Far Infrared Radiation Property of Elbaite/Alumina Composite Materials

Far infrared materials have been prepared by precipitation method using natural elbaite powder as raw materials, which belongs to tourmaline group. The chemical formula of elbaite is Na(Al, Li)3Al6B3Si6O27(O, OH, F)4. X-ray powder diffraction (XRD) shows that elbaite and alumina in composite material has good crystal form. In addition, XRD results indicate the formation of alumina crystallites show that alumina powder exists as nano-meter particles on the surface of elbaite powder. It can be calculated the particles diameter of Al2O3 is 47.86nm. The maximum infrared radiation rate of tourmaline/alumina composite materials is 0.89 when the ratio of alumina in elbaite powder is 20%. The infrared radiation rate has been increased by 0.03, compared with single elbaite. It shows that the infrared radiation rate of the composite materials is higher than any of a single component. Two reasons are attributed to the improve of the rate of far infrared radiation: alumina powder exists as nano-meter particles and different materials will increase the absorption peak and the vibration intensity in FTIR spectra

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC

Branched DNA-based Alu quantitative assay for cell-free plasma DNA levels in patients with sepsis or systemic inflammatory response syndrome

AbstractCell-free circulating DNA (cf-DNA) can be detected by various of laboratory techniques. We described a branched DNA–based Alu assay for measuring cf-DNA in septic patients. Compared to healthy controls and systemic inflammatory response syndrome (SIRS) patients, serum cf-DNA levels were significantly higher in septic patients (1426.54 ± 863.79 vs 692.02 ± 703.06 and 69.66 ± 24.66 ng/mL). The areas under the receiver operating characteristic curve of cf-DNA for normal vs sepsis and SIRS vs sepsis were 0.955 (0.884-1.025), and 0.856 (0.749-0.929), respectively. There was a positive correlation between cf-DNA and interleukin 6 or procalcitonin or Acute Physiology and Chronic Health Evaluation II. The cf-DNA concentration was higher in intensive care unit nonsurviving patients compared to surviving patients (2183.33 ± 615.26 vs 972.46 ± 648.36 ng/mL; P < .05). Branched DNA–based Alu assays are feasible and useful to quantify serum cf-DNA levels. Increased cf-DNA levels in septic patients might complement C-reactive protein and procalcitonin in a multiple marker format. Cell-free circulating DNA might be a new marker in discrimination of sepsis and SIRS

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenois