提出了一种基于多重信号分类(multiple signal classification,MUSIC)与模式搜索算法(pattern search algorithm,PSA)的异步电动机转子断条故障检测新方法。MUSIC方法对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以...提出了一种基于多重信号分类(multiple signal classification,MUSIC)与模式搜索算法(pattern search algorithm,PSA)的异步电动机转子断条故障检测新方法。MUSIC方法对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以及其他分量的频率;但对诸频率分量幅值和初相角则无法准确求解。因此引入PSA确定诸频率分量的幅值、初相角,并对1台Y100L-2型3 kW笼型异步电动机完成了转子断条故障检测实验。实验结果表明:基于MUSIC与PSA的异步电动机转子断条故障检测方法切实可行,适用于负荷波动、噪声等干扰严重情况。展开更多
Background Constipation is a common problem in children with spastic cerebral palsy(sCP)with a prevalence that reaches 75%.We hypothesized that treating constipation in those children will improve their health and sho...Background Constipation is a common problem in children with spastic cerebral palsy(sCP)with a prevalence that reaches 75%.We hypothesized that treating constipation in those children will improve their health and shorten time spent in daily care.Our aim was to evaluate the efficacy and safety of oral magnesium sulfate for treating chronic constipation in children with sCP.Methods A prospective,double-blinded randomized control trial was carried out involving 100 children aged 2-12 years with sCP(level Ⅲ-Ⅴ of the Gross Motor Functional Classification system)and chronic constipation.They were followed up in the Pediatric neurology clinic,Children's hospital,Ain Shams University,May 2017-January 2019.The intervention group(O-Mg)received oral magnesium sulfate 1 mL/kg/day daily for 1 month compared to the placebo.Outcome measures were constipation improvement and decrease in bowel evacuation time after 1 month.Results Initially,weekly bowel movements,constipation scores and stool consistency were comparable in both groups.After 1 month of regular administration of oral magnesium sulfate,the constipation score,stool frequency and consistency improved compared to the placebo group(P<0.001).Effective safe treatment was achieved in 31(68%)and 4(9.5%)patients in the O-Mg and placebo groups,respectively(RR,2.95;95%CI 2.0-4.5)(P<0.001).Painful bowel evacuation attempts spent by mothers decreased from 25(55.6%)of the cases initially to 10(22%)cases after one month in the O-Mg group(P=0.001).In contrast,in the placebo group,the decrease went from 21(50%)cases initially to 18(42.9%)after 1 month and was not significant(P=0.5).Conclusions Oral magnesium sulfate seems effective in alleviating chronic constipation and pain experience in children with sCP.Consequently,saving maternal time spent in daily bowel evacuation attempts.展开更多
Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,th...Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.展开更多
In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the di...In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.展开更多
文摘Background Constipation is a common problem in children with spastic cerebral palsy(sCP)with a prevalence that reaches 75%.We hypothesized that treating constipation in those children will improve their health and shorten time spent in daily care.Our aim was to evaluate the efficacy and safety of oral magnesium sulfate for treating chronic constipation in children with sCP.Methods A prospective,double-blinded randomized control trial was carried out involving 100 children aged 2-12 years with sCP(level Ⅲ-Ⅴ of the Gross Motor Functional Classification system)and chronic constipation.They were followed up in the Pediatric neurology clinic,Children's hospital,Ain Shams University,May 2017-January 2019.The intervention group(O-Mg)received oral magnesium sulfate 1 mL/kg/day daily for 1 month compared to the placebo.Outcome measures were constipation improvement and decrease in bowel evacuation time after 1 month.Results Initially,weekly bowel movements,constipation scores and stool consistency were comparable in both groups.After 1 month of regular administration of oral magnesium sulfate,the constipation score,stool frequency and consistency improved compared to the placebo group(P<0.001).Effective safe treatment was achieved in 31(68%)and 4(9.5%)patients in the O-Mg and placebo groups,respectively(RR,2.95;95%CI 2.0-4.5)(P<0.001).Painful bowel evacuation attempts spent by mothers decreased from 25(55.6%)of the cases initially to 10(22%)cases after one month in the O-Mg group(P=0.001).In contrast,in the placebo group,the decrease went from 21(50%)cases initially to 18(42.9%)after 1 month and was not significant(P=0.5).Conclusions Oral magnesium sulfate seems effective in alleviating chronic constipation and pain experience in children with sCP.Consequently,saving maternal time spent in daily bowel evacuation attempts.
基金Project supported by the National Natural Science Foundation of China(Nos.61702454 and 61772468)the MOE Project of Humanities and Social Sciences,China(No.17YJC870018)+1 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang Province,China(No.GB201901006)the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN260YB)
文摘Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.
基金This work was supported by the Natural Science Foundation of Jilin Province,China(20210101390JC).
文摘In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.
