针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据...针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据的局部流形特征.另外,将故障状态与高维空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展相结合,减少了计算量,提高了算法的实时性,从而为复杂非线性系统的故障诊断提供了一种新的有效的方法.展开更多
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
The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SD...The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.展开更多
Deriving reaction coordinates for the characterization of chemical reactions has long been a demanding task.In our previous work[ACS Cent.Sci.3,407(2017)],the reaction coordinate of a(retro-)Claisen rearrangement in a...Deriving reaction coordinates for the characterization of chemical reactions has long been a demanding task.In our previous work[ACS Cent.Sci.3,407(2017)],the reaction coordinate of a(retro-)Claisen rearrangement in aqueous solution optimized through a Bayesian measure,a linear combination of bond lengths formation and breakage,was judged to be optimal among all trails.Here,considering the nonlinearity of the transition state,we use isometric mapping and locally linear embedding to obtain one reaction coordinate which is composed of a few collective variables.With these methods,we find a more reasonable and powerful one-dimensional reaction coordinate,which can well describe the reaction progression.To explore the reaction mechanism,we analyze the contribution of intrinsic molecular properties and the solventsolute interactions to the nonlinear reaction coordinate.Furthermore,another coordinate is identified to characterize the heterogeneity of reaction mechanisms.展开更多
Call a periodic map h on the closed orientable surface Σg extendable if h extends to a periodic map over the pair(S3, Σg) for possible embeddings e : Σg→ S3. The authors determine the extendabilities for all perio...Call a periodic map h on the closed orientable surface Σg extendable if h extends to a periodic map over the pair(S3, Σg) for possible embeddings e : Σg→ S3. The authors determine the extendabilities for all periodical maps on Σ2. The results involve various orientation preserving/reversing behalves of the periodical maps on the pair(S3, Σg). To do this the authors first list all periodic maps on Σ2, and indeed the authors exhibit each of them as a composition of primary and explicit symmetries, like rotations, reflections and antipodal maps, which itself should be interesting. A by-product is that for each even g,the maximum order periodic map on Σg is extendable, which contrasts sharply with the situation in the orientation preserving category.展开更多
文摘针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据的局部流形特征.另外,将故障状态与高维空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展相结合,减少了计算量,提高了算法的实时性,从而为复杂非线性系统的故障诊断提供了一种新的有效的方法.
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.
文摘The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.
基金supported by the National Nature Science Foundation of China(No.21927901 and No.92053202 to Yi Qin Gao)Zhen Zhang is supported by the Education Department of Hebei Province(QN2018308)+1 种基金Post-doctoral Foundation Project of Tangshan Normal University(2018A03)the Nature Science Foundation of Hebei Province of China(E2019105073)。
文摘Deriving reaction coordinates for the characterization of chemical reactions has long been a demanding task.In our previous work[ACS Cent.Sci.3,407(2017)],the reaction coordinate of a(retro-)Claisen rearrangement in aqueous solution optimized through a Bayesian measure,a linear combination of bond lengths formation and breakage,was judged to be optimal among all trails.Here,considering the nonlinearity of the transition state,we use isometric mapping and locally linear embedding to obtain one reaction coordinate which is composed of a few collective variables.With these methods,we find a more reasonable and powerful one-dimensional reaction coordinate,which can well describe the reaction progression.To explore the reaction mechanism,we analyze the contribution of intrinsic molecular properties and the solventsolute interactions to the nonlinear reaction coordinate.Furthermore,another coordinate is identified to characterize the heterogeneity of reaction mechanisms.
基金supported by the National Natural Science Foundation of China(No.10631060)
文摘Call a periodic map h on the closed orientable surface Σg extendable if h extends to a periodic map over the pair(S3, Σg) for possible embeddings e : Σg→ S3. The authors determine the extendabilities for all periodical maps on Σ2. The results involve various orientation preserving/reversing behalves of the periodical maps on the pair(S3, Σg). To do this the authors first list all periodic maps on Σ2, and indeed the authors exhibit each of them as a composition of primary and explicit symmetries, like rotations, reflections and antipodal maps, which itself should be interesting. A by-product is that for each even g,the maximum order periodic map on Σg is extendable, which contrasts sharply with the situation in the orientation preserving category.
