31 research outputs found
Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model
In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm
Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model
In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm
Bearing remain life prediction based on weighted complex SVM models
Aiming to achieve the bearing remaining life prediction, this research proposed a method based on the weighted complex support vector machine (SVM) model. Firstly, the features are extracted by time domain, time-frequency domain method, so as the extract the original features. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis (PCA) is used to merge the features and reduce the dimension. And the bearing degradation indicator is constructed based on the first principal component, which can indicate the bearing early failure state precisely. Then, based on the life condition indicator, the weighted complex SVM model is used to achieve the bearing remain life prediction, in this model, the particle swarm algorithm (PSO) method is used to select the SVM internal parameters, the phase space reconstruction algorithm is used to determine the structure of the SVM. Cases of actual were analyzed, the results proved the effectiveness of the methodology
The regulatory role of APE1 in epithelial-to-mesenchymal transition and in determining EGFR-TKI responsiveness in non-small-cell lung cancer
Serum APE1 as a predictive marker for platinum-based chemotherapy of non-small cell lung cancer patients
Purpose: To define the role of the DNA repair protein apurinic/apyrimidinic endonuclease 1 (APE1) in predicting the prognosis and chemotherapeutic response of non-small cell lung cancer patients receiving platinum-containing chemotherapy. Results: Our investigations found that serum APE1 level was significantly elevated in 229 of 412 NSCLC patients and correlated with its level in tissue (r2 = 0.639, p < 0.001). The elevated APE1 level in both tissue and serum of patients prior to chemotherapy was associated with worse progression-free survival (HR: 2.165, p < 0.001, HR: 1.421, p = 0.012), but not with overall survival. After 6 cycles of chemotherapy, a low APE1 serum level was associated with better overall survival (HR: 0.497, p = 0.010). Experimental Design: We measured APE1 protein levels in biopsy tissue from 172 NSCLC patients and sera of 412 NSCLC patients receiving platinum-based chemotherapy by immunohistochemistry and a newly established sensitive and specific enzyme-linked immunosorbent assay, respectively. APE1 levels in sera of 523 healthy donors were also determined as control. Conclusions: Our studies indicate that APE1 is a biomarker for predicting prognosis and therapeutic efficacy in NSCLC. The chemotherapy-na\uefve serum APE1 level, which correlated with its tissue level inversely associated with progressionfree survival of platinum-containing doublet chemotherapy, whereas post-treatment serum APE1 level was inversely associated with overall survival
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been
monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT).
Based on the obtained data, we investigate the broadband spectrum of the source
throughout the outburst. We estimate the broadband flux of the source and
search for possible cyclotron line in the broadband spectrum. No evidence of
line-like features is, however, found up to . In the absence of
any cyclotron line in its energy spectrum, we estimate the magnetic field of
the source based on the observed spin evolution of the neutron star by applying
two accretion torque models. In both cases, we get consistent results with
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite
As China's first X-ray astronomical satellite, the Hard X-ray Modulation
Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15,
2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy
satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was
designed to perform pointing, scanning and gamma-ray burst (GRB) observations
and, based on the Direct Demodulation Method (DDM), the image of the scanned
sky region can be reconstructed. Here we give an overview of the mission and
its progresses, including payload, core sciences, ground calibration/facility,
ground segment, data archive, software, in-orbit performance, calibration,
background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech.
Astron. arXiv admin note: text overlap with arXiv:1910.0443
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci
Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1). Through analysis of de novo mutations in autism spectrum disorder (ASD), Sanders et al. find that small deletions, but not large deletions/duplications, contain one critical gene. Combining CNV and sequencing data, they identify 6 loci and 65 genes associated with ASD. © 2015 Elsevier Inc