A more efficient permutation algorithm which has less computer operation and better structure is presented here for radix-2 FFT(FHT). It can fasten the FFT and FHT efficiently when N becomes large.
随着光伏发电系统大规模接入电网,不可避免地带来了严重的谐波污染问题。为了有效监测光伏并网系统输出电流的谐波、间谐波,提出了一种基于改进快速最小二乘法-旋转不变法(total least squares-estimation of signal parameters via rot...随着光伏发电系统大规模接入电网,不可避免地带来了严重的谐波污染问题。为了有效监测光伏并网系统输出电流的谐波、间谐波,提出了一种基于改进快速最小二乘法-旋转不变法(total least squares-estimation of signal parameters via rotational invariance technique,TLS-ESPRIT)与2阶Blackman-Harris自卷积窗相结合的检测方法。首先对待测信号进行三次采样并利用快速TLS-ESPRIT算法检测频率。随后对检测结果基于简化K-means聚类算法进行分析,提取出真实的谐波分量。最后结合2阶Blackman-Harris自卷积窗对信号进行加窗插值计算,准确估算出其幅值、相位信息,实现了谐波、间谐波的高精度检测。仿真算例和现场数据测试结果表明,所提方法相较于传统方法具有更高的谐波、间谐波检测精度,且抗干扰能力更强。展开更多
In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidential...In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems.展开更多
Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment.For this purpose,different classification methods based on data,experts’knowledge...Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment.For this purpose,different classification methods based on data,experts’knowledge and both are considered in some cases.Besides,feature reduction and some clustering methods are used to improve their performance.Design/methodology/approach-First,the performances of classification methods are evaluated for differential diagnosis of different diseases.Then,experts’knowledge is utilized to modify the Bayesian networks’structures.Analyses of the results show that using experts’knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification.A total of ten different diseases are used for testing,taken from the Machine Learning Repository datasets of the University of California at Irvine(UCI).Findings-The proposed method improves both the computation time and accuracy of the classification methods used in this paper.Bayesian networks based on experts’knowledge achieve a maximum average accuracy of 87 percent,with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implications-The proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/value-This study presents the usefulness of experts’knowledge in the diagnosis while proposing an adopted improvement method for classifications.Besides,the Bayesian network based on experts’knowledge is useful for different diseases neglected by previous papers.展开更多
摘要A more efficient permutation algorithm which has less computer operation and better structure is presented here for radix-2 FFT(FHT). It can fasten the FFT and FHT efficiently when N becomes large.
摘要随着光伏发电系统大规模接入电网,不可避免地带来了严重的谐波污染问题。为了有效监测光伏并网系统输出电流的谐波、间谐波,提出了一种基于改进快速最小二乘法-旋转不变法(total least squares-estimation of signal parameters via rotational invariance technique,TLS-ESPRIT)与2阶Blackman-Harris自卷积窗相结合的检测方法。首先对待测信号进行三次采样并利用快速TLS-ESPRIT算法检测频率。随后对检测结果基于简化K-means聚类算法进行分析,提取出真实的谐波分量。最后结合2阶Blackman-Harris自卷积窗对信号进行加窗插值计算,准确估算出其幅值、相位信息,实现了谐波、间谐波的高精度检测。仿真算例和现场数据测试结果表明,所提方法相较于传统方法具有更高的谐波、间谐波检测精度,且抗干扰能力更强。
摘要In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems.
摘要Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment.For this purpose,different classification methods based on data,experts’knowledge and both are considered in some cases.Besides,feature reduction and some clustering methods are used to improve their performance.Design/methodology/approach-First,the performances of classification methods are evaluated for differential diagnosis of different diseases.Then,experts’knowledge is utilized to modify the Bayesian networks’structures.Analyses of the results show that using experts’knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification.A total of ten different diseases are used for testing,taken from the Machine Learning Repository datasets of the University of California at Irvine(UCI).Findings-The proposed method improves both the computation time and accuracy of the classification methods used in this paper.Bayesian networks based on experts’knowledge achieve a maximum average accuracy of 87 percent,with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implications-The proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/value-This study presents the usefulness of experts’knowledge in the diagnosis while proposing an adopted improvement method for classifications.Besides,the Bayesian network based on experts’knowledge is useful for different diseases neglected by previous papers.