This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators...This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.展开更多
To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed t...To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.展开更多
The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were co...The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were constructed to analyze the genetic structure and relationship among 10 Chinese indigenous chicken breeds.The results showed that dendograms of DA genetic distance using the NJ method divided the 10 chicken breeds into two main clusters;one consisted of breeds of low weight body(CHA,TTB,XIA,GUS and BAI),the other contained heavier breeds(LAN,DAG,YOU,XIS and LUY).In the lighter breeds,TIB and CHA clustered together,as did XIA and GUS.In the heavier breeds,XIS and LUY was clustered together in one branch,but LAN,DAG and YOU clustered in independent branches.The results were consistent with Nm estimates among the 10 indigenous chicken breeds.The STRUCTURE program properly inferred the presence of genetic structure despite not pre-defining the origin of individuals.The genetic cluster inferred by STRUCTURE was basically the same as that from the DA distance clustering method.An advantage of the STRUCTURE program was its ability to identify the migrants and admixed individuals in the 10 chicken populations;this could not be achieved by use of the DA distance clustering method.展开更多
Graph-theoretical approaches have been widely used for data clustering and image segmentation recently. The goal of data clustering is to discover the underlying distribution and structural information of the given da...Graph-theoretical approaches have been widely used for data clustering and image segmentation recently. The goal of data clustering is to discover the underlying distribution and structural information of the given data, while image segmentation is to partition an image into several non-overlapping regions. Therefore, two popular graph-theoretical clustering methods are analyzed, including the directed tree based data clustering and the minimum spanning tree based image segmentation. There are two contributions： （1） To improve the directed tree based data clustering for image segmentation, （2） To improve the minimum spanning tree based image segmentation for data clustering. The extensive experiments using artificial and real-world data indicate that the improved directed tree based image segmentation can partition images well by preserving enough details, and the improved minimum spanning tree based data clustering can well cluster data in manifold structure.展开更多
Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clus...Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.展开更多
The cluster-shaped plasmonic nanostructures are used to manage the incident light inside an ultra-thin silicon solar cell.Here we simulate spherical,conical,pyramidal,and cylindrical nanoparticles in a form of a clust...The cluster-shaped plasmonic nanostructures are used to manage the incident light inside an ultra-thin silicon solar cell.Here we simulate spherical,conical,pyramidal,and cylindrical nanoparticles in a form of a cluster at the rear side of a thin silicon cell,using the finite difference time domain(FDTD)method.By calculating the optical absorption and hence the photocurrent,it is shown that the clustering of nanoparticles significantly improves them.The photocurrent enhancement is the result of the plasmonic effects of clustering the nanoparticles.For comparison,first a cell with a single nanoparticle at the rear side is evaluated.Then four smaller nanoparticles are put around it to make a cluster.The photocurrents of 20.478 mA/cm2,23.186 mA/cm2,21.427 mA/cm2,and 21.243 mA/cm2 are obtained for the cells using clustering conical,spherical,pyramidal,cylindrical NPs at the backside,respectively.These values are 13.987 mA/cm2,16.901 mA/cm2,16.507 mA/cm2,17.926 mA/cm2 for the cell with one conical,spherical,pyramidal,cylindrical NPs at the backside,respectively.Therefore,clustering can significantly improve the photocurrents.Finally,the distribution of the electric field and the generation rate for the proposed structures are calculated.展开更多
To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and ...To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.展开更多
With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of th...With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.展开更多
Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct ar...Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.展开更多
为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰...为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52076038,U22B20112,No.52106238)the Fundamental Research Funds for Central Universities(No.423162,B230201051).
文摘This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.
基金Supported by the National Natural Science Foundation of China (42174142)National Science and Technology Major Project (2017ZX05039-002)+2 种基金Operation Fund of China National Petroleum Corporation Logging Key Laboratory (2021DQ20210107-11)Fundamental Research Funds for Central Universities (19CX02006A)Major Science and Technology Project of China National Petroleum Corporation (ZD2019-183-006)。
文摘To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.
基金supported by the Program of National Technological Basis from Ministry of Science and Technology of China(No.2005DKA21101)the National Natural Science Foundation of China(No.30700572)
文摘The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were constructed to analyze the genetic structure and relationship among 10 Chinese indigenous chicken breeds.The results showed that dendograms of DA genetic distance using the NJ method divided the 10 chicken breeds into two main clusters;one consisted of breeds of low weight body(CHA,TTB,XIA,GUS and BAI),the other contained heavier breeds(LAN,DAG,YOU,XIS and LUY).In the lighter breeds,TIB and CHA clustered together,as did XIA and GUS.In the heavier breeds,XIS and LUY was clustered together in one branch,but LAN,DAG and YOU clustered in independent branches.The results were consistent with Nm estimates among the 10 indigenous chicken breeds.The STRUCTURE program properly inferred the presence of genetic structure despite not pre-defining the origin of individuals.The genetic cluster inferred by STRUCTURE was basically the same as that from the DA distance clustering method.An advantage of the STRUCTURE program was its ability to identify the migrants and admixed individuals in the 10 chicken populations;this could not be achieved by use of the DA distance clustering method.
基金Supported by the Key National Natural Science Foundation of China(61035003)~~
文摘Graph-theoretical approaches have been widely used for data clustering and image segmentation recently. The goal of data clustering is to discover the underlying distribution and structural information of the given data, while image segmentation is to partition an image into several non-overlapping regions. Therefore, two popular graph-theoretical clustering methods are analyzed, including the directed tree based data clustering and the minimum spanning tree based image segmentation. There are two contributions： （1） To improve the directed tree based data clustering for image segmentation, （2） To improve the minimum spanning tree based image segmentation for data clustering. The extensive experiments using artificial and real-world data indicate that the improved directed tree based image segmentation can partition images well by preserving enough details, and the improved minimum spanning tree based data clustering can well cluster data in manifold structure.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1804121 and 11304167)
文摘Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.
文摘The cluster-shaped plasmonic nanostructures are used to manage the incident light inside an ultra-thin silicon solar cell.Here we simulate spherical,conical,pyramidal,and cylindrical nanoparticles in a form of a cluster at the rear side of a thin silicon cell,using the finite difference time domain(FDTD)method.By calculating the optical absorption and hence the photocurrent,it is shown that the clustering of nanoparticles significantly improves them.The photocurrent enhancement is the result of the plasmonic effects of clustering the nanoparticles.For comparison,first a cell with a single nanoparticle at the rear side is evaluated.Then four smaller nanoparticles are put around it to make a cluster.The photocurrents of 20.478 mA/cm2,23.186 mA/cm2,21.427 mA/cm2,and 21.243 mA/cm2 are obtained for the cells using clustering conical,spherical,pyramidal,cylindrical NPs at the backside,respectively.These values are 13.987 mA/cm2,16.901 mA/cm2,16.507 mA/cm2,17.926 mA/cm2 for the cell with one conical,spherical,pyramidal,cylindrical NPs at the backside,respectively.Therefore,clustering can significantly improve the photocurrents.Finally,the distribution of the electric field and the generation rate for the proposed structures are calculated.
基金The 15th Plan National Defence Preven-tive Research Project (No.413030201)
文摘To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.
基金supported by the Key Technology Projects of the China Southern Power Grid Corporation(STKJXM20200059)the Key Support Project of the Joint Fund of the National Natural Science Foundation of China(U22B20123)。
文摘With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.
基金supported by the National Natural Science Foundation of China under Grant No.U2341208.
文摘Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.
文摘为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。
