基于EEMD-MRA方法的LIBS信号处理研究

LIBS技术优点众多,但由于光谱噪声干扰和基体效应等因素,影响了分析的准确度^[1]; EEMD方法能清晰的将LIBS信号中的不同特征成分自适应的分解开来。MRA方法能够补偿元素信号之间的互干扰,可进一步提高LIBS信号的准确性。通过自行搭建的测试系统获得了标准样品的原始信号,使用EEMD-MRA方法进行处理后,元素浓度曲线的决定系数R²得到了极大的提高,大大的提高数据的准确性,为LIBS信号的处理提供了一种全新的方式。     

基于三维荧光光谱法和PARAFAC对多环芳烃定性定量分析

三维荧光光谱法在研究多环芳烃(PAHs)类物质的荧光信息时起到了重要作用。多环芳烃类物质具有致癌性,难降解性,多由尾气排放,垃圾焚烧产生,危害着人类健康及环境,因此人们不断探索对多环芳烃检测的方法。实验选取多环芳烃中的苊和萘作为检测物质,利用FLS920荧光光谱仪,为避免荧光光谱仪本身产生的瑞利散射影响,设置起始的发射波长滞后激发波长40 nm,设置扫描的激发波长(λex)范围为:200~370 nm,发射波长(λem)范围为:240~390 nm,对多环芳烃进行荧光扫描获取荧光数据,采用三维荧光光谱技术结合平行因子算法对混合溶液中的苊和萘进行定性定量分析。实验选用的苊和萘均购于阿拉丁试剂官网,配制浓度为10 mg·L-1的一级储备液,再将一级储备液稀释,得到苊和萘浓度为0.5,1,1.5,2,2.5,3,3.5,4和4.5 mg·L-1的二级储备液,并将苊和萘进行混合。在进行光谱分析前需要对苊和萘的光谱进行预处理,采用空白扣除法扣除拉曼散射的影响,并采用集合经验模态分解(EEMD)消除干扰噪声。实验测得苊存在两个波峰,位于λex=298 nm,λem=324/338 nm处,萘存在一个波峰,位于λex=280 nm,λem=322 nm处。选用的PARAFAC算法对组分数的的选择很敏感,因此采用核一致诊断法预估组分数,估计值2和3的核一致值都在60%以上,分别对混合样品进行了2因子和3因子的PARAFAC分解,将分解后得到的激发发射光谱数据和各组分浓度数据进行归一化处理,并绘制光谱图,与归一化处理后的真实的激发发射光谱图和各组分浓度图进行对比。同时将PARAFAC得到的混合样本的预测浓度,通过计算回收率(R)和均方根误差(RMSEP)来判定定量分析的准确度。选择2因子时,各混合样品中苊和萘拟合度为95.7%和96.7%,平均回收率分别为101.8%和98.9%,均方根误差分别为0.0187和0.0316;选择3因子时,各混合样品中苊和萘拟合度为95.3%和95.8%,平均回收率分别为97%和102.5%,均方根误差分别为0.033和0.116,由三项指标可得选用2因子进行定性定量分析的效果明显好于选用3因子。分析实验结果表明,基于三维荧光光谱法和PARAFAC算法对混合样品进行定性定量分析,能够有效的判定混合样品的类别,同时能够成功的预测出混合样品的浓度。     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

EEMD修正爆破加速度零漂信号中的最优白噪声系数

为了有效降低振动信号误差以提高数据的可信度,先针对花岗岩爆破试验中的加速度零漂信号,使用集合经验模态分解(ensemble empirical mode decomposition,EEMD)与高低频处理相结合的方法进行修正;接着,根据信号冲击响应谱提出表征修正前后频域平均偏差幅度的修正指数;最后,基于频域和时域分别讨论了不同白噪声系数范围内信号的修正效果。分析表明:EEMD方法能够有效地消除爆破加速度信号的零漂现象,但对积分后速度信号的零漂趋势改善有限;随着白噪声系数增大,不同频段上修正指数均不同程度地增大,二者呈现幂指数关系;根据不同频段上的修正指数分析,可以确定不同零漂加速度信号对应的最优白噪声系数范围。本文提出的修正指数可为EEMD方法处理加速度零漂信号时白噪声系数的合理选取提供参考。     

基于荧光光谱法与深度信念网络的稻种发芽率检测方法研究

针对传统稻种发芽率检测效率低、精度差、专业化要求高等问题,通过荧光光谱法结合深度信念网络(DBN)建立稻种发芽率预测模型。首先,将连粳7号和武运粳均分别老化0~7 d后,以5 min为间隔在纯净水中分别浸泡5~30 min。然后用荧光光谱仪检测浸泡液的荧光光谱,光谱数据经中心化后用集合经验模态分解(EEMD)去噪,并通过主成分分析法提取441.5 nm的特征荧光波长。最后,利用偏最小二乘回归(PLSR),反向传播神经网络(BPNN),径向基函数神经网络(RBFNN)和深度信念网络(DBN)建立水稻种子发芽预测模型。比较后得出,DBN模型在少数据、弱信号情况下的预测精度最高,预测集相关系数Rp和均方根误差RMSEP最大可达0.979 2和0.101。同时,通过分析混合稻种荧光数据R_p的变化趋势,得到最佳浸泡时间为22.1 min,实际上,精确度超过0.95(R_p)需要5 min左右。研究结果表明,结合荧光光谱法和EEMD-DBN模型,非破坏性地预测水稻种子发芽率具有可行性和高准确性,并且适用于不同颜色和污染水平的水稻种子的检测。     

