针对滚动球轴承振动加速度信号特征提取问题,提出一种基于中心对称局部二值模式(center-symmetric local binary pattern,简称CSLBP)的时频特征提取方法。首先,利用广义S变换对滚动球轴承振动加速度信号进行处理,通过采用时频聚集性度...针对滚动球轴承振动加速度信号特征提取问题,提出一种基于中心对称局部二值模式(center-symmetric local binary pattern,简称CSLBP)的时频特征提取方法。首先,利用广义S变换对滚动球轴承振动加速度信号进行处理,通过采用时频聚集性度量准则自适应地确定广义S变换的调整参数,从而获取时频分辨性较好的二维时频图;然后,计算二维时频图的CSLBP,提取CSLBP纹理谱描述滚动球轴承振动加速度信号的时频特征。对滚动球轴承正常、外圈故障、内圈故障和滚动体故障4种不同状态的振动加速度信号进行了研究。结果表明,CSLBP纹理谱能有效地表达滚动球轴承振动加速度信号的时频特征,与局部二值模式(local binary pattern,简称LBP)和统一模式LBP纹理谱相比,CSLBP纹理谱具有特征维数低和区分性能好的优点。展开更多
拉波夫的叙事分析模式包括点题、指向、进展、评议、结果或结局和回应六个部分。本文从语篇分析教学的角度,运用拉波夫的叙事分析模式对《Face to Face with Guns》一文的叙事结构进行了讨论,指出该分析模式在语篇分析教学中对读写教学...拉波夫的叙事分析模式包括点题、指向、进展、评议、结果或结局和回应六个部分。本文从语篇分析教学的角度,运用拉波夫的叙事分析模式对《Face to Face with Guns》一文的叙事结构进行了讨论,指出该分析模式在语篇分析教学中对读写教学具有一定的指导意义。展开更多
Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal pro...Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.展开更多
Labov的语篇分析模式为叙事语篇的分析提供了具有指导意义的操作方法。他所提出的分析模式包括点题、指向、进展、评议、结局和回应六个部分。结合这一模式分析Cat in the Rain一文的叙事结构,探讨该语篇的特点,以期进一步了解作者谋篇...Labov的语篇分析模式为叙事语篇的分析提供了具有指导意义的操作方法。他所提出的分析模式包括点题、指向、进展、评议、结局和回应六个部分。结合这一模式分析Cat in the Rain一文的叙事结构,探讨该语篇的特点,以期进一步了解作者谋篇布局的超结构和写作意图。展开更多
基金This work was supported by National Natural Science Foundation of China(52076087)the Ministry of Science and Technology of the People’s Republic of China(2017YFE0100600)Wuhan City Science and Technology Program(2020010601012197).
文摘Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.
