A time-scale decomposition approach to optimize wireless packet resource allocation and scheduling

2003 ◽  
Author(s):  
Hao Lin ◽  
Wei Wu ◽  
Yong Ren ◽  
Xiuming Shan
Keyword(s):  
Resource Allocation ◽  
Time Scale ◽  
Decomposition Approach ◽  
Time Scale Decomposition ◽  
Wireless Packet

scholarly journals A Time-Scale Decomposition Approach to Statistically Downscale Summer Rainfall over North China

2012 ◽  
Vol 25 (2) ◽  
pp. 572-591 ◽  
Author(s):  
Yan Guo ◽  
Jianping Li ◽  
Yun Li
Keyword(s):  
Time Scale ◽  
North China ◽  
Climate Models ◽  
Rainfall Variability ◽  
Summer Rainfall ◽  
Present Climate ◽  
Decomposition Approach ◽  
Southwest Indian Ocean ◽  
Interannual Rainfall Variability ◽  
Time Scale Decomposition

Abstract A time-scale decomposition (TSD) approach to statistically downscale summer rainfall over North China is described. It makes use of two distinct downscaling models respectively corresponding to the interannual and interdecadal rainfall variability. The two models were developed based on objective downscaling scheme that 1) identifies potential predictors based on correlation analysis between rainfall and considered climatic variables over the global scale and 2) selects the “optimal” predictors from the identified potential predictors via cross-validation-based stepwise regression. The downscaling model for the interannual rainfall variability is linked to El Niño–Southern Oscillation and the 850-hPa meridional wind over East China, while the one for the interdecadal rainfall variability is related to the sea level pressure over the southwest Indian Ocean. Taking the downscaled interannual and interdecadal components together the downscaled total rainfall was obtained. The results show that the TSD approach achieved a good skill to predict the observed rainfall with the correlation coefficient of 0.82 in the independent validation period. The authors further apply the model to obtain downscaled rainfall projections from three climate models under present climate and the A1B emission scenario in future. The resulting downscaled values provide a closer representation of the observation than the raw climate model simulations in the present climate; for the near future, climate models simulated a slight decrease in rainfall, while the downscaled values tend to be slightly higher than the present state.

scholarly journals An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Lévy Noise ◽  
Model Parameters ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Levy Noise ◽  
The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Time Scale ◽  
Energy Index ◽  
Signal Processing Algorithm ◽  
Intrinsic Time ◽  
Graph Signal Processing ◽  
Laplacian Energy ◽  
Rotational Components ◽  
Time Scale Decomposition

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

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