To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPT...To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.展开更多
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was appli...Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.展开更多
该文提出一种多标签排位小波支持向量机(rank wavelet support vector machine,Rank-WSVM),并将其应用于电能质量复合扰动分类中。Rank-WSVM将小波技术与多标签排位支持向量机(Rank-SVM)结合,利用小波的优良特性提高分类器的整体性能。...该文提出一种多标签排位小波支持向量机(rank wavelet support vector machine,Rank-WSVM),并将其应用于电能质量复合扰动分类中。Rank-WSVM将小波技术与多标签排位支持向量机(Rank-SVM)结合,利用小波的优良特性提高分类器的整体性能。首先,对电能质量扰动信号进行离散小波分解,计算Tsallis小波熵作为特征向量;然后利用所提出的Rank-WSVM多标签分类器进行分类。仿真结果表明,在不同噪声条件下,该方法有效改善了Rank-SVM的分类性能,可有效识别电压暂降、电压暂升、电压短时中断、脉冲暂态、振荡暂态、谐波和闪变等电能质量扰动及其组合而成的复合扰动。展开更多
[目的/意义]为实现山楂水分含量的快速无损检测,本研究探索了一种基于高光谱成像技术结合机器学习算法的检测方法。[方法]首先,收集458个来自不同产区不同品种的新鲜山楂样品,分别采集每个样品在可见-近红外波段(Visible to Near Infrar...[目的/意义]为实现山楂水分含量的快速无损检测,本研究探索了一种基于高光谱成像技术结合机器学习算法的检测方法。[方法]首先,收集458个来自不同产区不同品种的新鲜山楂样品,分别采集每个样品在可见-近红外波段(Visible to Near Infrared,VNIR)和短波红外(Short-Wave Infrared,SWIR)波段的高光谱数据,利用阈值分割算法确定每个山楂的感兴趣区域(Region of Interest,ROI),提取果实ROI的平均反射光谱作为原始数据。随后,采用卷积平滑、乘法散射校正、标准正态变换、一阶导数和二阶导数五种预处理方法,对原始光谱数据进行优化。在此基础上,结合偏最小二乘回归、支持向量回归(Support Vector Regression,SVR)、随机森林与多层感知机等机器学习方法,系统评估不同摆放方式(果柄朝侧面、朝上、朝下及三者融合)和光谱范围(VNIR、SWIR、VNIR+SWIR)对模型预测性能的影响。最后,采用连续投影算法、竞争自适应重加权采样算法、变量迭代空间收缩方法,以及离散小波变换-逐步回归(Discrete Wavelet Transform-Stepwise Regression,DWT-SR)四种方法对全波段数据进行降维处理,进一步减少数据冗余,提高模型效率。[结果和讨论]果柄朝下的摆放方式、SWIR波段范围(940~2500 nm)及一阶导数预处理组合下,SVR模型表现最优,测试集的绝对系数(Coefficient of Determination,R^(2)_(p))为0.8605、平均绝对误差(Mean Absolute Error,MAE p)为0.7111、均方根误差(Root Mean Square Error,RMSE p)为0.9142、相对分析误差(Ratio of Performance to Deviation,RPD)为2.6776。在性能最优分析条件下,DWT-SR方法基于小波基函数“db6”在分解层级为1时,提取出17个关键特征波段,所建模型在降低数据维度的同时可以保持高水平预测性能(R^(2)_(p)=0.8571、MAE_(p)=0.6692、RMSE p=0.9252、RPD=2.6457)。[结论]本研究证明了高光谱成像结合机器学习方法在山楂水分无损检测中的可行性,为果品水分在线监测及智能分选提供了理论依据与技术支撑。展开更多
文摘To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.
基金Supported by the Research Program of International Technology Collaboration and Communication of Sichuan(2007H12-017)
文摘Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
文摘该文提出一种多标签排位小波支持向量机(rank wavelet support vector machine,Rank-WSVM),并将其应用于电能质量复合扰动分类中。Rank-WSVM将小波技术与多标签排位支持向量机(Rank-SVM)结合,利用小波的优良特性提高分类器的整体性能。首先,对电能质量扰动信号进行离散小波分解,计算Tsallis小波熵作为特征向量;然后利用所提出的Rank-WSVM多标签分类器进行分类。仿真结果表明,在不同噪声条件下,该方法有效改善了Rank-SVM的分类性能,可有效识别电压暂降、电压暂升、电压短时中断、脉冲暂态、振荡暂态、谐波和闪变等电能质量扰动及其组合而成的复合扰动。
文摘[目的/意义]为实现山楂水分含量的快速无损检测,本研究探索了一种基于高光谱成像技术结合机器学习算法的检测方法。[方法]首先,收集458个来自不同产区不同品种的新鲜山楂样品,分别采集每个样品在可见-近红外波段(Visible to Near Infrared,VNIR)和短波红外(Short-Wave Infrared,SWIR)波段的高光谱数据,利用阈值分割算法确定每个山楂的感兴趣区域(Region of Interest,ROI),提取果实ROI的平均反射光谱作为原始数据。随后,采用卷积平滑、乘法散射校正、标准正态变换、一阶导数和二阶导数五种预处理方法,对原始光谱数据进行优化。在此基础上,结合偏最小二乘回归、支持向量回归(Support Vector Regression,SVR)、随机森林与多层感知机等机器学习方法,系统评估不同摆放方式(果柄朝侧面、朝上、朝下及三者融合)和光谱范围(VNIR、SWIR、VNIR+SWIR)对模型预测性能的影响。最后,采用连续投影算法、竞争自适应重加权采样算法、变量迭代空间收缩方法,以及离散小波变换-逐步回归(Discrete Wavelet Transform-Stepwise Regression,DWT-SR)四种方法对全波段数据进行降维处理,进一步减少数据冗余,提高模型效率。[结果和讨论]果柄朝下的摆放方式、SWIR波段范围(940~2500 nm)及一阶导数预处理组合下,SVR模型表现最优,测试集的绝对系数(Coefficient of Determination,R^(2)_(p))为0.8605、平均绝对误差(Mean Absolute Error,MAE p)为0.7111、均方根误差(Root Mean Square Error,RMSE p)为0.9142、相对分析误差(Ratio of Performance to Deviation,RPD)为2.6776。在性能最优分析条件下,DWT-SR方法基于小波基函数“db6”在分解层级为1时,提取出17个关键特征波段,所建模型在降低数据维度的同时可以保持高水平预测性能(R^(2)_(p)=0.8571、MAE_(p)=0.6692、RMSE p=0.9252、RPD=2.6457)。[结论]本研究证明了高光谱成像结合机器学习方法在山楂水分无损检测中的可行性,为果品水分在线监测及智能分选提供了理论依据与技术支撑。
文摘针对滚动轴承的多类故障特征非线性难以有效辨识的问题,提出基于局部切空间排列和小波支持向量机的滚动轴承故障诊断方法。在由集成经验模式分解(Ensemble Empirical Mode Decomposition,EEMD)处理后的频带能量组成的故障特征集中,首先采用局部切空间排列进行约简降维,提取其中的低维敏感特征,随后将获取的低维敏感特征输入给小波支持向量机进行滚动轴承的多类故障辨识。实验结果表明,基于局部切空间排列(local tangent space arrangement,LTSA)和小波支持向量机(wavelet support vector machine,WSVM)的滚动轴承故障诊断方法能够有效提取多类故障的低维敏感特征,并且相对传统诊断方法而言故障诊断精度更高。
