How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle co...How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.展开更多
A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental...A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.展开更多
针对电力机车牵引变流器中故障率最高的单相脉宽调制(pulse width modulation,PWM)整流器,提出一种流形学习算法融合多域特征的故障诊断方法。根据整流器在不同工作状态下的时域、频域和时频域特征构建多域特征向量;采用Hessian局部线...针对电力机车牵引变流器中故障率最高的单相脉宽调制(pulse width modulation,PWM)整流器,提出一种流形学习算法融合多域特征的故障诊断方法。根据整流器在不同工作状态下的时域、频域和时频域特征构建多域特征向量;采用Hessian局部线性嵌入(Hessian local linear embedding,HLLE)算法融合多域特征,根据故障样本数和聚类结果,解决高维数据中固有维数和最近邻数选取困难的问题,得到用于描述故障特征的最优低维特征向量,减少特征之间的冲突和冗余;采用支持向量机进行模式识别,实现对整流器的故障诊断。结果表明:对不同的输出电压,不同的训练和测试比,15种故障模式均具有较高的诊断率。与其他方法相比,本文方法具有较好的融合效果和较强的鲁棒性。展开更多
LLE(Locally Linear Embedding)算法是一种较好的流形学习算法,但它只能以批处理的方式进行.只要有新的样本加入,就必须重作该算法的全部内容,而原处理结果被全部丢弃.本文提出了一种基于正交迭代的增量LLE算法,能有效地利用前面的处理...LLE(Locally Linear Embedding)算法是一种较好的流形学习算法,但它只能以批处理的方式进行.只要有新的样本加入,就必须重作该算法的全部内容,而原处理结果被全部丢弃.本文提出了一种基于正交迭代的增量LLE算法,能有效地利用前面的处理结果,实现增量处理.实验表明该算法是有效的.展开更多
在人像识别方面,传统的特征提取方法大都是线性的,不能很好地保持样本的拓扑结构。支持向量机能提高学习的泛化能力,防止过学习,是一种很好的分类器。为此,提出一种增强的LLE(Locally Linear Em- bedding)和SVM(support Vector Machine...在人像识别方面,传统的特征提取方法大都是线性的,不能很好地保持样本的拓扑结构。支持向量机能提高学习的泛化能力,防止过学习,是一种很好的分类器。为此,提出一种增强的LLE(Locally Linear Em- bedding)和SVM(support Vector Machine)结合的人像识别方法,采用PCA(Principal Component Analysis)与LLE相结合算法,对光照归一化处理过的人脸图像进行特征提取,利用SVM的分类机制对人脸图像样本集进行训练和识别。在ORL(Olivetti Research Laboratory)人脸数据库上实验表明,该算法稳健、快速,识别率达到了90%以上。展开更多
鸡蛋新鲜度是反映鸡蛋内部品质的一个重要指标。为了能够实现鸡蛋新鲜度的快速无损检测,利用微型光纤光谱仪采集鸡蛋550～950nm的透射率光谱曲线,与鸡蛋的哈夫单位值进行了定量分析。通过不同的预处理方式分别结合偏最小二乘回归（parti...鸡蛋新鲜度是反映鸡蛋内部品质的一个重要指标。为了能够实现鸡蛋新鲜度的快速无损检测,利用微型光纤光谱仪采集鸡蛋550～950nm的透射率光谱曲线,与鸡蛋的哈夫单位值进行了定量分析。通过不同的预处理方式分别结合偏最小二乘回归（partial least squares regression,PLSR）与支持向量回归（support vector regression,SVR）建立模型,比较了不同模型的预测结果,发现一阶微分结合SVR能够实现较好地预测,且利用SVR建模要优于PLSR。为了提高运算效率,减少无用信息对建模的不良影响,分别利用线性降维主成分分析法（principal component analysis,PCA）与非线性降维局部线性嵌入（locally linear embedding,LLE）对一阶微分后的光谱数据降维,比较两种降维方法的预测效果,得出了LLE降维要优于PCA降维,其训练集和预测集的相关系数与均方根误差分别为92.2%,7.21和91.1%,8.80,训练集交叉验证的均方根误差相比减少了0.79。实验结果表明,利用局部线性嵌入结合支持向量回归进行非线性建模,能够提高鸡蛋新鲜度的预测能力,表明该方法对鸡蛋新鲜度的可见/近红外光谱检测可行。展开更多
基金National Key Science & Technology Special Projects(Grant No.2008ZX05000-004)CNPC Projects(Grant No.2008E-0610-10).
文摘How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
基金supported by the Fundamental Research Funds for the Central Universities(No.2016083)
文摘A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.
文摘针对电力机车牵引变流器中故障率最高的单相脉宽调制(pulse width modulation,PWM)整流器,提出一种流形学习算法融合多域特征的故障诊断方法。根据整流器在不同工作状态下的时域、频域和时频域特征构建多域特征向量;采用Hessian局部线性嵌入(Hessian local linear embedding,HLLE)算法融合多域特征,根据故障样本数和聚类结果,解决高维数据中固有维数和最近邻数选取困难的问题,得到用于描述故障特征的最优低维特征向量,减少特征之间的冲突和冗余;采用支持向量机进行模式识别,实现对整流器的故障诊断。结果表明:对不同的输出电压,不同的训练和测试比,15种故障模式均具有较高的诊断率。与其他方法相比,本文方法具有较好的融合效果和较强的鲁棒性。
文摘LLE(Locally Linear Embedding)算法是一种较好的流形学习算法,但它只能以批处理的方式进行.只要有新的样本加入,就必须重作该算法的全部内容,而原处理结果被全部丢弃.本文提出了一种基于正交迭代的增量LLE算法,能有效地利用前面的处理结果,实现增量处理.实验表明该算法是有效的.
文摘在人像识别方面,传统的特征提取方法大都是线性的,不能很好地保持样本的拓扑结构。支持向量机能提高学习的泛化能力,防止过学习,是一种很好的分类器。为此,提出一种增强的LLE(Locally Linear Em- bedding)和SVM(support Vector Machine)结合的人像识别方法,采用PCA(Principal Component Analysis)与LLE相结合算法,对光照归一化处理过的人脸图像进行特征提取,利用SVM的分类机制对人脸图像样本集进行训练和识别。在ORL(Olivetti Research Laboratory)人脸数据库上实验表明,该算法稳健、快速,识别率达到了90%以上。
文摘鸡蛋新鲜度是反映鸡蛋内部品质的一个重要指标。为了能够实现鸡蛋新鲜度的快速无损检测,利用微型光纤光谱仪采集鸡蛋550～950nm的透射率光谱曲线,与鸡蛋的哈夫单位值进行了定量分析。通过不同的预处理方式分别结合偏最小二乘回归（partial least squares regression,PLSR）与支持向量回归（support vector regression,SVR）建立模型,比较了不同模型的预测结果,发现一阶微分结合SVR能够实现较好地预测,且利用SVR建模要优于PLSR。为了提高运算效率,减少无用信息对建模的不良影响,分别利用线性降维主成分分析法（principal component analysis,PCA）与非线性降维局部线性嵌入（locally linear embedding,LLE）对一阶微分后的光谱数据降维,比较两种降维方法的预测效果,得出了LLE降维要优于PCA降维,其训练集和预测集的相关系数与均方根误差分别为92.2%,7.21和91.1%,8.80,训练集交叉验证的均方根误差相比减少了0.79。实验结果表明,利用局部线性嵌入结合支持向量回归进行非线性建模,能够提高鸡蛋新鲜度的预测能力,表明该方法对鸡蛋新鲜度的可见/近红外光谱检测可行。
