[Objective] This paper aims to construct an improved fuzzy decision tree which is based on clustering,and researches into its application in the screening of maize germplasm.[Method] A new decision tree algorithm base...[Objective] This paper aims to construct an improved fuzzy decision tree which is based on clustering,and researches into its application in the screening of maize germplasm.[Method] A new decision tree algorithm based upon clustering is adopted in this paper,which is improved against the defect that traditional decision tree algorithm fails to handle samples of no classes.Meanwhile,the improved algorithm is also applied to the screening of maize varieties.Through the indices as leaf area,plant height,dry weight,potassium（K） utilization and others,maize seeds with strong tolerance of hypokalemic are filtered out.[Result] The algorithm in the screening of maize germplasm has great applicability and good performance.[Conclusion] In the future more efforts should be made to compare improved the performance of fuzzy decision tree based upon clustering with the performance of traditional fuzzy one,and it should be applied into more realistic problems.展开更多
The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development o...The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks,the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers,but rather a large group of professional people in practice.The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable.However,the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers.Given this background,a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers.Group decision-making preferences are obtained by clustering analysis,and the final black-start decision-making results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case.This work extends the black-start decision-making problem to situations with a large group of decision-makers.展开更多
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study pres...Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.展开更多
According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferen...According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.展开更多
Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)oper...Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.展开更多
Integration interval and decision threshold issues were investigated for improved transmitted reference pulse cluster （iTRPC-） ultra-wideband （UWB） systems. Our analysis shows that the bit error rate （BER） perfo...Integration interval and decision threshold issues were investigated for improved transmitted reference pulse cluster （iTRPC-） ultra-wideband （UWB） systems. Our analysis shows that the bit error rate （BER） performance of iTRPC-UWB systems can be significantly improved via integration interval determination （IID） and decision threshold optimization. For this purpose, two modifications can be made at the autocorrelation receiver as follows. Firstly, the liD processing is performed for autocorrelation operation to capture multi-path energy as much as possible. Secondly, adaptive decision threshold （ADT） instead of zero decision threshold （ZDT）, is used as estimated optimal decision threshold for symbol detection. Performance of iTRPCUWB systems using liD and ADT was evaluated in realistic IEEE 802.15.4a UWB channel models and the simulation results demonstrated our theoretical analysis.展开更多
The level of present understanding of earthquake prediction of seismologists at home and abroad is very different. This is because China has opened up a special path of earthquake prediction research that has not been...The level of present understanding of earthquake prediction of seismologists at home and abroad is very different. This is because China has opened up a special path of earthquake prediction research that has not been explored by other countries, with its own advantages and potentialities.Therefore, we considered that it is the most practical way to use the advantages and potentialities for raising the earthquake prediction level. For this purpose, we have developed a set of intelligent decision support system for earthquake prediction, with the analysis of cluster anomalies process at the core. The facts show that it can obviously raise the level of synthetic earthquake prediction.展开更多
The paper study improved K-means algorithm and establish indicators to classify customers according to RFM model. Experimental results show that, the new algorithm has good convergence and stability, it has better tha...The paper study improved K-means algorithm and establish indicators to classify customers according to RFM model. Experimental results show that, the new algorithm has good convergence and stability, it has better than single use of FKP algorithms for clustering. Finally the paper study the application of clustering in customer segmentation of mobile communication enterprise. It discusses the basic theory, customer segmentation methods and steps, the customer segmentation model based on consumption behavior psychology, and the segmentation model is successfully applied to the process of marketing decision support.展开更多
