For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of char...For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.展开更多
Network topology inference is one of the important applications of network tomography.Traditional network topology inference may impact network normal operation due to its generation of huge data traffic.A unicast net...Network topology inference is one of the important applications of network tomography.Traditional network topology inference may impact network normal operation due to its generation of huge data traffic.A unicast network topology inference is proposed to use time to live(TTL)for layering and classify nodes layer by layer based on the similarity of node pairs.Finally,the method infers logical network topology effectively with self-adaptive combination of previous results.Simulation results show that the proposed method holds a high accuracy of topology inference while decreasing network measuring flow,thus improves measurement efficiency.展开更多
For the load modeling of a large power grid,the large number of substations covered by it must be segregated into several categories and,thereafter,a load model built for each type.To address the problem of skewed clu...For the load modeling of a large power grid,the large number of substations covered by it must be segregated into several categories and,thereafter,a load model built for each type.To address the problem of skewed clustering tree in the classical hierarchical clustering method used for categorizing substations,a fair hierarchical clustering method is proposed in this paper.First,the fairness index is defined based on the Gini coefficient.Thereafter,a hierarchical clustering method is proposed based on the fairness index.Finally,the clustering results are evaluated using the contour coefficient and the t-SNE two-dimensional plane map.The substations clustering example of a real large power grid considered in this paper illustrates that the proposed fair hierarchical clustering method can effectively address the problem of the skewed clustering tree with high accuracy.展开更多
The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized...The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.展开更多
Based on non-maximally entangled four-particle cluster states, we propose a new hierarchical information splitting protocol to probabilistically realize the quantum state sharing of an arbitrary unknown two-qubit stat...Based on non-maximally entangled four-particle cluster states, we propose a new hierarchical information splitting protocol to probabilistically realize the quantum state sharing of an arbitrary unknown two-qubit state. In this scheme, the sender transmits the two-qubit secret state to three agents who are divided into two grades with two Bell-state measurements,and broadcasts the measurement results via a classical channel. One agent is in the upper grade and two agents are in the lower grade. The agent in the upper grade only needs to cooperate with one of the other two agents to recover the secret state but both of the agents in the lower grade need help from all of the agents. Every agent who wants to recover the secret state needs to introduce two ancillary qubits and performs a positive operator-valued measurement（POVM） instead of the usual projective measurement. Moreover, due to the symmetry of the cluster state, we extend this protocol to multiparty agents.展开更多
Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the S...Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.展开更多
How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.A...How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.展开更多
In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for disc...In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including k-means, fuzzy c-means, and hierarchical clustering have been widely used in literatures. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods, specially, in yeast saccharomyces cerevisiae. In this paper, these three gene clustering methods are compared. Classification accuracy and CPU time cost are employed for measuring performance of these algorithms. Our results show that hierarchical clustering outperforms k-means and fuzzy c-means clustering. The analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis of gene expression.展开更多
A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lowe...A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.展开更多
传统的数据挖掘方法一般从组方中所有的药物出发,挖掘药物的用药规律,计算量大,且仅仅基于药物频次对组方进行研究,忽略了药物剂量因素,难以发现频次低但剂量占比高的具有良好疗效的药物。针对以上问题,提出一种改进的基于效用度(Effect...传统的数据挖掘方法一般从组方中所有的药物出发,挖掘药物的用药规律,计算量大,且仅仅基于药物频次对组方进行研究,忽略了药物剂量因素,难以发现频次低但剂量占比高的具有良好疗效的药物。针对以上问题,提出一种改进的基于效用度(Effect Degree,ED)核心药物发现算法,并将基于效用度的点式互信息(Pointwise Mutual Information with Herb Pair ED,PMIED)与节点度结合,定义一种新的加权相关系数作为药物权重,在所发现的核心药物中运用层次聚类算法研究用药规律。实验结果表明,该算法可有效挖掘出组方中的核心药物,经过分析,所发现的核心药物和药物组合均对痰瘀互阻证具有良好疗效。展开更多
实体关系联合抽取作为构建知识图谱的核心环节,旨在从非结构化文本中提取实体-关系三元组。针对现有联合抽取方法在解码时未能有效处理实体关系间的相互作用,导致对语境理解不足,产生冗余信息等问题,提出一种基于并行解码和聚类的实体...实体关系联合抽取作为构建知识图谱的核心环节,旨在从非结构化文本中提取实体-关系三元组。针对现有联合抽取方法在解码时未能有效处理实体关系间的相互作用,导致对语境理解不足,产生冗余信息等问题,提出一种基于并行解码和聚类的实体关系联合抽取模型。首先,利用BERT(bidirectional encoder representations from transformers)模型进行文本编码,获取语义信息丰富的字符向量。其次,采用非自回归并行解码器增强实体关系间的交互,并引入层次凝聚聚类算法及多数投票机制进一步优化解码结果以捕获语境信息,减少冗余信息。最后,生成高质量的三元组集合,以构建课程知识图谱。为评估该方法的性能,在公共数据集NYT和WebNLG以及自建C语言数据集上进行实验,结果表明,该方法在精确率和F1值上优于其他对比模型。展开更多
基金supported by the National Natural Science Foundation of China(No.51575047)
文摘For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.
