Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined compo...Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.展开更多
This paper studies the multiscale entropy （MSE） of electrocardiogram＇s ST segment and compares the MSE results of ST segment with that of electrocardiogram in the first time. Electrocardiogram complexity changing c...This paper studies the multiscale entropy （MSE） of electrocardiogram＇s ST segment and compares the MSE results of ST segment with that of electrocardiogram in the first time. Electrocardiogram complexity changing characteristics has important clinical significance for early diagnosis. Study shows that the average MSE values and the varying scope fluctuation could be more effective to reveal the heart health status. Particularly the multiscale values varying scope fluctuation is a more sensitive parameter for early heart disease detection and has a clinical diagnostic significance.展开更多
Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time s...Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time scales can lead to alterations in postural center of pressure(Co P)fluctuations.Multiscale entropy(MSE) has been used to identify differences in fluctuations of postural Co P time series between groups with and without known physiological impairments at multiple time scales.The purpose of this paper is to:1) review basic elements and current developments in entropy techniques used to assess physiological complexity;and 2) identify how MSE can provide insights into the complexity of physiological systems operating at multiple time scales that underlie the control of posture.We review and synthesize evidence from the literature providing support for MSE as a valuable tool to evaluate the breakdown in the physiological processes that accompany changes due to aging and disease in postural control.This evidence emerges from observed lower MSE values in individuals with multiple sclerosis,idiopathic scoliosis,and in older individuals with sensory impairments.Finally,we suggest some future applications of MSE that will allow for further insight into how physiological deficits impact the complexity of postural fluctuations;this information may improve the development and evaluation of new therapeutic interventions.展开更多
文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用...文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用信息贡献率方法对不同尺度熵值作加权,全面、准确、可靠地评估区域降水复杂性。此外,基于黑龙江省旬降水复杂性测度结果,探索影响黑龙江省降水复杂性潜在因素。结果表明,黑龙江省旬降水复杂性呈现西部低东部高的显著空间分布特征。此外,水域面积和城建面积与降水复杂性测度结果相关系数分别为-0.629和0.451,存在显著相关关系。为分析模型性能,引入蝗虫优化算法改进多尺度熵模型(The multiscale entropy based on grasshopper optimization algorithm,GOA-MSE),可知GOA-RCMSE区分度和Spearman等级相关系数分别为1.1141和0.995,而GOA-MSE区分度和Spearman等级相关系数分别为1.0935和0.973,表明GOARCMSE具备更高的可靠性和稳定性。综上,GOA-RCMSE可全面合理评价区域降水复杂性,同时为不同区域解决降水复杂性测度问题提供新思路。展开更多
The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete ra...The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.展开更多
针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(sin...针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。展开更多
基金Projects(City U 11201315,T32-101/15-R)supported by the Research Grants Council of the Hong Kong Special Administrative Region,China
文摘Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.
基金Project supported by the National Science Foundation (Grant No 60501003)Jiangsu Province University Science Research Guidance Plans (Grant No 06KJD510138)support from Jiangsu Province Cyan projects (Grant No TJ207016)
文摘This paper studies the multiscale entropy （MSE） of electrocardiogram＇s ST segment and compares the MSE results of ST segment with that of electrocardiogram in the first time. Electrocardiogram complexity changing characteristics has important clinical significance for early diagnosis. Study shows that the average MSE values and the varying scope fluctuation could be more effective to reveal the heart health status. Particularly the multiscale values varying scope fluctuation is a more sensitive parameter for early heart disease detection and has a clinical diagnostic significance.
文摘Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time scales can lead to alterations in postural center of pressure(Co P)fluctuations.Multiscale entropy(MSE) has been used to identify differences in fluctuations of postural Co P time series between groups with and without known physiological impairments at multiple time scales.The purpose of this paper is to:1) review basic elements and current developments in entropy techniques used to assess physiological complexity;and 2) identify how MSE can provide insights into the complexity of physiological systems operating at multiple time scales that underlie the control of posture.We review and synthesize evidence from the literature providing support for MSE as a valuable tool to evaluate the breakdown in the physiological processes that accompany changes due to aging and disease in postural control.This evidence emerges from observed lower MSE values in individuals with multiple sclerosis,idiopathic scoliosis,and in older individuals with sensory impairments.Finally,we suggest some future applications of MSE that will allow for further insight into how physiological deficits impact the complexity of postural fluctuations;this information may improve the development and evaluation of new therapeutic interventions.
文摘文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用信息贡献率方法对不同尺度熵值作加权,全面、准确、可靠地评估区域降水复杂性。此外,基于黑龙江省旬降水复杂性测度结果,探索影响黑龙江省降水复杂性潜在因素。结果表明,黑龙江省旬降水复杂性呈现西部低东部高的显著空间分布特征。此外,水域面积和城建面积与降水复杂性测度结果相关系数分别为-0.629和0.451,存在显著相关关系。为分析模型性能,引入蝗虫优化算法改进多尺度熵模型(The multiscale entropy based on grasshopper optimization algorithm,GOA-MSE),可知GOA-RCMSE区分度和Spearman等级相关系数分别为1.1141和0.995,而GOA-MSE区分度和Spearman等级相关系数分别为1.0935和0.973,表明GOARCMSE具备更高的可靠性和稳定性。综上,GOA-RCMSE可全面合理评价区域降水复杂性,同时为不同区域解决降水复杂性测度问题提供新思路。
文摘The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.
文摘针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。