基于DE-LSSVM的滚动轴承故障诊断

针对滚动轴承故障诊断难以获得大量样本的问题以及LS-SVM 模型参数选择方法易陷入局部最优的缺点,提出了一种集合经验模态分解能量熵和差分进化算法（DE）优化最小二乘支持向量机相结合的轴承故障诊断方法。首先原始振动信号采用EEMD分解得到一组固有模态函数（IMF）,从有效本征模态函数IMF分量中提取的能量特征作为输入建立支持向量机,通过计算不同振动信号的能量熵值大小来判断轴承的故障损伤程度。为了提高模型的诊断精度,采用差分进化算法对LS-SVM的结构参数进行优化,并与LS-SVM和PSO-LSSVM模型相比较。结果表明,DE-LSSVM 模型的故障分类准确性得到了提高,可以有效应用于滚动轴承故障诊断中。     

Noise source identification of diesel engine based on variational mode decomposition and robust independent component analysis

Through separating and identifying the noise sources of diesel engine, each independent noise obtained can be used as reference for the noise reduction, condition monitoring and fault diagnosis. In the noise source identification of diesel engine, the combustion noise and the piston slap noise are found to be overlapped in time domain and frequency domain. So it is intricate to accurately separate them. Therefore, the noise source identification method which is based on variational mode decomposition (VMD), robust independent component analysis (RobustICA) and continuous wavelet transform (CWT) is proposed. In the test, a 6-cylinder diesel engine was tested in a semi-anechoic chamber. The lead wrapped method was adopted to wrap No. 1–5 cylinders so as to isolate the interference noises, only the No. 6 cylinder part was bared. The single channel noise signal of cylinder head was measured. Then the variational mode decomposition algorithm is utilized to decompose the noise signal into several variational mode components. The RobustICA algorithm is adopted to extract the independent components. Finally, the continuous wavelet transform and the prior knowledge of diesel engine are applied to further identify the separated results. The results show that by using the proposed method to separate and identify the radiation noise of the cylinder head of the diesel engine, the independent components obtained are respectively the combustion noise and the piston slap noise. Comparing with the EEMD-RobustICA-CWT method, each independent noises obtained through the proposed method are more accurate and pure with less other interference noises.     

基于EEMD-Elman-Adaboost的中美股票价格预测研究

针对股票价格序列高度非正态、非线性、非平稳等复杂特征,文章以Elman神经网络为基础,引入集合经验模态分解(EEMD)与Adaboost算法,对中美股票的日收盘价进行预测。首先,利用EEMD算法将样本分解为多个本征模函数分量和1个残差分量。其次,用Adaboost算法优化Elman神经网络,对各个分量进行预测。最后,将各分量预测结果进行求和,作为最终预测结果。研究结果表明：EEMD-Elman-Adaboost模型对中美股票价格预测的均方根误差、平均相对误差、平均绝对误差均比现有的BP、Elman、EMD-Elman、EEMD-Elman模型小,新组合模型融合了EEMD、Elman神经网络、Adaboost算法的优点,具有更强的泛化能力和跟随能力。     

基于EEMD的LS-SVM和BP神经网络混合短期负荷预测

提出了基于总体平均经验模态分解(EEMD)、最小二乘支持向量机(LSSVM)和BP神经网络的实用综合短期负荷预测方法,进行电力系统短期负荷预测.首先运用EEMD方法将非平稳的负荷序列分解,然后根据分解后各分量的特点选用最佳的核函数,利用最小二乘支持向量机分别对各分量进行预测,最后对各分量预测结果采用BP神经网络重构得到最终的预测结果.对实测数据的分析表明基于该综合方法的电力系统短期负荷预测具有较高的精度.     

基于EEMD与模糊回归的投资组合选择模型及实证

随着模糊理论的不断发展与其在证券市场的广泛应用,越来越多的学者关注到参数模糊化对投资组合优化具有重要作用。本文利用集合经验模态分解(EEMD)和模糊线性回归相结合的预测方法,构建了基于对称三角模糊数的投资组合模型。并将提出的模型与集合经验模态分解和普通最小二乘结合的方法、单一模糊线性回归方法进行了对比分析,结果表明基于集合经验模态分解和模糊线性回归建立的投资组合模型最优,这对构建最优投资组合具有参考意义。