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
In this paper, a new nonlinear fault detection technique based on locally linear embedding （LLE） is developed. LLE can efficiently compute the low-dimensional embedding of the data with the local neighborhood struct...In this paper, a new nonlinear fault detection technique based on locally linear embedding （LLE） is developed. LLE can efficiently compute the low-dimensional embedding of the data with the local neighborhood structure information preserved. In this method, a data-dependent kernel matrix which can reflect the nonlinear data structure is defined. Based on the kernel matrix, the Nystrrm formula makes the mapping extended to the testing data possible. With the kernel view of the LLE, two monitoring statistics are constructed. Together with the out of sample extensions, LLE is used for nonlinear fault detection. Simulation cases were studied to demonstrate the performance of the proposed method.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
Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is...Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select k.Different samples will produce different k adaptively according to the density of the data distribution.The method is applied to classify plant leaves.The experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.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.
基金supported in part by the National Basic Research Program of China(973 Program)(No.2012CB720505)the National Natural Science Foundation of China(No.61273167)
文摘In this paper, a new nonlinear fault detection technique based on locally linear embedding （LLE） is developed. LLE can efficiently compute the low-dimensional embedding of the data with the local neighborhood structure information preserved. In this method, a data-dependent kernel matrix which can reflect the nonlinear data structure is defined. Based on the kernel matrix, the Nystrrm formula makes the mapping extended to the testing data possible. With the kernel view of the LLE, two monitoring statistics are constructed. Together with the out of sample extensions, LLE is used for nonlinear fault detection. Simulation cases were studied to demonstrate the performance of the proposed method.
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.
基金This study was financially supported by the National Natural Science Foundation of China(61172127)the Research Fund for the Doctoral Program of Higher Education(KJQN1114)+2 种基金Anhui Provincial Natural Science Foundation(1308085QC58)the 211 Project Youth Scientific Research Fund of Anhui UniversityProvincial Natural Science Foundation of Anhui Universities(KJ2013A026)。
文摘Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select k.Different samples will produce different k adaptively according to the density of the data distribution.The method is applied to classify plant leaves.The experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.
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。
