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Neutron-gamma discrimination method based on blind source separation and machine learning 被引量:5
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作者 Hanan Arahmane El-Mehdi Hamzaoui +1 位作者 Yann Ben Maissa Rajaa Cherkaoui El Moursli 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2021年第2期70-80,共11页
The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimina... The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20). 展开更多
关键词 blind source separation Nonnegative tensor factorization(NTF) Support vector machines(SVM) Continuous wavelets transform(CWT) Otsu thresholding method
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Source Recovery in Underdetermined Blind Source Separation Based on Artificial Neural Network 被引量:3
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作者 Weihong Fu Bin Nong +2 位作者 Xinbiao Zhou Jun Liu Changle Li 《China Communications》 SCIE CSCD 2018年第1期140-154,共15页
We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source sep... We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source separation, the artificial neural network with single-layer perceptron is introduced into the proposed algorithm. Source signals are regarded as the weight vector of single-layer perceptron, and approximate ι~0-norm is taken into account for output error decision rule of the perceptron, which leads to the sparse recovery. Then the procedure of source recovery is adjusting the weight vector of the perceptron. What's more, the optimal learning factor is calculated and a descent sequence of smoothed parameter is used during iteration, which improves the performance and significantly decreases computational complexity of the proposed algorithm. The simulation results reveal that the algorithm proposed can recover the source signal with high precision, while it requires lower computational cost. 展开更多
关键词 underdetermined blind source separation ι~0-norm artificial neural network sparse reconstruction
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Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA 被引量:1
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作者 游荣义 陈忠 《Chinese Physics B》 SCIE EI CAS CSCD 2005年第11期2176-2180,共5页
Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by di... Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals. 展开更多
关键词 blind source separation ELECTROENCEPHALOGRAM wavelet transform independent component analysis
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New Wavelet Threshold Denoising Method in Noisy Blind Source Separation 被引量:1
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作者 Xuan-Sen He Tian-Jiao Zhao 《Journal of Electronic Science and Technology》 CAS 2010年第4期356-361,共6页
In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural... In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural gradient algorithm based on bias removal technology to estimate the demixing matrix under noisy environment. Then the discrete wavelet transform technology is applied to the separated signals to further remove noise. In order to improve the separation effect, this paper analyzes the deficiency of hard threshold and soft threshold, and proposes a new wavelet threshold function based on the wavelet decomposition and reconfiguration. The simulations have verified that this method improves the signal noise ratio (SNR) of the separation results and the separation precision. 展开更多