基金supported by the National Natural Science Foundation of China (Nos.61373137,61373017, 61373139)the Major Program of Jiangsu Higher Education Institutions (No.14KJA520002)+1 种基金the Six Industries Talent Peaks Plan of Jiangsu(No.2013-DZXX-014)the Jiangsu Qinglan Project
文摘Network topology inference is one of the important applications of network tomography.Traditional network topology inference may impact network normal operation due to its generation of huge data traffic.A unicast network topology inference is proposed to use time to live(TTL)for layering and classify nodes layer by layer based on the similarity of node pairs.Finally,the method infers logical network topology effectively with self-adaptive combination of previous results.Simulation results show that the proposed method holds a high accuracy of topology inference while decreasing network measuring flow,thus improves measurement efficiency.
基金supported by the Major Science and Technology Project of Yunnan Province entitled“Research and Application of Key Technologies of Power Grid Operation Analysis and Protection Control for Improving Green Power Consumption”(202002AF080001)the China South Power Grid Science and Technology Project entitled“Research on Load Model and Modeling Method of Yunnan Power Grid”(YNKJXM20180017).
文摘For the load modeling of a large power grid,the large number of substations covered by it must be segregated into several categories and,thereafter,a load model built for each type.To address the problem of skewed clustering tree in the classical hierarchical clustering method used for categorizing substations,a fair hierarchical clustering method is proposed in this paper.First,the fairness index is defined based on the Gini coefficient.Thereafter,a hierarchical clustering method is proposed based on the fairness index.Finally,the clustering results are evaluated using the contour coefficient and the t-SNE two-dimensional plane map.The substations clustering example of a real large power grid considered in this paper illustrates that the proposed fair hierarchical clustering method can effectively address the problem of the skewed clustering tree with high accuracy.
基金the General Program of National Natural Science Foundation of China(62072049).
文摘The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant No.61671087)
文摘Based on non-maximally entangled four-particle cluster states, we propose a new hierarchical information splitting protocol to probabilistically realize the quantum state sharing of an arbitrary unknown two-qubit state. In this scheme, the sender transmits the two-qubit secret state to three agents who are divided into two grades with two Bell-state measurements,and broadcasts the measurement results via a classical channel. One agent is in the upper grade and two agents are in the lower grade. The agent in the upper grade only needs to cooperate with one of the other two agents to recover the secret state but both of the agents in the lower grade need help from all of the agents. Every agent who wants to recover the secret state needs to introduce two ancillary qubits and performs a positive operator-valued measurement（POVM） instead of the usual projective measurement. Moreover, due to the symmetry of the cluster state, we extend this protocol to multiparty agents.
文摘Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
基金supported by the National Natural Science Foundation of China(Grant No.61170219)the Joint Research Foundation of the Ministry of Education of the People’s Republic of China and China Mobile(Grant No.MCM20150202)the Science and Technology Project Affiliated to Chongqing Education Commission(KJ1602201)
文摘How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030,60701015, and 60736029
文摘In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including k-means, fuzzy c-means, and hierarchical clustering have been widely used in literatures. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods, specially, in yeast saccharomyces cerevisiae. In this paper, these three gene clustering methods are compared. Classification accuracy and CPU time cost are employed for measuring performance of these algorithms. Our results show that hierarchical clustering outperforms k-means and fuzzy c-means clustering. The analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis of gene expression.
文摘A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.
文摘传统的数据挖掘方法一般从组方中所有的药物出发,挖掘药物的用药规律,计算量大,且仅仅基于药物频次对组方进行研究,忽略了药物剂量因素,难以发现频次低但剂量占比高的具有良好疗效的药物。针对以上问题,提出一种改进的基于效用度(Effect Degree,ED)核心药物发现算法,并将基于效用度的点式互信息(Pointwise Mutual Information with Herb Pair ED,PMIED)与节点度结合,定义一种新的加权相关系数作为药物权重,在所发现的核心药物中运用层次聚类算法研究用药规律。实验结果表明,该算法可有效挖掘出组方中的核心药物,经过分析,所发现的核心药物和药物组合均对痰瘀互阻证具有良好疗效。
文摘实体关系联合抽取作为构建知识图谱的核心环节,旨在从非结构化文本中提取实体-关系三元组。针对现有联合抽取方法在解码时未能有效处理实体关系间的相互作用,导致对语境理解不足,产生冗余信息等问题,提出一种基于并行解码和聚类的实体关系联合抽取模型。首先,利用BERT(bidirectional encoder representations from transformers)模型进行文本编码,获取语义信息丰富的字符向量。其次,采用非自回归并行解码器增强实体关系间的交互,并引入层次凝聚聚类算法及多数投票机制进一步优化解码结果以捕获语境信息,减少冗余信息。最后,生成高质量的三元组集合,以构建课程知识图谱。为评估该方法的性能,在公共数据集NYT和WebNLG以及自建C语言数据集上进行实验,结果表明,该方法在精确率和F1值上优于其他对比模型。