This paper deals with the types and specifications of combing roller covering for spinning pureramie noil rotor-spun yarns.A handling mode combining Fuzzy Decision-making and FuzzyCluster Analysis has been used for an...This paper deals with the types and specifications of combing roller covering for spinning pureramie noil rotor-spun yarns.A handling mode combining Fuzzy Decision-making and FuzzyCluster Analysis has been used for analyzing the experimental results.It is shown that,with regard to the specifications of the sawtooth clothing of the combing rol-ler,large working angle,large tooth pitch,fine tooth shape,short tooth height,smooth finish andgood wearability are of benefit to improving the spinning stability and the spun yarn properties.The pinned combing roller,however,regardless of its complicated process of production,is sug-gested to be preferred for spinning the pure ramie noil rotor-spun yarns.The handling mode used in this work is efficient in improving the reliability and objectivity ofthe conclusions and can be used for solving the similar problems.展开更多
This paper analyzes users’ trust decision patterns for detecting phishing sites. Our previous work proposed HumanBoost [1] which improves the accuracy of detecting phishing sites by using users’ Past Trust Decisions...This paper analyzes users’ trust decision patterns for detecting phishing sites. Our previous work proposed HumanBoost [1] which improves the accuracy of detecting phishing sites by using users’ Past Trust Decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. Human-Boostassumed that a database of Web user’s PTD would be transformed into a binary vector, representing phishing or not-phishing, and the binary vector can be used for detecting phishing sites, similar to the existing heuristics. Here, this paper explores the types of the users whose PTDs are useful by running a subject experiment, where 309 participants- browsed 40 websites, judged whether the site appeared to be a phishing site, and described the criterion while assessing the credibility of the site. Based on the result of the experiment, this paper classifies the participants into eight groups by clustering approach and evaluates the detection accuracy for each group. It then clarifies the types of the users who can make suitable trust decisions for HumanBoost.展开更多
Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust the...Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust their predictions in practical applications.One important and interesting question remains unanswered:is it possible to use intrinsically interpretable models to achieve accuracy comparable to that of black-box models?With an aim of answering this question,this study proposes an intrinsically interpretable machine learning-based method to forecast building energy loads.It creatively combines two intrinsically interpretable machine learning algorithms:clustering decision trees and adaptive multiple linear regression.Clustering decision trees aim to automatically identify various building operation conditions,allowing for the training of multiple models tailored to each condition.It can reduce the complexity of model training data,leading to higher accuracy.Adaptive multiple linear regression is an improved regression algorithm tailored to building energy load prediction.It can adaptively modify regression coefficients according to building operations,enhancing the non-linear fitting capability of multiple linear regression.The proposed method is evaluated utilizing the operational data from an office building.The results indicate that the proposed method exhibits comparable accuracy to both random forests and extreme gradient boosting.Furthermore,it shows significantly superior accuracy,with an average improvement of 10.2%,compared with some popular black-box algorithms such as artificial neural networks,support vector regression,and classification and regression trees.As for model interpretability,the proposed method reveals that historical cooling loads are the most crucial for predicting building cooling loads under most conditions.Additionally,outdoor air temperature has a significant contribution to building cooling load prediction during the daytime on weekdays in summer and transition seasons.In the future,it will be valuable to explore integrating the laws of physics into the proposed method to further enhance its interpretability.展开更多
储能是促进新能源消纳的重要手段,其面向收益的运行决策受多种因素制约。针对储能电站与风电集群合作运行决策与收益分配问题,该文提出了基于纳什谈判理论的储能电站与风电集群合作运行日前-实时决策优化方法。结合储能电站与风电集群...储能是促进新能源消纳的重要手段,其面向收益的运行决策受多种因素制约。针对储能电站与风电集群合作运行决策与收益分配问题,该文提出了基于纳什谈判理论的储能电站与风电集群合作运行日前-实时决策优化方法。结合储能电站与风电集群独立运行模型计算纳什谈判破裂点,日前阶段构建基于纳什谈判理论的储能电站与风电集群合作运行模型,并将其等效转化为联盟效益最大化和收益分配两个子模型,分别求解联盟最优运行决策与收益分配问题,风电集群中各风电场收益采用Shapley值法进行分配。实时阶段根据储能电站实时荷电状态(state of charge,SOC)与风电超短期功率预测结果滚动优化联盟运行决策。风电集群与储能合作运行算例分析表明,储能和风电集群在合作运行后收益分别提升7.23%和1.45%,验证了所提方法的有效性,能够在保证各主体参与公平性和收入分配合理性的同时,实现联盟整体收益的最大化。展开更多
文摘[Objective] This paper aims to construct an improved fuzzy decision tree which is based on clustering,and researches into its application in the screening of maize germplasm.[Method] A new decision tree algorithm based upon clustering is adopted in this paper,which is improved against the defect that traditional decision tree algorithm fails to handle samples of no classes.Meanwhile,the improved algorithm is also applied to the screening of maize varieties.Through the indices as leaf area,plant height,dry weight,potassium（K） utilization and others,maize seeds with strong tolerance of hypokalemic are filtered out.[Result] The algorithm in the screening of maize germplasm has great applicability and good performance.[Conclusion] In the future more efforts should be made to compare improved the performance of fuzzy decision tree based upon clustering with the performance of traditional fuzzy one,and it should be applied into more realistic problems.