关键词 Bias removal blind source separation gradient algorithm wavelet threshold denoising.
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Blind Source Separation based on Time-Frequency Morphological Characteristics for Rigid Acoustic Scattering by Underwater Objects 被引量:1
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作者 Yang Yang Xiukun Li 《Journal of Marine Science and Application》 CSCD 2016年第2期201-207,共7页
Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have t... Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation (BSS) can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution (WVD) observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. A simulation experimental has been used to analyze the feasibility of the new method, with changing the pulse width of the transmitted signal, the relative amplitude and the time delay parameter. And simulation results show that the new method can not only separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects. 展开更多
关键词 underwater object highlight structure rigid scattering components image morphology TIME-FREQUENCY blind source separation
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Maximum Likelihood Blind Separation of Convolutively Mixed Discrete Sources
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作者 辜方林 张杭 朱德生 《China Communications》 SCIE CSCD 2013年第6期60-67,共8页
In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation proce... In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation procedure of the EM algorithm with a less computational load,the algorithm named Iterative Maximum Likelihood algorithm(IML) is proposed to calculate the likelihood and recover the source signals.An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter.Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources.Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures.Furthermore,the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters. 展开更多
关键词 blind source separation convolutive mixture EM Finite Alphabet
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A blind source separation algorithm based on negentropy and signal noise ratio
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作者 万俊 《Journal of Chongqing University》 CAS 2012年第3期134-140,共7页
A novel blind source separation (BSS) algorithm based on the combination of negentropy and signal noise ratio (SNR) is presented to solve the deficiency of the traditional independent component analysis (ICA) al... A novel blind source separation (BSS) algorithm based on the combination of negentropy and signal noise ratio (SNR) is presented to solve the deficiency of the traditional independent component analysis (ICA) algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely. 展开更多
关键词 blind source separation independent component analysis NEGENTROPY signal noise ratio
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For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology 被引量:1
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作者 Chengjie Li Lidong Zhu Zhen Zhang 《China Communications》 SCIE CSCD 2024年第2期85-95,共11页
In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signal... In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system. 展开更多
