The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can b...The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.展开更多
The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization effici...The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.展开更多
Radio-frequency interference（RFI） detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation. There are several robu...Radio-frequency interference（RFI） detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation. There are several robust techniques to identify the RFI signals, such as the mean/standard deviation method and the normalized principal component analysis method. However, verification of these existing detection methods remains an open issue in the absence of a reliable validation data-set of the ‘true＇ RFI signals. In this paper, a cross-validation scheme using two independent RFI detection methods is proposed to derive the thresholds for identifying the RFI-contaminated data for the Advanced Microwave Scanning Radiometer for Earth Observing System（AMSR-E）. It is shown that the new scheme is effective in the quantitative classification of the RFI signals in the AMSR-E C-and X-band channels over the continents. Strong RFI signals are found to be populated over cities of the United States at AMSR-E C-band, while RFIs at X-band are mainly observed over Europe and Japan.展开更多
Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of su...Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.展开更多
基金Project(51275030)supported by the National Natural Science Foundation of ChinaProject(2016JBM051)supported by the Fundamental Research Funds for the Central Universities,China
文摘The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.60971082,60872049,60972073and60871042)the National Key Basic Research Program of China(Grant No.2009CB320400)+1 种基金the National Great Science Specific Project(Grant Nos.2009ZX03003-001,2009ZX03003-011and2010ZX03001003)Chinese Universities Scientific Fund,China
文摘The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.
基金supported by the Special Fund for Meteorological Research in the Public Interest of China(Project No.GYHY201406008)the National Natural Science Foundation of China[grant number 91337218]
文摘Radio-frequency interference（RFI） detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation. There are several robust techniques to identify the RFI signals, such as the mean/standard deviation method and the normalized principal component analysis method. However, verification of these existing detection methods remains an open issue in the absence of a reliable validation data-set of the ‘true＇ RFI signals. In this paper, a cross-validation scheme using two independent RFI detection methods is proposed to derive the thresholds for identifying the RFI-contaminated data for the Advanced Microwave Scanning Radiometer for Earth Observing System（AMSR-E）. It is shown that the new scheme is effective in the quantitative classification of the RFI signals in the AMSR-E C-and X-band channels over the continents. Strong RFI signals are found to be populated over cities of the United States at AMSR-E C-band, while RFIs at X-band are mainly observed over Europe and Japan.
基金the National Basic Research Program of China (973 Program) under Grant No.2012CB821200，the National Natural Science Foundation of China under Grants No.91024001,No.61070142，the Beijing Natural Science Foundation under Grant No.4111002
文摘Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.
