Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected ...Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.展开更多
In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manif...In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.展开更多
经数据分析途径实现机器智能的故障决策引发出了关于故障数据集的降维问题。通过将等距映射算法(Isometric Mapping,ISOMAP)、局部线性嵌入(Locally Linear Embedding,LLE)算法的优缺点进行互补,提出一种适用于非线性数据集降维的核框...经数据分析途径实现机器智能的故障决策引发出了关于故障数据集的降维问题。通过将等距映射算法(Isometric Mapping,ISOMAP)、局部线性嵌入(Locally Linear Embedding,LLE)算法的优缺点进行互补,提出一种适用于非线性数据集降维的核框架下等距映射与局部线性嵌入相结合的KISOMAPLLE算法。该算法能够同时满足全局距离保持性和局部结构保持能力的数据降维基本要求。用典型的人工数据集和转子故障数据集进行的降维验证结果表明,该算法能够继承ISOMAP、LLE两种算法的各自优良性能,具有能够显著提高典型非线性数据集分类精度的性能。展开更多
由于人类DNA序列上单核苷酸具有多态性,DNA序列异常挖掘是后基因组时代的一个重要研究课题。文章在分析现有DNA序列数据挖掘方法的基础上,利用流形学习中不同低维嵌入向量之间向量距离不同的特点,提出了基于流形学习的DNA序列数据挖掘方...由于人类DNA序列上单核苷酸具有多态性,DNA序列异常挖掘是后基因组时代的一个重要研究课题。文章在分析现有DNA序列数据挖掘方法的基础上,利用流形学习中不同低维嵌入向量之间向量距离不同的特点,提出了基于流形学习的DNA序列数据挖掘方法(5Dlocally linear embedding,简称5DLLE)。实验结果表明,与隐马尔可夫模型(HMM)和支持向量机(SVM)相比,文中所提出的5DLLE方法在DNA序列数据挖掘方面具有一定优势,不但平均识别率高,而且计算时间相对较少。展开更多
基金supported by Research Grant from the Kajima Foundation,JST CREST Grant No.JPMJCR1911,JapanJSPS KAKENHI(Nos.17K06633,21K04351).
文摘Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.
基金supported in part by the National Natural Science Foundation of China(Nos.61373093,61402310,61672364,and 61672365)the National Key Research and Development Program of China(No.2018YFA0701701)。
文摘In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.
文摘经数据分析途径实现机器智能的故障决策引发出了关于故障数据集的降维问题。通过将等距映射算法(Isometric Mapping,ISOMAP)、局部线性嵌入(Locally Linear Embedding,LLE)算法的优缺点进行互补,提出一种适用于非线性数据集降维的核框架下等距映射与局部线性嵌入相结合的KISOMAPLLE算法。该算法能够同时满足全局距离保持性和局部结构保持能力的数据降维基本要求。用典型的人工数据集和转子故障数据集进行的降维验证结果表明,该算法能够继承ISOMAP、LLE两种算法的各自优良性能,具有能够显著提高典型非线性数据集分类精度的性能。
文摘由于人类DNA序列上单核苷酸具有多态性,DNA序列异常挖掘是后基因组时代的一个重要研究课题。文章在分析现有DNA序列数据挖掘方法的基础上,利用流形学习中不同低维嵌入向量之间向量距离不同的特点,提出了基于流形学习的DNA序列数据挖掘方法(5Dlocally linear embedding,简称5DLLE)。实验结果表明,与隐马尔可夫模型(HMM)和支持向量机(SVM)相比,文中所提出的5DLLE方法在DNA序列数据挖掘方面具有一定优势,不但平均识别率高,而且计算时间相对较少。