关键词 blind source separation greedy optimization algorithm interference sensing LEO satellite communication networks signal reconstruction
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Initialization for NMF-Based Audio Source Separation Using Priors on Encoding Vectors 被引量:2
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作者 Jaeuk Byun Jong Won Shin 《China Communications》 SCIE CSCD 2019年第9期177-186,共10页
Nonnegative matrix factorization(NMF)has shown good performances on blind audio source separation(BASS).While the NMF analysis is a non-convex optimization problem when both the basis and encoding matrices need to be ... Nonnegative matrix factorization(NMF)has shown good performances on blind audio source separation(BASS).While the NMF analysis is a non-convex optimization problem when both the basis and encoding matrices need to be estimated simultaneously,the source separation step of the NMF-based BASS with a fixed basis matrix has been considered convex.However,because the basis matrix for the BASS is typically constructed by concatenating the basis matrices trained with individual source signals,the subspace spanned by the basis vectors for one source may overlap with that for other sources.In this paper,we have shown that the resulting encoding vector is not unique when the subspaces spanned by basis vectors for the sources overlap,which implies that the initialization of the encoding vector in the source separation stage is not trivial.Furthermore,we propose a novel method to initialize the encoding vector for the separation step based on the prior model of the encoding vector.Experimental results showed that the proposed method outperformed the uniform random initialization by 1.09 and 2.21dB in the source-to-distortion ratio,and 0.20 and 0.23 in PESQ scores for supervised and semi-supervised cases,respectively. 展开更多
关键词 blind AUDIO source separation NONNEGATIVE matrix FACTORIZATION speech enhancement
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Robust Blind Separation for MIMO Systems against Channel Mismatch Using Second-Order Cone Programming 被引量:1
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作者 Zhongqiang Luo Chengjie Li Lidong Zhu 《China Communications》 SCIE CSCD 2017年第6期168-178,共11页
To improve the deteriorated capacity gain and source recovery performance due to channel mismatch problem,this paper reports a research about blind separation method against channel mismatch in multiple-input multiple... To improve the deteriorated capacity gain and source recovery performance due to channel mismatch problem,this paper reports a research about blind separation method against channel mismatch in multiple-input multiple-output(MIMO) systems.The channel mismatch problem can be described as a channel with bounded fluctuant errors due to channel distortion or channel estimation errors.The problem of blind signal separation/extraction with channel mismatch is formulated as a cost function of blind source separation(BSS) subject to the second-order cone constraint,which can be called as second-order cone programing optimization problem.Then the resulting cost function is solved by approximate negentropy maximization using quasi-Newton iterative methods for blind separation/extraction source signals.Theoretical analysis demonstrates that the proposed algorithm has low computational complexity and improved performance advantages.Simulation results verify that the capacity gain and bit error rate(BER) performance of the proposed blind separation method is superior to those of the existing methods in MIMO systems with channel mismatch problem. 展开更多
