extracts local temporal features and adaptively adjusts channel weights,while a BiGRU with multi-head temporal attention sub-network captures global bidirectional dependencies and critical degradation periods within the degradation sequence.Finally,the two types of features are fused,and the RUL prediction result is output.Experimental results demonstrate that the proposed model achieves an RMSE,MAE,and R2of 0.0582,0.0477,and 0.9483 respectively on the IEEE PHM 2012 dataset,and average values of 0.0780,0.0559,and 0.9133 on a self-built laboratory bearing dataset,indicating good prediction accuracy,robustness,and generalization ability. 展开更多
关键词 remaining useful life prediction adaptive variational mode decomposition temporal convolutional network bidirectional gated recurrent unit multi-head temporal attention mechanism
Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition 认领 引用
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作者 Canyu Mo Qianqiang Lin +1 位作者 Yuanduo Niu Haoran Du 《Journal of Electronic Research and Application》 2024年第1期7-15,共9页
In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decompositi... In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance. 展开更多
关键词 Micro-motion modulation signal Variational mode decomposition Genetic algorithm Adaptive optimization
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Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading 认领 引用 被引量：3
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作者 Yuze Li Shangrong Jiang +1 位作者 Xuerong Li Shouyang Wang 《Financial Innovation》 2022年第1期901-924,共24页
In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture chan... In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals. 展开更多
关键词 Bitcoin price Variational mode decomposition Deep learning Price forecasting Algorithmic trading
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噪声环境下基于AVMD和MMDE-SMFF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 赵昱宇 田翼 王雨潇 《振动与冲击》 EI CSCD 北大核心 2025年第21期289-301,共13页
针对现有深度学习模型强噪声环境下滚动轴承故障诊断性能不佳、难以有效揭示特征间复杂相关性等问题,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)、多尺度模态抑噪增强(multi-scale mode denoising ... 针对现有深度学习模型强噪声环境下滚动轴承故障诊断性能不佳、难以有效揭示特征间复杂相关性等问题,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)、多尺度模态抑噪增强(multi-scale mode denoising enhancement,MMDE)和空间映射特征融合(spatial mapping feature fusion,SMFF)的滚动轴承故障诊断方法。为避免模态混叠和端点问题,提出一种AVMD方法,通过斑马优化算法优化变分模态分解参数,以自适应提取各种工况下不同模态函数振荡特性成分;提出MMDE进行噪声抑制和模态特征提取,通过紧凑型多分支卷积层捕获多尺度模态的局部特征,利用通道动态降噪门控对故障特征进行自适应去噪增强;设计基于改进Transformer的SMFF进行特征融合,采用注意力机制捕获特征间相关性,通过卷积前馈网络学习序列非线性高维特征;结合AVMD、MMDE-SMFF与诊断决策器建立滚动轴承故障诊断模型。通过CWRU和XJTU-SY轴承故障数据集进行验证,结果表明,相较于现有智能故障诊断方法,所提方法抗噪性能良好,具有更高的准确性和可靠性。 展开更多
