Summer is the most beautiful season in northwest China’s Qinghai Province.Placid Qinghai Lake is nestled at the foot of mighty snowy mountains and complements golden cole flower fields as well as cattle,sheep,and bir...Summer is the most beautiful season in northwest China’s Qinghai Province.Placid Qinghai Lake is nestled at the foot of mighty snowy mountains and complements golden cole flower fields as well as cattle,sheep,and birds on the grassland.An achievement thanks to local ecological protection efforts,the beautiful scenery has become a strong driving force propelling the province’s high-quality development.展开更多
With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the ac...With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.展开更多
1.Background The Olympic motto,"Faster,Higher,Stronger-Together",represents the pursuit of athletic excellence,united as a global community."Excellence,respect and friendship"are the Olympic values...1.Background The Olympic motto,"Faster,Higher,Stronger-Together",represents the pursuit of athletic excellence,united as a global community."Excellence,respect and friendship"are the Olympic values.Both the motto and values are multifaceted and holistic in nature,and all-encompassing in their reach while recognizing no single entity rises above the others.Collectively,these foundational statements anchor the Olympic movement in its quest to build a better world through sport.展开更多
The continuous pursuit for a better quality of life promotes continuous advancements in intelligent technology.Flexible wearable and implantable bioelectronics have emerged as an innovative complement to rigid materia...The continuous pursuit for a better quality of life promotes continuous advancements in intelligent technology.Flexible wearable and implantable bioelectronics have emerged as an innovative complement to rigid material-based electronic devices[1-3].Due to their distinct advantages in terms of ductile,ultrathin,and biocompatible features,these elastic and soft bioelectronic devices can be seamlessly mounted onto various real or artificial tissues and organs.展开更多
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv...The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.展开更多
文摘Summer is the most beautiful season in northwest China’s Qinghai Province.Placid Qinghai Lake is nestled at the foot of mighty snowy mountains and complements golden cole flower fields as well as cattle,sheep,and birds on the grassland.An achievement thanks to local ecological protection efforts,the beautiful scenery has become a strong driving force propelling the province’s high-quality development.
基金the financial support of this work by the National Natural Science Foundation of Hebei Province China under Grant E2020208052.
文摘With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.
文摘1.Background The Olympic motto,"Faster,Higher,Stronger-Together",represents the pursuit of athletic excellence,united as a global community."Excellence,respect and friendship"are the Olympic values.Both the motto and values are multifaceted and holistic in nature,and all-encompassing in their reach while recognizing no single entity rises above the others.Collectively,these foundational statements anchor the Olympic movement in its quest to build a better world through sport.
文摘The continuous pursuit for a better quality of life promotes continuous advancements in intelligent technology.Flexible wearable and implantable bioelectronics have emerged as an innovative complement to rigid material-based electronic devices[1-3].Due to their distinct advantages in terms of ductile,ultrathin,and biocompatible features,these elastic and soft bioelectronic devices can be seamlessly mounted onto various real or artificial tissues and organs.
基金Supported by the National Science Foundation of China(42055402)。
文摘The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.