关键词 multiple-input multiple-output channel mismatch second-order cone programming blind source separation independent component analysis
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多谱自适应小波和盲源分离耦合的生理信号降噪方法
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作者 王振宇 向泽锐 +2 位作者 支锦亦 丁铁成 邹瑞 《北京航空航天大学学报》 北大核心 2025年第3期910-921,共12页
为提高生理信号的质量和可靠性,将盲源分离和小波阈值方法进行耦合研究,提出了多谱自适应小波信号增强方法并与改进的盲源分离方法相结合进行降噪处理。为评估所提方法的有效性,使用小波变换中软阈值、硬阈值、自适应阈值3种方法计算信... 为提高生理信号的质量和可靠性,将盲源分离和小波阈值方法进行耦合研究,提出了多谱自适应小波信号增强方法并与改进的盲源分离方法相结合进行降噪处理。为评估所提方法的有效性,使用小波变换中软阈值、硬阈值、自适应阈值3种方法计算信噪比(SNR)和均方根误差(RMSE)。结果表明:所提方法在软阈值下具有较强的适用性,增强后的信号软阈值相比硬阈值,SNR提升约44.2%,RMSE下降约28.8%,处理时间减少约1.4%。软阈值相比自适应阈值,SNR提升约706%,RMSE下降约16.7%,处理时间减少约3.0%。为对比软阈值下各参数差异,使用软阈值对原始信号、加噪信号和增强信号进行对比分析及归一化处理。结果显示增强后的信号具有较好的SNR、较低的RMSE和较短的处理时间,软阈值下增强后的信号与原始信号相比,SNR提升约0.12%,RMSE下降约2.5%,处理时间减少约3.9%,进一步验证了所提方法的有效性,并提高了信号质量。 展开更多
关键词 多谱自适应小波 盲源分离 小波变换 降噪方法 生理信号
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基于盲源分离的多人呼吸信号检测方法
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作者 杨轩 王子颖 +2 位作者 张力 赵恒 洪弘 《雷达学报(中英文)》 北大核心 2025年第1期117-134,共18页
近年来,人们越来越关注多人环境下的呼吸监测,以及如何同时监测多人的健康状态。在多人呼吸检测的算法中,盲源分离算法因其无需先验信息并且对硬件性能依赖性较小而备受研究者关注。然而,在多人呼吸监测场景中,目前的盲源分离算法通常... 近年来,人们越来越关注多人环境下的呼吸监测,以及如何同时监测多人的健康状态。在多人呼吸检测的算法中,盲源分离算法因其无需先验信息并且对硬件性能依赖性较小而备受研究者关注。然而,在多人呼吸监测场景中,目前的盲源分离算法通常将相位信号作为源信号进行分离,该文引入FMCW雷达下距离维信号和相位信号的对比,推导出相位信号作为源信号存在近似误差,并通过仿真验证距离维信号作为源信号时分离效果更好。另外,该文提出了基于非圆复数独立成分分析的多人呼吸信号分离算法,分析了不同呼吸信号参数对分离效果的影响,仿真和实测实验表明,所提出的方法适用于天线个数不小于目标个数时多人呼吸信号的检测,并且在目标角度差为9.46°时,也能够准确分离呼吸信号。 展开更多
关键词 非接触呼吸检测 FMCW雷达 多人呼吸检测 盲源分离 复数独立成分分析
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基于EEMD-SCBSS的岩石声发射信号去噪方法 被引量:14
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作者 赵奎 杨道学 +4 位作者 曾鹏 王晓军 钟文 龚囱 闫雷 《振动与冲击》 EI CSCD 北大核心 2021年第5期179-185,210,共8页
针对岩石声发射(AE)信号的低信噪比、随机性强、非平稳性等特点,提出了一种基于总体经验模态(EEMD)及单通道盲源分离(SCBSS)的AE信号滤波方法。将含有背景噪声的AE信号进行EEMD分解,得到一系列按频率从高到低排列的本征模函数(IMF);提... 针对岩石声发射(AE)信号的低信噪比、随机性强、非平稳性等特点,提出了一种基于总体经验模态(EEMD)及单通道盲源分离(SCBSS)的AE信号滤波方法。将含有背景噪声的AE信号进行EEMD分解,得到一系列按频率从高到低排列的本征模函数(IMF);提取高频背景噪声信号与观测信号构建虚拟多通道观测信号;利用快速不动点优化算法(FastICA)对构建的虚拟多通道观测信号进行盲源分离(BSS),进而得到滤波后的AE信号。通过构造含噪声AE信号进行数值仿真实验及实测数据分析,将基于EEMD及SCBSS滤波方法与小波阈值滤波方法进行比较。实验结果表明:小波阈值滤波方法会导致滤波后的AE信号频域信息失真,影响滤波后的AE信号上升时间,能量等参数识别;该方法可以对含噪声AE信号进行有效地滤波处理,能够较好地滤除AE信号中的非平稳随机噪声,并且能够保护滤波后的AE信号频域信息。 展开更多
关键词 岩石声发射信号 去噪 总体经验模态 单通道盲源分离
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基于改进二阶统计量BSS算法的风力机主轴承故障诊断研究 被引量:3
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作者 刘欢 王健 +3 位作者 郭烁 张琳琳 李金凤 王庆辉 《可再生能源》 CAS 北大核心 2016年第3期421-426,共6页
针对大型风力机主轴承易发生故障且特征信号难以提取的问题和传统盲分离算法计算量大、收敛性较差的缺点,提出一种改进二阶统计量的盲源分离算法;利用信号的非平稳性,将传感器数据分成不重叠的时间窗,用广义时滞协方差矩阵代替标准协方... 针对大型风力机主轴承易发生故障且特征信号难以提取的问题和传统盲分离算法计算量大、收敛性较差的缺点,提出一种改进二阶统计量的盲源分离算法;利用信号的非平稳性,将传感器数据分成不重叠的时间窗,用广义时滞协方差矩阵代替标准协方差矩阵,然后估计每个窗内的时滞协方差矩阵平均值来提高算法的稳健性和精确度。且将该算法成功应用于某风场大型风力机主轴承故障信号的提取中。分析结果表明,该算法可有效分离大型风力机主轴承与其他部件的振动信号,与其他算法相比具有分离精度高、可靠性好等优点,对风力机主轴承的故障诊断十分有效。 展开更多
关键词 风力机 盲源分离 主轴承 二阶统计量 故障诊断
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联合BSS和FRFT的雷达抗主瓣干扰新方法 被引量:4
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作者 王瑜 李小波 +1 位作者 周青松 董玮 《现代雷达》 CSCD 北大核心 2016年第7期72-77,共6页
