In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as ...In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.展开更多
为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,...为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度;再次,通过局部最大同步挤压算子进行时频重排;最后,采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性,研究结果表明:利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法,基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。展开更多
针对滚动轴承传统故障诊断方法需要先验知识以及人工提取特征导致故障识别错误率高的问题,提出一种基于广义S变换(Generalized S transform,GST)和改进卷积神经网络(Convolutional Neural Network,CNN)的滚动轴承智能故障诊断方法。使用...针对滚动轴承传统故障诊断方法需要先验知识以及人工提取特征导致故障识别错误率高的问题,提出一种基于广义S变换(Generalized S transform,GST)和改进卷积神经网络(Convolutional Neural Network,CNN)的滚动轴承智能故障诊断方法。使用GST将一维振动信号转换为特征信息更加丰富的时频图,更加全面提取滚动轴承的故障特征信息。通过加入弹性斜率和高斯分布的神经元噪声,提出改进的激活函数EReLTanh(Elastic Rectified Linear Tanh,EReLTanh),并基于EReLTanh激活函数构建CNN。将得到的时频图进行压缩和归一化处理,生成时频图数据集并划分数据集。利用时频图数据集训练改进CNN,实现滚动轴承的智能故障诊断。使用自制实验平台采集不同种类滚动轴承故障数据,利用t-SNE进行全连接层特征降维可视化,结果表明:使用EReLTanh激活函数的CNN模型能够将不同故障样本的特征进行准确的分类,达到故障识别要求,同时使用该数据利用S变换、小波变换、GST并结合改进CNN和未改进CNN进行对比,提出的方法准确率得到提升。通过分析和对比实验可得出结论,利用GST和改进CNN的滚动轴承智能故障诊断方法能够在实际工程中更加简单方便地判断出故障类型及损伤程度,满足实际工程的需求。展开更多
空间中存在的射频干扰(Radio Frequency Interference,RFI)会污染合成孔径雷达(Synthetic Aperture Radar,SAR)的回波数据,进而影响成像质量以及基于图像的应用。本文针对RFI的特点,提出了一种基于广义S变换(Generalized S Transform,G...空间中存在的射频干扰(Radio Frequency Interference,RFI)会污染合成孔径雷达(Synthetic Aperture Radar,SAR)的回波数据,进而影响成像质量以及基于图像的应用。本文针对RFI的特点,提出了一种基于广义S变换(Generalized S Transform,GST)时频滤波的干扰抑制算法。该算法首先利用配对样本T检验对存在干扰的回波数据进行检测并标记,然后对被标记的回波数据的实部与虚部分别进行处理:将数据变换到广义S变换域,逐条对时间窗内的数据进行子空间滤波完成干扰抑制,接着把干扰抑制后的数据反变换到时域并与未标记信号组成新的纯净回波数据集,最后利用成像算法进行成像处理得到清晰的SAR图像。所提出算法可以在有效抑制SAR数据中射频干扰的同时,减少处理过程中有用信号的损失,实验结果验证了算法的有效性。展开更多
By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization...By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization, and Weyl ordering as its three special cases. Some operator identities can be derived directly by virtue of the s-parameterized quantization scheme.展开更多
In the present paper, the authors introduce a new integral transform which yields a number of potentially useful （known or new） integral transfoms as its special cases. Many fundamental results about this new integr...In the present paper, the authors introduce a new integral transform which yields a number of potentially useful （known or new） integral transfoms as its special cases. Many fundamental results about this new integral transform, which are established in this paper, in- clude （for example） existence theorem, Parseval-type relationship and inversion formula. The relationship between the new integral transform with the H-function and the H-transform are characterized by means of some integral identities. The introduced transform is also used to find solution to a certain differential equation. Some illustrative examples are also given.展开更多
探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：...探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：①用广义 S 变换对高信噪比的叠加地震信号逐道进行时频分析；②在每个时间点,根据地层吸收特点提取各个频率的能量吸收衰减因子；③用加权方法对每个时间所对应的各个频率的广义 S 变换系数进行补偿,使各个频率在不同时间的能量相同；④将所有时间各个频率加权补偿的结果重构回地震记录,实现对地层吸收的补偿。模拟结果表明,广义 S 变换时频分析方法能够提高信号时频分布的分辨率。对实际二维地震数据的试算结果表明,基于广义 S 变换的吸收衰减补偿方法能较好地对地层吸收进行补偿,提高地震资料的分辨率,改善地震资料的品质。展开更多
基金supported by National 973 Key Basic Research Development Program(No.2007CB209602)National 863 High Technology Research Development Program (No.2007AA067.229)
文摘In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.