关键词 滚动轴承 故障诊断 Transformer 自适应变分模态分解(AVMD) 抗噪声 多尺度模态
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基于AVMD与Teager能量算子的风电机组故障诊断方法 认领 引用 被引量：4
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作者 时培明 伊思颖 +2 位作者 张慧超 范雅斐 韩东颖 《振动．测试与诊断》 EI CSCD 北大核心 2025年第2期390-397,418,共8页
为解决变分模态分解(variational mode decomposition,简称VMD)在噪声情况下提取风电机组故障特征时因参数设置的人为经验不足而带来的误差问题及耗费时间的问题,提出一种基于自适应变分模态分解(adaptive variational mode decompositi... 为解决变分模态分解(variational mode decomposition,简称VMD)在噪声情况下提取风电机组故障特征时因参数设置的人为经验不足而带来的误差问题及耗费时间的问题,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,简称AVMD)算法的风电机组故障诊断方法。首先,将包络熵-峭度-互信息准则(envelope entropy,kurtosis and mutual information,简称EKM)作为黏菌算法(slime mold algorithm,简称SMA)的适应度函数来寻找最优解,并按照最优解对故障信号进行分解;其次,计算每个固有模态函数分量(inherent modal function,简称IMF)的峭度和与原信号的互信息,选择具有故障特征的分量进行重构;最后,通过Teager能量算子解调来识别风电机组故障特征频率。仿真信号和实际风电机组故障信号表明,所提方法能够找到故障频率及其倍频,验证了其在风电机组故障诊断领域中的有效性。 展开更多
关键词 自适应变分模态分解 黏菌算法 包络熵-峭度-互信息准则 Teager能量算子
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AVMD及其在滚动轴承早期故障诊断中的应用 认领 引用 被引量：5
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作者 陆志杰 张子恒 +2 位作者 马晨波 张玉言 王志良 《振动工程学报》 EI CSCD 北大核心 2025年第12期3101-3112,共12页
针对滚动轴承早期故障特征难以准确提取的问题,提出了一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的早期故障诊断方法。该方法建立了一种无需先验知识的故障冲击度量指标(fault impact measure index,FI... 针对滚动轴承早期故障特征难以准确提取的问题,提出了一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的早期故障诊断方法。该方法建立了一种无需先验知识的故障冲击度量指标(fault impact measure index,FIMI),以指导多策略改进的鹦鹉算法(improved parrot optimizer,IPO)自适应获得变分模态分解(variational mode decomposition,VMD)的最优参数组合[K,α],实现故障信号的精准分解;基于FIMI最大化准则提取主故障特征模态分量;对其进行增强包络谱分析,从而识别故障类型。仿真信号和试验数据证实了该方法在滚动轴承早期故障诊断方面的有效性,并展示了其相对于现有技术方法的优越性。 展开更多
关键词 滚动轴承 早期故障 自适应变分模态分解 故障冲击度量指标 鹦鹉算法
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A Combined Denoising Method of Adaptive VMD and Wavelet Threshold for Gear Health Monitoring 认领 引用 被引量：1
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作者 Guangfei Jia Jinqiu Yang Hanwen Liang 《Structural Durability & Health Monitoring》 EI 2025年第4期1057-1072,共16页
Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key inno... Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis. 展开更多
关键词 Improve horned lizard optimization algorithm variational mode decomposition wavelet threshold inertial weight secondary noise reduction structural health monitoring
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A Remark on Adaptive Decomposition for Nonlinear Time-frequency Analysis 认领 引用
