Low-Frequency Characteristics of Persistent Abnormal Precipitation over the Jianghuai Region during 2000-2017

2019 ◽  
Vol 08 (06) ◽  
pp. 719-730
Author(s):  
颖 夏
Keyword(s):  
Low Frequency ◽  
Frequency Characteristics

scholarly journals Experimental Investigation on Time-Frequency Characteristics of Microseismic Signals in the Damage Evolution Process of Coal and Rock

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 809
Author(s):  
Wei Yang ◽  
Chengwu Li ◽  
Rui Xu ◽  
Xunchang Li
Keyword(s):  
Damage Evolution ◽  
Resonance Effect ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Evolution Process ◽  
Deformation And Failure ◽  
Time Frequency ◽  
Rock Materials ◽  
Damage Degree

The deformation and failure of coal and rock materials is the primary cause of many engineering disasters. How to accurately and effectively monitor and forecast the damage evolution process of coal and rock mass, and form a set of prediction methods and prediction indicators is an urgent engineering problems to be solved in the field of rock mechanics and engineering. As a form of energy dissipation in the deformation process of coal and rock, microseismic (MS) can indirectly reflect the damage of coal and rock. In order to analyze the relationship between the damage degree of coal and rock and time-frequency characteristics of MS, the deformation and fracture process of coal and rock materials under different loading modes was tested. The time-frequency characteristics and generation mechanism of MS were analyzed under different loading stages. Meanwhile, the influences of properties of coal and rock materials on MS signals were studied. Results show that there is an evident mode cutoff point between high-frequency and low-frequency MS signals. The properties of coal and rock, such as the development degree of the original fracture, particle size and dense degree have a decisive influence on the amplitude, frequency, energy and other characteristic parameters of MS signals. The change of MS parameters is closely related to material damage, but has no strong relation with the loading rate. The richness of MS signals before the main fracture depends on the homogeneity of materials. With the increase of damage, the energy release rate increases, which can lead to the widening of MS signals spectrum. The stiffness and natural frequency of specimens decreases correspondingly. Meanwhile, the main reason that the dominant frequency of MS detected by sensors installed on the surface of coal and rock materials is mainly low-frequency is friction loss and the resonance effect. In addition, the spectrum and energy evolution of MS can be used as a characterization method of the damage degree of coal and rock materials. Furthermore, the results can provide important reference for prediction and early warning of some rock engineering disasters.

Transfer of Learning in Transformed Random-Dot Stereostimuli

Perception ◽  
1982 ◽  
Vol 11 (4) ◽  
pp. 409-414 ◽  
Author(s):  
Nigel R Long
Keyword(s):  
Spatial Frequency ◽  
High Frequency ◽  
Transfer Of Learning ◽  
Low Frequency ◽  
Frequency Characteristics ◽  
Frequency Transformations

The transfer of learning between normal and monocularly-transformed small-disparity, random-dot stereostimuli has been examined under extended viewing conditions. When the disparity value was constant, transfer of learning between normal and monocularly-transformed stereostimuli was disrupted by both low-frequency and high-frequency transformations. These results suggest that stereolearning is restricted to disparity units that are selective to the same spatial-frequency characteristics.

Dynamic Response Characteristics of Underground Powerhouse Caverns for Sandaowan Hydropower Station

2011 ◽  
Vol 382 ◽  
pp. 80-83 ◽  
Author(s):  
Zhen Zhong Shen ◽  
Hua Chun Ren
Keyword(s):  
Seismic Waves ◽  
Low Frequency ◽  
Time History ◽  
Element Model ◽  
Frequency Characteristics ◽  
Dynamic Responses ◽  
Principal Stresses ◽  
Underground Powerhouse ◽  
Response Characteristics ◽  
Set Up

According to the practical situation, the 3-D finite element model of Sandaowan underground powerhouse caverns on Taolai River is set up for analyzing the behaviors under earthquake action. Based on static stress field of the surrounding rock mass, and with the selection of appropriate seismic waves for dynamic time-history analysis method, the dynamic responses of underground powerhouse caverns are analyzed. It is shown that the time-history waveform of dynamic displacement of given points has a very similar variation regularity with that of acceleration, and the wave phases of both are almost synchronous. The dynamic displacements and principal stresses of the given points on rock walls are with the vibration of low-frequency characteristics, the acceleration response is with the vibration of high-frequency characteristics.

Low Frequency Characteristics of Mixed Conductors

1962 ◽  
Vol 1 (3) ◽  
pp. 144-147 ◽  
Author(s):  
Isaaki Yokota ◽  
Shin-ya Miyatani
Keyword(s):  
Low Frequency ◽  
Frequency Characteristics ◽  
Mixed Conductors

scholarly journals Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dongbao Jia ◽  
Cunhua Li ◽  
Qun Liu ◽  
Qin Yu ◽  
Xiangsheng Meng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Frequency Characteristics ◽  
Brain Diseases ◽  
Time Dimension ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Low Frequency Power

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.

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