文摘为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度;再次,通过局部最大同步挤压算子进行时频重排;最后,采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性,研究结果表明:利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法,基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。
文摘针对滚动轴承传统故障诊断方法需要先验知识以及人工提取特征导致故障识别错误率高的问题,提出一种基于广义S变换(Generalized S transform,GST)和改进卷积神经网络(Convolutional Neural Network,CNN)的滚动轴承智能故障诊断方法。使用GST将一维振动信号转换为特征信息更加丰富的时频图,更加全面提取滚动轴承的故障特征信息。通过加入弹性斜率和高斯分布的神经元噪声,提出改进的激活函数EReLTanh(Elastic Rectified Linear Tanh,EReLTanh),并基于EReLTanh激活函数构建CNN。将得到的时频图进行压缩和归一化处理,生成时频图数据集并划分数据集。利用时频图数据集训练改进CNN,实现滚动轴承的智能故障诊断。使用自制实验平台采集不同种类滚动轴承故障数据,利用t-SNE进行全连接层特征降维可视化,结果表明:使用EReLTanh激活函数的CNN模型能够将不同故障样本的特征进行准确的分类,达到故障识别要求,同时使用该数据利用S变换、小波变换、GST并结合改进CNN和未改进CNN进行对比,提出的方法准确率得到提升。通过分析和对比实验可得出结论,利用GST和改进CNN的滚动轴承智能故障诊断方法能够在实际工程中更加简单方便地判断出故障类型及损伤程度,满足实际工程的需求。
文摘空间中存在的射频干扰(Radio Frequency Interference,RFI)会污染合成孔径雷达(Synthetic Aperture Radar,SAR)的回波数据,进而影响成像质量以及基于图像的应用。本文针对RFI的特点,提出了一种基于广义S变换(Generalized S Transform,GST)时频滤波的干扰抑制算法。该算法首先利用配对样本T检验对存在干扰的回波数据进行检测并标记,然后对被标记的回波数据的实部与虚部分别进行处理:将数据变换到广义S变换域,逐条对时间窗内的数据进行子空间滤波完成干扰抑制,接着把干扰抑制后的数据反变换到时域并与未标记信号组成新的纯净回波数据集,最后利用成像算法进行成像处理得到清晰的SAR图像。所提出算法可以在有效抑制SAR数据中射频干扰的同时,减少处理过程中有用信号的损失,实验结果验证了算法的有效性。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11147009,11347026,and 11244005)the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2013AM012 and ZR2012AM004)the Natural Science Foundation of Liaocheng University,China
文摘By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization, and Weyl ordering as its three special cases. Some operator identities can be derived directly by virtue of the s-parameterized quantization scheme.
文摘In the present paper, the authors introduce a new integral transform which yields a number of potentially useful （known or new） integral transfoms as its special cases. Many fundamental results about this new integral transform, which are established in this paper, in- clude （for example） existence theorem, Parseval-type relationship and inversion formula. The relationship between the new integral transform with the H-function and the H-transform are characterized by means of some integral identities. The introduced transform is also used to find solution to a certain differential equation. Some illustrative examples are also given.
文摘探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：①用广义 S 变换对高信噪比的叠加地震信号逐道进行时频分析；②在每个时间点,根据地层吸收特点提取各个频率的能量吸收衰减因子；③用加权方法对每个时间所对应的各个频率的广义 S 变换系数进行补偿,使各个频率在不同时间的能量相同；④将所有时间各个频率加权补偿的结果重构回地震记录,实现对地层吸收的补偿。模拟结果表明,广义 S 变换时频分析方法能够提高信号时频分布的分辨率。对实际二维地震数据的试算结果表明,基于广义 S 变换的吸收衰减补偿方法能较好地对地层吸收进行补偿,提高地震资料的分辨率,改善地震资料的品质。