有源压制干扰从雷达天线的主瓣进入雷达内部,干扰信号很强时,将严重影响雷达的检测性能。传统的旁瓣消隐、旁瓣相消以及低副瓣天线等技术难以奏效。文中分析了盲源分离技术应用雷达主瓣抗干扰时盲源分离的信号存在幅度、相位的不确定性... 有源压制干扰从雷达天线的主瓣进入雷达内部,干扰信号很强时,将严重影响雷达的检测性能。传统的旁瓣消隐、旁瓣相消以及低副瓣天线等技术难以奏效。文中分析了盲源分离技术应用雷达主瓣抗干扰时盲源分离的信号存在幅度、相位的不确定性,提出了一种联合盲源分离和分数阶傅里叶变换的雷达抗主瓣干扰的新方法。并给出新方法与传统脉冲压缩方法主瓣干扰抑制的仿真结果,仿真结果表明了在强噪声压制干扰环境中,新方法具有良好的抗主瓣干扰的性能。 展开更多
关键词 盲源分离 分数阶傅里叶变换 线性调频信号 脉冲压缩
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BSS和SVM在齿轮箱智能故障诊断中的应用 被引量:1
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作者 李凤兰 葛玉珉 +1 位作者 马维金 万晓飞 《煤矿机械》 北大核心 2014年第1期234-236,共3页
齿轮箱振动信号中蕴藏着齿轮箱运行状态的所有信息。阐述了盲源分离(BSS)的基本模型及其算法,模拟了变速箱的4种常见故障,对实验室中获得的振动信号进行BSS预处理,提取小波包能量向量之后应用支持向量机算法进行智能故障分类,故障识别... 齿轮箱振动信号中蕴藏着齿轮箱运行状态的所有信息。阐述了盲源分离(BSS)的基本模型及其算法,模拟了变速箱的4种常见故障,对实验室中获得的振动信号进行BSS预处理,提取小波包能量向量之后应用支持向量机算法进行智能故障分类,故障识别率达到85%以上。 展开更多
关键词 齿轮箱 特征提取 盲源分离 支持向量机
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基于EEPS-BSS算法的脑深部诱发电位提取
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作者 熊慧 杨雪 +3 位作者 李刚 林凌 张旺明 徐如祥 《天津工业大学学报》 CAS 北大核心 2013年第4期55-59,70,共6页
脑深部诱发电位对探究神经刺激器治疗疾病的工作机理起着重要作用.为解决临床治疗中采集电极数少于源信号数而产生的欠定盲源分离问题,提出了一种在欠定情况下不需利用先验知识的改进盲源分离法.针对单路脑深部诱发电位的提取,仅利用经... 脑深部诱发电位对探究神经刺激器治疗疾病的工作机理起着重要作用.为解决临床治疗中采集电极数少于源信号数而产生的欠定盲源分离问题,提出了一种在欠定情况下不需利用先验知识的改进盲源分离法.针对单路脑深部诱发电位的提取,仅利用经验模态分解对观测信号进行分层处理,以一定的规则重构信号来扩展观测信号数目,进而进行分离.仿真与实测数据表明:该方法能实现从低信噪比的单路信号中有效提取微弱诱发电位,分离后各输出信号间的互相关系数较分离前大幅下降,从而证实了该算法提取诱发电位的有效性. 展开更多
关键词 脑深部诱发电位 欠定盲源分离 经验模态分解 低信噪比
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基于SVMD-IDBOICA的单通道旋翼声信号分离研究
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作者 徐超逸 刘正江 《直升机技术》 2025年第1期24-30,共7页
针对在风洞试验室采集到的旋翼声信号会夹杂电机、减速器等中高频干扰信号的问题,开展单通道旋翼声信号分离研究。为在保留旋翼本征气动噪声信息的同时剔除中高频干扰信号,提出连续变分模态分解(SVMD)与基于改进蜣螂算法的独立分量分析(... 针对在风洞试验室采集到的旋翼声信号会夹杂电机、减速器等中高频干扰信号的问题,开展单通道旋翼声信号分离研究。为在保留旋翼本征气动噪声信息的同时剔除中高频干扰信号,提出连续变分模态分解(SVMD)与基于改进蜣螂算法的独立分量分析(IDBOICA)相结合的盲源分离方法。首先为提高蜣螂算法的寻优性能与收敛速度,利用Logistic-Tent混沌映射与t-分布扰动变异来改进算法;然后以峭度为目标函数,将改进蜣螂算法应用于独立分量分析(ICA)的优化算法中,以改善ICA的分离性能;最后联合SVMD和IDBOICA算法(SVMD-IDBOICA)对含噪声信号进行分离。仿真试验结果表明,使用该算法分离后的目标本征信号与仿真信号相似系数在0.96以上,信噪比明显提升,且效果优于SVMD-FastICA和SVMD-DBOICA。风洞试验旋翼声信号分析应用表明,SVMD-IDBOICA分离算法能够较好地分离并剔除中高频干扰信号,进一步验证了算法的有效性。 展开更多
关键词 连续变分模态分解 改进蜣螂优化算法 旋翼声信号 盲源分离
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基于BSS的含噪声机械振动信号分离研究 被引量:2
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作者 马超 吕志强 章林柯 《噪声与振动控制》 CSCD 北大核心 2010年第6期161-164,174,共5页
水下航行器的噪声源识别面临的两个问题(:1)无法获得振源信号(,2)测得振动信号有环境噪声影响且振源之间相互耦合。将环境噪声作为一个独立的噪声源,给出瞬时混合信号的盲源分离(BSS)数学模型;利用基于二阶统计特性的两次去相关盲源分... 水下航行器的噪声源识别面临的两个问题(:1)无法获得振源信号(,2)测得振动信号有环境噪声影响且振源之间相互耦合。将环境噪声作为一个独立的噪声源,给出瞬时混合信号的盲源分离(BSS)数学模型;利用基于二阶统计特性的两次去相关盲源分离算法,对机械振动加白噪声的混合信号和水池试验实测混合信号进行分离;通过试验验证两次去相关盲源分离方法可以用来解决上述问题。 展开更多
关键词 振动与波 水下航行器 噪声源识别 盲源分离 两次去相关
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基于FSS-kernel BSS方法的机械故障诊断 被引量:2
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作者 杨彦龙 程伟 常洪振 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2012年第11期1557-1561,共5页
机械设备发生故障时,故障特征的提取是很重要的.为了从观测信号中分离出不同的故障特征源信号,并根据分离信号准确地进行故障诊断,从观测信号样本出发,提出了基于有限支持样本核函数的盲源分离(FSS-kernel BSS)方法.此方法利用有限的观... 机械设备发生故障时,故障特征的提取是很重要的.为了从观测信号中分离出不同的故障特征源信号,并根据分离信号准确地进行故障诊断,从观测信号样本出发,提出了基于有限支持样本核函数的盲源分离(FSS-kernel BSS)方法.此方法利用有限的观测样本估计信号的概率分布,得到了评价函数,具有很好的自适应能力.仿真试验结果表明:此方法能成功地分离超、亚高斯混合信号,与其他盲源分离方法相比,此方法具有更好的分离性能.将该方法用于转子不平衡和支座松动的复合故障信号的盲分离,分离出了各复合故障的主要频谱.分离结果表明:此方法应用于机械设备复合故障诊断中是可行的. 展开更多
关键词 故障诊断 盲源分离 有限支持样本 核函数
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