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
Analysis of multiscale entropy(MSE) and multiscale standard deviation(MSD) are performed for both the heart rate interval series and the interval increment series.For the interval series,it is found that,it is impract...Analysis of multiscale entropy(MSE) and multiscale standard deviation(MSD) are performed for both the heart rate interval series and the interval increment series.For the interval series,it is found that,it is impractical to discriminate the diseases of atrial fibrillation(AF) and congestive heart failure(CHF) unambiguously from the healthy.A clear discrimination from the healthy,both young and old,however,can be made in the MSE analysis of the increment series where we find that both CHF and AF sufferers have significantly low MSE values in the whole range of time scales investigated,which reveals that there are common dynamic characteristics underlying these two different diseases.In addition,we propose the sample entropy(SE) corresponding to time scale factor 4 of increment series as a diag-nosis index of both AF and CHF,and the reference threshold is recommended.Further indication that this index can help discriminate sensitively the mild heart failure(cardiac function classes 1 and 2) from the healthy gives a clue to early clinic diagnosis of CHF.展开更多
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。
基金Supported by the National Natural Science Foundation of China (Grant No. 60701002
文摘Analysis of multiscale entropy(MSE) and multiscale standard deviation(MSD) are performed for both the heart rate interval series and the interval increment series.For the interval series,it is found that,it is impractical to discriminate the diseases of atrial fibrillation(AF) and congestive heart failure(CHF) unambiguously from the healthy.A clear discrimination from the healthy,both young and old,however,can be made in the MSE analysis of the increment series where we find that both CHF and AF sufferers have significantly low MSE values in the whole range of time scales investigated,which reveals that there are common dynamic characteristics underlying these two different diseases.In addition,we propose the sample entropy(SE) corresponding to time scale factor 4 of increment series as a diag-nosis index of both AF and CHF,and the reference threshold is recommended.Further indication that this index can help discriminate sensitively the mild heart failure(cardiac function classes 1 and 2) from the healthy gives a clue to early clinic diagnosis of CHF.
