为实现滚动轴承故障特征分析,提出了一种基于小波包变换(Wavelet Packet Transform,WPT)结合随机森林(Random Forests,RF)的滚动轴承故障分析模型。首先,采用小波包变换对振动信号进行分解,对终端节点进行重构,再计算重构信号及其希尔...为实现滚动轴承故障特征分析,提出了一种基于小波包变换(Wavelet Packet Transform,WPT)结合随机森林(Random Forests,RF)的滚动轴承故障分析模型。首先,采用小波包变换对振动信号进行分解,对终端节点进行重构,再计算重构信号及其希尔伯特边际谱的11种统计参数,得到统计特征,构建原始特征集;针对原始特征集中存在的冗余和干扰特征,提出一种基于平均精确率减少的特征选择方法(Features Selection base on Mean Decrease Accuracy,FSMDA),标记特征对轴承故障的重要度,选取重要度高的统计特征用于故障状态识别;最后,利用随机森林实现滚动轴承故障特征分析与状态识别。采用12种轴承故障状态数据进行实验分析,实验结果表明FSMDA能够选择出对故障状态较为重要的特征,提高故障状态识别准确率,并且具有较好的适应性。展开更多
Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin,Northwest China.Using species distribution models to predict the biogeographical distribution of species is regularly used in c...Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin,Northwest China.Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities.However,the uncertainty in the data and models inevitably reduces their prediction power.The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T.ramosissima distribution,to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T.ramosissima in this basin.Methods Three models—the generalized linear model(GLM),classification and regression tree(CART)and Random Forests—were selected and were processed on the BIOMOD platform.The presence/absence data of T.ramosissima in the Tarim Basin,which were calculated from vegetation maps,were used as response variables.Climate,soil and digital elevation model(DEM)data variables were divided into four datasets and then used as predictors.The four datasets were(i)climate variables,(ii)soil,climate and DEM variables,(iii)principal component analysis(PCA)-based climate variables and(iv)PCA-based soil,climate and DEM variables.Important Findings The results indicate that predictive variables for species distribution models should be chosen carefully,because too many predictors can reduce the prediction power.The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models.Our results implied that it is better to reduce the correlating predictors before model processing.The Random Forests model was more precise than the GLM and CART models.The best model for T.ramosissima was the Random Forests model with climate predictors alone.Soil variables considered in this study could not significantly improve the model’s prediction accuracy for T.ramosissima.The potential distribution area of 展开更多
Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, ...Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data.Methods Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs,multilocus sequence typing(MLST), and polymorphism trees were analyzed using whole-genome sequencing data(WGS).Results This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal(49/70) and healthy groups(15/24).Conclusion We developed a random forest(RF) prediction model of TEM.1 + baeR + mphA + mphB +QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.展开更多
文摘为实现滚动轴承故障特征分析,提出了一种基于小波包变换(Wavelet Packet Transform,WPT)结合随机森林(Random Forests,RF)的滚动轴承故障分析模型。首先,采用小波包变换对振动信号进行分解,对终端节点进行重构,再计算重构信号及其希尔伯特边际谱的11种统计参数,得到统计特征,构建原始特征集;针对原始特征集中存在的冗余和干扰特征,提出一种基于平均精确率减少的特征选择方法(Features Selection base on Mean Decrease Accuracy,FSMDA),标记特征对轴承故障的重要度,选取重要度高的统计特征用于故障状态识别;最后,利用随机森林实现滚动轴承故障特征分析与状态识别。采用12种轴承故障状态数据进行实验分析,实验结果表明FSMDA能够选择出对故障状态较为重要的特征,提高故障状态识别准确率,并且具有较好的适应性。
基金National Basic Research Program of China(973 Program)(No.2010CB951303 and No.2009CB421106).
文摘Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin,Northwest China.Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities.However,the uncertainty in the data and models inevitably reduces their prediction power.The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T.ramosissima distribution,to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T.ramosissima in this basin.Methods Three models—the generalized linear model(GLM),classification and regression tree(CART)and Random Forests—were selected and were processed on the BIOMOD platform.The presence/absence data of T.ramosissima in the Tarim Basin,which were calculated from vegetation maps,were used as response variables.Climate,soil and digital elevation model(DEM)data variables were divided into four datasets and then used as predictors.The four datasets were(i)climate variables,(ii)soil,climate and DEM variables,(iii)principal component analysis(PCA)-based climate variables and(iv)PCA-based soil,climate and DEM variables.Important Findings The results indicate that predictive variables for species distribution models should be chosen carefully,because too many predictors can reduce the prediction power.The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models.Our results implied that it is better to reduce the correlating predictors before model processing.The Random Forests model was more precise than the GLM and CART models.The best model for T.ramosissima was the Random Forests model with climate predictors alone.Soil variables considered in this study could not significantly improve the model’s prediction accuracy for T.ramosissima.The potential distribution area of
基金funded by the National Pathogen Identification Network project and Research on Key Technologies of Intelligent Monitoring,Early Warning and Tracing of Infectious Diseases in Miyun。
文摘Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data.Methods Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs,multilocus sequence typing(MLST), and polymorphism trees were analyzed using whole-genome sequencing data(WGS).Results This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal(49/70) and healthy groups(15/24).Conclusion We developed a random forest(RF) prediction model of TEM.1 + baeR + mphA + mphB +QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.