基金supported by National Natural Science Foundation of China (No.51007080)National High Technology Research and Development Program of China (863 Program) (No.2011AA05A105)+1 种基金the Fundamental Research Funds for the Central Universities (No.2012QNA4011)key project from Zhejiang Electric Power Corporation
文摘The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks,the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers,but rather a large group of professional people in practice.The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable.However,the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers.Given this background,a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers.Group decision-making preferences are obtained by clustering analysis,and the final black-start decision-making results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case.This work extends the black-start decision-making problem to situations with a large group of decision-makers.
基金This research is funded by the National Natural Science Foundation of China(Grant Nos.41807285 and 51679117)Key Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(SKLGP2019Z002)+3 种基金the National Science Foundation of Jiangxi Province,China(20192BAB216034)the China Postdoctoral Science Foundation(2019M652287 and 2020T130274)the Jiangxi Provincial Postdoctoral Science Foundation(2019KY08)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)。
文摘Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.
文摘According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ50047,2023JJ40306)the Research Foundation of Education Bureau of Hunan Province(23A0494,20B260)the Key R&D Projects of Hunan Province(2019SK2331)。
文摘Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.
基金supported in part by the National Natural Science Foundation of China under Grant 61271262,61473047 and 61572083in part by Shaanxi Provincial Natural Science Foundation under Grant 2015JM6310in part by the Special Fund for Basic Scientific Research of Central Colleges,Chang’an University 310824152010 and 0009-2014G1241043
文摘Integration interval and decision threshold issues were investigated for improved transmitted reference pulse cluster （iTRPC-） ultra-wideband （UWB） systems. Our analysis shows that the bit error rate （BER） performance of iTRPC-UWB systems can be significantly improved via integration interval determination （IID） and decision threshold optimization. For this purpose, two modifications can be made at the autocorrelation receiver as follows. Firstly, the liD processing is performed for autocorrelation operation to capture multi-path energy as much as possible. Secondly, adaptive decision threshold （ADT） instead of zero decision threshold （ZDT）, is used as estimated optimal decision threshold for symbol detection. Performance of iTRPCUWB systems using liD and ADT was evaluated in realistic IEEE 802.15.4a UWB channel models and the simulation results demonstrated our theoretical analysis.
基金This research is one of the key projects No. 863-306-04-03-4 in the State High Science-Technology Program (Program 863), China.
文摘The level of present understanding of earthquake prediction of seismologists at home and abroad is very different. This is because China has opened up a special path of earthquake prediction research that has not been explored by other countries, with its own advantages and potentialities.Therefore, we considered that it is the most practical way to use the advantages and potentialities for raising the earthquake prediction level. For this purpose, we have developed a set of intelligent decision support system for earthquake prediction, with the analysis of cluster anomalies process at the core. The facts show that it can obviously raise the level of synthetic earthquake prediction.
文摘The paper study improved K-means algorithm and establish indicators to classify customers according to RFM model. Experimental results show that, the new algorithm has good convergence and stability, it has better than single use of FKP algorithms for clustering. Finally the paper study the application of clustering in customer segmentation of mobile communication enterprise. It discusses the basic theory, customer segmentation methods and steps, the customer segmentation model based on consumption behavior psychology, and the segmentation model is successfully applied to the process of marketing decision support.
