a b s t r a c tIncreasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson's r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1-5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.

No abstract

Online dictionary learning (ODL) is an emerging and efficient dictionary learning algorithm, which can extract fault features information of fault signals in most occasions. However, the typical ODL algorithm fails to consider the interference of noise and the structural features of the fault signals, which leads to the fault features of weak fault signals that are difficult to extract. For that, a novel feature enhancement method based on an improved constraint model of an ODL (ICM-ODL) algorithm has been proposed in this paper. For the stage of dictionary learning, the elastic-net constraint is used to promote the anti-noise performance of the dictionary atoms. For the stage of signals sparse coding, the l 2,1 norm constraint is added to learn the structural features of fault signals. In addition, a variational mode decomposition algorithm is used to reduce the impact of noise on the signal initially. Taking the weak fault signals of bearing as examples for analysis, the results show that the feature enhancement of the weak fault signals is fulfilled by using the ICM-ODL algorithm. Compared with the typical ODL method, the ICM-ODL algorithm can not only improves the anti-noise performance of the dictionary atoms, but also removes the noise compositions of the reconstructed signal significantly. INDEX TERMS Online dictionary learning, sparse representation, elastic-net, l 2,1 norm, feature enhancement.

No abstract

No abstract