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作者 LIU XU WANG HAI-NA Ma Fu-ming 《Communications in Mathematical Research》 CSCD 2016年第4期319-324,共6页
In recent study the bank of real square integrable functions that have nonlinear phases and admit a well-behaved Hilbert transform has been constructed for adaptive representation of nonlinear signals. We first show i... In recent study the bank of real square integrable functions that have nonlinear phases and admit a well-behaved Hilbert transform has been constructed for adaptive representation of nonlinear signals. We first show in this paper that the available basic functions are adequate for establishing an ideal adaptive decomposition algorithm. However, we also point out that the best approximation algorithm, which is a common strategy in decomposing a function into a sum of functions in a prescribed class of basis functions, should not be considered as a candidate for the ideal algorithm. 展开更多
关键词 Hilbert transform empirical mode decomposition adaptive decompo-sition algorithm best approximation
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基于优化VMD二次分解的短期电力负荷预测 认领 引用 被引量：2
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作者 蒋建东 韩文轩 +3 位作者 赵云飞 燕跃豪 鲍薇 刘晓辉 《郑州大学学报（工学版）》 CAS 北大核心 2026年第1期124-130,共7页
针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模... 针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模态分解对配变负荷数据进行二次分解;再次,将两次分解得到的子序列输入时间卷积网络模型中进行预测;最后,将各子序列的预测结果叠加,得到最终的负荷预测结果。在郑州市某台区配变负荷数据上进行仿真分析,与传统时间卷积网络模型相比,所提模型MAE、MAPE和RMSE分别减少了64.29%,9.66百分点和59.00%。实验结果表明,所提组合预测模型具有更好的预测效果和更高的预测精度。 展开更多
关键词 二次分解 负荷预测 完全自适应噪声集合经验模态分解 变分模态分解 时间卷积网络
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基于RIME-VMD联合小波阈值的爆破振动信号去噪方法 认领 引用 被引量：3
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作者 王薇 程忠耀 +1 位作者 向延念 宋良俊 《铁道科学与工程学报》 EI CAS CSCD 北大核心 2026年第1期465-479,共15页
随着现代化建设的加速推进,邻近既有建筑的爆破作业日益增多,监测和分析爆破引起的振动对结构安全的评估至关重要。然而,爆破振动信号的非线性特性和复杂的环境因素干扰使得从实测信号中提取有效信号成分难度较大,给后续的信号分析造成... 随着现代化建设的加速推进,邻近既有建筑的爆破作业日益增多,监测和分析爆破引起的振动对结构安全的评估至关重要。然而,爆破振动信号的非线性特性和复杂的环境因素干扰使得从实测信号中提取有效信号成分难度较大,给后续的信号分析造成了较大影响。为提高爆破振动信号的降噪精度,将雾凇优化算法(RIME)、变分模态分解(VMD)和小波阈值进行融合,形成一种爆破振动信号联合去噪方法。该方法首先通过雾凇优化算法对VMD关键参数进行优化,然后通过优化后的VMD对振动信号进行自适应分解,剔除方差贡献率较低的分量,再采用小波阈值对筛选后的分量进行降噪处理,最终重构得到去噪后的信号。对该方法的降噪效果进行仿真分析和实际工程验证,结果表明:在仿真信号分析中,经RIME-VMD联合小波阈值的降噪方法去噪后的信号与无噪声的纯净信号相比,形状与特征高度吻合,且信噪比(SNR)和均方根误差(RMSE)等去噪指标优于EMD、小波阈值、EMD联合小波阈值等常用去噪方法;经工程实际案例验证,该方法能够在极大保留原信号基本特征的前提下,有效去除爆破振动信号中的高频噪声,降噪后信号更加符合爆破振动信号的主频范围,且具有比EMD、小波阈值、EMD联合小波阈值等常用去噪方法更好的去噪效果。该研究成果对爆破振动信号的降噪处理具有参考意义。 展开更多
关键词 爆破振动 信号处理 联合降噪 雾凇优化算法 变分模态分解 小波阈值去噪
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结合变分模态分解与小波阈值的微震去噪方法 认领 引用 被引量：1
