This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous ...This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.展开更多
The end tidal carbon dioxide(EtCO_(2))is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO_(2)holds s...The end tidal carbon dioxide(EtCO_(2))is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO_(2)holds significant importance in clinical practice.The measurements of EtCO_(2)based 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 EtCO_(2)concentration.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 EtCO_(2).展开更多
Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extrac...Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.展开更多
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
An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Mult...An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.展开更多
The general corrosion and local corrosion of Q235 steel were tested by acoustic emission （AE） detecting system under 6% FeCl3.6H2O solution to effectively detect the corrosion acoustic emission signal from complex b...The general corrosion and local corrosion of Q235 steel were tested by acoustic emission （AE） detecting system under 6% FeCl3.6H2O solution to effectively detect the corrosion acoustic emission signal from complex background noise. The short-time fractal dimension and discrete fractional cosine transform methods are combined to reduce noise. The input SNR is 0-15 dB while corrosion acoustic emission signals being added with white noise, color noise and pink noise respectively. The results show that the output signal-to-noise ratio is improved by up to 8 dB compared with discrete cosine transform and discrete fractional cosine transform. The above-mentioned noise reduction method is of significance for the identification of corrosion induced acoustic emission signals and the evaluation of the metal remaining life.展开更多
为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和...为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis,LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征,使得具有不同分布特征的数据在可视化二维空间中显著分离;最后,采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对二维空间中的数据进行聚类。结果表明,与主成分分析(principal component analysis,PCA)算法、局部线性嵌入(locally linear embedding,LLE)算法和原t-SNE算法相比,所提方法能够对各种复杂工况数据进行可视化分离聚类,并对异常数据进行识别和剔除。展开更多
基金This research was funded by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019 /0000867by the Ministry of Education of the Czech Republic, Project No. SP2021/32.
文摘This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.
基金supported by the Key Research and Development Program of Hebei Province(No.22375415D).
文摘The end tidal carbon dioxide(EtCO_(2))is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO_(2)holds significant importance in clinical practice.The measurements of EtCO_(2)based 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 EtCO_(2)concentration.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 EtCO_(2).
文摘Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.
基金supported by Central Guidance on Local Science and Technology Development Fund of Hebei Province(Grant No.226Z1906G)funded by Science Research Project of Hebei Education Department(CXY2024038)+1 种基金funded by Basic Research Project of Shijiazhuang University in Hebei Province(241791157A)National Natural Science Foundation of China(52206224).
文摘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.
基金supported by the National Natural Science Foundation of China(No.61177018)the Program for New Century Excellent Talents in University(No.NECT-11-0596)+1 种基金the Key Program of Beijing Sci-ence and Technology Plan(No.D121100004812001)Beijing Nova Program(No.2011066)
文摘An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.
文摘The general corrosion and local corrosion of Q235 steel were tested by acoustic emission （AE） detecting system under 6% FeCl3.6H2O solution to effectively detect the corrosion acoustic emission signal from complex background noise. The short-time fractal dimension and discrete fractional cosine transform methods are combined to reduce noise. The input SNR is 0-15 dB while corrosion acoustic emission signals being added with white noise, color noise and pink noise respectively. The results show that the output signal-to-noise ratio is improved by up to 8 dB compared with discrete cosine transform and discrete fractional cosine transform. The above-mentioned noise reduction method is of significance for the identification of corrosion induced acoustic emission signals and the evaluation of the metal remaining life.
文摘为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis,LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征,使得具有不同分布特征的数据在可视化二维空间中显著分离;最后,采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对二维空间中的数据进行聚类。结果表明,与主成分分析(principal component analysis,PCA)算法、局部线性嵌入(locally linear embedding,LLE)算法和原t-SNE算法相比,所提方法能够对各种复杂工况数据进行可视化分离聚类,并对异常数据进行识别和剔除。