文摘This paper deals with the types and specifications of combing roller covering for spinning pureramie noil rotor-spun yarns.A handling mode combining Fuzzy Decision-making and FuzzyCluster Analysis has been used for analyzing the experimental results.It is shown that,with regard to the specifications of the sawtooth clothing of the combing rol-ler,large working angle,large tooth pitch,fine tooth shape,short tooth height,smooth finish andgood wearability are of benefit to improving the spinning stability and the spun yarn properties.The pinned combing roller,however,regardless of its complicated process of production,is sug-gested to be preferred for spinning the pure ramie noil rotor-spun yarns.The handling mode used in this work is efficient in improving the reliability and objectivity ofthe conclusions and can be used for solving the similar problems.
文摘This paper analyzes users’ trust decision patterns for detecting phishing sites. Our previous work proposed HumanBoost [1] which improves the accuracy of detecting phishing sites by using users’ Past Trust Decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. Human-Boostassumed that a database of Web user’s PTD would be transformed into a binary vector, representing phishing or not-phishing, and the binary vector can be used for detecting phishing sites, similar to the existing heuristics. Here, this paper explores the types of the users whose PTDs are useful by running a subject experiment, where 309 participants- browsed 40 websites, judged whether the site appeared to be a phishing site, and described the criterion while assessing the credibility of the site. Based on the result of the experiment, this paper classifies the participants into eight groups by clustering approach and evaluates the detection accuracy for each group. It then clarifies the types of the users who can make suitable trust decisions for HumanBoost.
基金supported by the National Natural Science Foundation of China(No.52161135202).
文摘Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust their predictions in practical applications.One important and interesting question remains unanswered:is it possible to use intrinsically interpretable models to achieve accuracy comparable to that of black-box models?With an aim of answering this question,this study proposes an intrinsically interpretable machine learning-based method to forecast building energy loads.It creatively combines two intrinsically interpretable machine learning algorithms:clustering decision trees and adaptive multiple linear regression.Clustering decision trees aim to automatically identify various building operation conditions,allowing for the training of multiple models tailored to each condition.It can reduce the complexity of model training data,leading to higher accuracy.Adaptive multiple linear regression is an improved regression algorithm tailored to building energy load prediction.It can adaptively modify regression coefficients according to building operations,enhancing the non-linear fitting capability of multiple linear regression.The proposed method is evaluated utilizing the operational data from an office building.The results indicate that the proposed method exhibits comparable accuracy to both random forests and extreme gradient boosting.Furthermore,it shows significantly superior accuracy,with an average improvement of 10.2%,compared with some popular black-box algorithms such as artificial neural networks,support vector regression,and classification and regression trees.As for model interpretability,the proposed method reveals that historical cooling loads are the most crucial for predicting building cooling loads under most conditions.Additionally,outdoor air temperature has a significant contribution to building cooling load prediction during the daytime on weekdays in summer and transition seasons.In the future,it will be valuable to explore integrating the laws of physics into the proposed method to further enhance its interpretability.
文摘储能是促进新能源消纳的重要手段,其面向收益的运行决策受多种因素制约。针对储能电站与风电集群合作运行决策与收益分配问题,该文提出了基于纳什谈判理论的储能电站与风电集群合作运行日前-实时决策优化方法。结合储能电站与风电集群独立运行模型计算纳什谈判破裂点,日前阶段构建基于纳什谈判理论的储能电站与风电集群合作运行模型,并将其等效转化为联盟效益最大化和收益分配两个子模型,分别求解联盟最优运行决策与收益分配问题,风电集群中各风电场收益采用Shapley值法进行分配。实时阶段根据储能电站实时荷电状态(state of charge,SOC)与风电超短期功率预测结果滚动优化联盟运行决策。风电集群与储能合作运行算例分析表明,储能和风电集群在合作运行后收益分别提升7.23%和1.45%,验证了所提方法的有效性,能够在保证各主体参与公平性和收入分配合理性的同时,实现联盟整体收益的最大化。