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作者 姚振静 陈家豪 +3 位作者 郝蕾 秦岚 栗文哲 段丽 《石油地球物理勘探》 EI CSCD 北大核心 2026年第1期63-72,共10页
微地震监测技术在非常规油气藏开发、矿井灾害监控等领域具有重要应用价值,但其信号易受噪声干扰,导致信噪比低,严重影响后续震源定位及机制反演准确性。针对传统去噪方法,比如互补集合经验模态分解(Complete Ensemble Empirical Mode D... 微地震监测技术在非常规油气藏开发、矿井灾害监控等领域具有重要应用价值,但其信号易受噪声干扰,导致信噪比低,严重影响后续震源定位及机制反演准确性。针对传统去噪方法,比如互补集合经验模态分解(Complete Ensemble Empirical Mode Decomposition,CEEMD)法和小波模极大值(Wavelet Modulus Maxima,WMM)法,在处理非平稳微震信号时存在的局限性,文中提出融合麻雀优化变分模态分解(Variational Mode Decomposition,VMD)与自适应小波阈值的微震去噪方法,简称SSA-VMD-CC-WT法。首先,利用麻雀优化算法(Sparrow Search Algorithm,SSA)确定VMD算法的关键参数;其次,通过互相关系数(Cross-Correlation Coefficient,CC)筛选有效模态分量,抑制噪声;最后,采用自适应小波阈值(Wavelet Thresholding,WT)法对有效分量二次去噪,降低信号失真。仿真测试表明,SSA-VMD-CC-WT法在强噪声背景下较CEEMD法及WMM法能更精准地分离噪声与有效信号;实际微震资料处理结果显示,该方法在显著压制低频和高频噪声的同时,有效保护了微弱震源信息,提升了数据的可解释性和信噪比。与此同时,相较传统遗传优化算法(Genetic Algorithm,GA),SSA的优化效率更高。 展开更多
关键词 微震信号去噪 麻雀优化算法 变分模态分解 互相关系数 自适应小波阈值法
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基于BWO-VMD-ISSA-LSTM的交通运输业碳排放预测研究 认领 引用 被引量：2
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作者 王庆荣 王俊杰 +3 位作者 朱昌锋 张金鹏 何润田 刘心康 《计算机工程与应用》 EI CSCD 北大核心 2026年第10期376-388,共13页
针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及L... 针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及LSTM的碳排放预测模型。引入最大互信息系数(maximum information coefficient,MIC)提取影响碳排放量的主要因素,剔除冗余特征。利用BWO对VMD的分解模态数和惩罚因子寻优,增强两参数间的协调性,进而将碳排放量分解为不同频率的模态分量和剩余分量,削弱原始碳排放量的非线性;通过在LSTM的输入端嵌入特征注意力机制(feature attention mechanism,FA),突出关键输入特征。引入基于改进Tent混沌映射、动态步长权重因子、混合变异算子和精英反向学习的混合策略改进SSA算法,避免算法陷入局部最优。对各模态分量分别构建基于ISSA-LSTM的预测模型,并对预测结果进行集成。采用中国交通运输业1990—2019年的碳排放数据对模型进行验证,结果表明,所提模型较最优对比模型的RMSE、MAE和MAPE分别降低了26.28%、31.64%和33.32%,能够有效地预测交通运输业碳排放量。 展开更多
关键词 交通运输业 碳排放预测 白鲸优化算法 变分模态分解 麻雀搜索算法 最大互信息系数
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基于CEEMDAN-混合算法-LSTM的区域地下水埋深预测模型 认领 引用 被引量：2
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作者 刘东 覃胡涛 +2 位作者 张祥敏 张亮亮 齐晓晨 《农业机械学报》 EI CAS CSCD 北大核心 2026年第6期320-328,共9页
为提高区域地下水埋深预测精度,提出一种CEEMDAN-混合算法-LSTM预测模型。基于完全自适应噪声集成经验模态分解法(CEEMDAN),将建三江分公司下辖15个农场的地下水埋深数据分解为5个模态分量,从而有效降低输入数据的复杂性。同时,将红狐... 为提高区域地下水埋深预测精度,提出一种CEEMDAN-混合算法-LSTM预测模型。基于完全自适应噪声集成经验模态分解法(CEEMDAN),将建三江分公司下辖15个农场的地下水埋深数据分解为5个模态分量,从而有效降低输入数据的复杂性。同时,将红狐优化算法(RFO)和鲸鱼优化算法(WOA)结合的混合算法,用于优化长短记忆神经网络(LSTM)模型关键参数,包括时间步长、隐藏单元数、批量大小和学习率,以进一步提高模型预测精度。将月降水量和水田井灌水量作为LSTM模型输入因子,分别对5个模态分量进行预测,最终通过累加各分量预测值得到地下水埋深预测值。结果表明:与反向传播神经网络(BP)模型和循环神经网络(RNN)模型相比,CEEMDAN-混合算法-LSTM模型均方根误差(RMSE)降低43%以上,决定系数R2和纳什效率系数(NSE)均提升超18%;预测结果表明,2023—2027年建三江分公司整体地下水埋深变化幅度达6.22%,其中南部农场地下水埋深普遍大于北部农场。 展开更多
关键词 地下水埋深 CEEMDAN 混合算法 LSTM 建三江分公司
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