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Xuejin Fan,Qi Peng,Yanhua Chen,Zeke Ma,Xiaoguang He,Biying Deng,Hui Huang,Juan Zeng,Chuyun Cheng,Shaoji Liu,Xiaomei Lu 한국유전학회 2015 Genes & Genomics Vol.37 No.12
The aim of this study was to test the impact of variants rs900400 (located near LEKR1 and CCNL1) and rs9883204 (located in ADCY5) on birth weight in a Chinese population. We conducted a case–control study including 156 low-birth- weight infants as the case group and 100 normal-birth-weight infants as the control group. The rs900400 and rs9883204 variants were analyzed by gene sequencing in all the participants. Our results revealed a significant difference in the genotype distribution (v2 = 10.449, p = 0.005) and allele distribution (v2 = 9.277, p = 0.002) of rs900400 between the case group and the control group. The C allele of rs900400 was associated with lower birth weight (OR 1.771 [95 % CI 1.237–2.535]) in the Chinese population. However, the rs9883204 polymorphism was not informative in the Chinese population. Our study shows that the ‘‘birth weightlowering’’ variant rs900400 located near LEKR1 and CCNL1, which is strongly associated with birth weight in European cohorts, appears to have a similar association in Chinese cohorts. However, the rs9883204 variant located in ADCY5 does not appear to be correlated with low birth weight in the same population. Moreover, we found that the variant rs900400 may also be associated with premature birth, thereby supporting the need for further research in this area.
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Rolling bearing composite fault diagnosis method based on EEMD fusion feature
Yixin Zhao,Yao Fan,Hu Li,Xuejin Gao 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.9
Aiming at the problem that the composite fault vibration signal of rolling bearing is complex and it is difficult to effectively extract the impact characteristics of the composite fault, a composite fault diagnosis method of rolling bearing based on multi-scale fuzzy entropy feature fusion is proposed. Compared with traditional fault feature extraction methods that can only extract single fault feature information, this method can increase the discrimination of composite fault features, effectively separate multiple composite fault features, and more comprehensively characterize composite fault feature information. First, the signal is processed by EEMD, getting a series of IMF components. Secondly, the energy and kurtosis index of the IMF component are calculated, the appropriate IMF component is selected through the correlation coefficient to obtain a new time series, the multi-scale fuzzy entropy is calculated, and feature fusion performed. Finally, the least square support vector machine is used to diagnose the fault of the fusion feature. The method is verified by a mechanical failure simulation test bench. The experimental results show that this method can quantitatively characterize the data information of fault signal, improve the anti-interference ability, have good feature extraction ability of composite fault of rolling bearings, and can effectively identify the type of composite fault. Compared with the method using multi-scale fuzzy entropy, energy and kurtosis index alone, the accuracy of fault diagnosis increases by 8.12 % and 11.65 %, respectively.
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