Measurement sensitivity analysis and on-orbit calibration of systematic errors for a narrow field-of-view camera.

Narrow field-of-view (FOV) cameras enable long-range observations and have been often used in deep space exploration missions. To solve the problem of systematic error calibration for a narrow FOV camera, the sensitivity of the camera systematic errors to the angle between the stars is analyzed theoretically, based on a measurement system for observing the angle between stars. In addition, the systematic errors for a narrow FOV camera are classified into "Non-attitude Errors" and "Attitude Errors". Furthermore, the on-orbit calibration methods for the two types of errors are researched. Simulations show that the proposed method is more effective in the on-orbit calibration of systematic errors for a narrow FOV camera than the traditional calibration methods.

Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package.

The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately.

Long non-coding RNA TUG1 accelerates abnormal growth of airway smooth muscle cells in asthma by targeting the miR-138-5p/E2F3 axis.

Asthma is a chronic airway inflammatory disease. The present study aimed to explore the effect of the long non-coding RNA taurine-upregulated gene 1 (TUG1) on the viability and migration of airway smooth muscle cells (ASMCs) in asthma. Rat asthma models were constructed with ovalbumin sensitization and challenge and the level of serum immunoglobulin E (IgE) and the rates of inspiratory and expiratory resistance were measured. Reverse transcription-quantitative PCR was also performed to determine the expression levels of TUG1. Platelet-derived growth factor-BB (PDGF-BB)-treated ASMCs were then used as a cell model of asthma. The viability and migratory abilities of ASMCs were analysed with the MTT and Transwell assays. Additionally, a dual-luciferase reporter assay was used to confirm the relationship between TUG1 and microRNA (miR)-138-5p and between transcription factor E2F3 and miR-138-5p. The expression of TUG1, level of serum IgE, inspiratory resistance and expiratory resistance were clearly increased in the rat asthma model in comparison with controls. Knockdown of TUG1 the viability and migration of PDGF-BB-induced ASMCs and reduced the inspiratory and expiratory resistances. In addition, TUG1 functioned as a bait of miR-138-5p, and miR-138-5p modulated E2F3 expression. Knockdown of E2F3 hindered the abnormal growth of ASMCs. Moreover, miR-138-5p inhibition or E2F3 overexpression reversed the inhibitory effects of TUG1 knockdown on viability and migration of PDGF-BB-induced ASMCs. The TUG1/miR-138-5p/E2F3 regulatory axis appeared to play a critical role in accelerating the viability and migration of ASMCs and may therefore have a role in asthma.

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion.

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

[Effect of mogroside VI on acute liver injury induced by sepsis in mice and related mechanisms].

OBJECTIVE: To study the effect of mogroside VI (MVI) on acute liver injury induced by sepsis in mice and its possible mechanisms. Methods A total of 60 male C57BL/6 mice were randomly divided into five groups: sham-operation, model, low-dose MVI (25 mg/kg), high-dose MVI (100 mg/kg), peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) inhibitor (100 mg/kg MVI+30 mg/kg PGC-1α inhibitor SR-18292), with 12 mice in each group. Cecal ligation and puncture were performed to establish a mouse model of sepsis. The drugs were given by intraperitoneal injection after the model was established, once a day for 3 consecutive days. ELISA was used to measure the serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Colorimetry was used to measure the levels of malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) in liver tissue. Hematoxylin-eosin staining was used to observe liver histopathological changes. Liver mitochondrial respiratory function was measured, and mitochondrial respiratory control rate was calculated. RT-PCR was used to measure the copy number of mitochondrial DNA (mtDNA) in liver tissue and the mRNA expression levels of PGC-1α, nuclear respiratory factor-1 (NRF-1), and mitochondrial transcription factor A (TFAM) in liver tissue. Western blot was used to measure the protein expression levels of PGC-1α, NRF-1, and TFAM in liver tissue. RESULTS: Compared with the sham-operation group, the model group had significant increases in the serum levels of ALT and AST and the content of MDA in liver tissue (P<0.05) and significant reductions in the activities of GSH-Px and SOD in liver tissue (P<0.05). The model group had also severe liver histopathological injury and significant reductions in the mitochondrial respiratory control rate, the copy number of mtDNA, and the mRNA and protein expression levels of PGC-1α, NRF-1, and TFAM in liver tissue (P<0.05). Compared with the model group, the high-dose group had significant reductions in the serum levels of ALT and AST and the content of MDA in liver tissue (P<0.05), significant increases in the activities of GSH-Px and SOD in liver tissue (P<0.05), significant improvement in liver histopathological injury, and significant increases in the mitochondrial respiratory control rate, the copy number of mtDNA, and the mRNA and protein expression levels of PGC-1α, NRF-1, and TFAM in liver tissue (P<0.05). There were no significant differences in the above indicators between the low-dose and model groups (P>0.05). The PGC-1α inhibitor SR-18292 significantly inhibited the intervention effect of high-dose MVI (P<0.05). CONCLUSIONS: MVI can effectively alleviate acute liver injury caused by sepsis in mice, possibly by enhancing mitochondrial biosynthesis mediated by PGC-1α.

SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage-Husa Adaptive Filter.

Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage-Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.

SINS/Landmark Integrated Navigation Based on Landmark Attitude Determination.

Based on the situation that the traditional SINS (strapdown inertial navigation system)/CNS (celestial navigation system) integrated navigation system fails to realize all-day and all-weather navigation, this paper proposes a SINS/Landmark integrated navigation method based on landmark attitude determination to solve this problem. This integrated navigation system takes SINS as the basic scheme and uses landmark navigation to correct the error of SINS. The way of the attitude determination is to use the landmark information photographed by the landmark camera to complete feature matching. The principle of the landmark navigation and the process of attitude determination are discussed, and the feasibility of landmark attitude determination is analyzed, including the orthogonality of the attitude transform matrix, as well as the influences of the factors such as quantity and geometric position of landmarks. On this basis, the paper constructs the equations of the SINS/Landmark integrated navigation system, testifies the effectiveness of landmark attitude determination on the integrated navigation by Kalman filter, and improves the navigation precision of the system.

Fault Identification for a Closed-Loop Control System Based on an Improved Deep Neural Network.

Fault identification for closed-loop control systems is a future trend in the field of fault diagnosis. Due to the inherent feedback adjustment mechanism, a closed-loop control system is generally very robust to external disturbances and internal noises. Closed-loop control systems often encourage faults to propagate inside the systems, which may lead to the consequence that faults amplitude becomes smaller and fault characteristics difference becomes more inapparent. Hence, it has been challenging to achieve fault identification for such systems. Traditional fault identification methods are not particularly designed for closed-loop control systems and thus cannot be applied directly. In this work, a new fault identification method is proposed, which is based on the deep neural network for closed-loop control systems. Firstly, the fault propagation mechanism in closed-loop control systems is theoretically derived, and the influence of fault propagation on system variables is analyzed. Then deep neural network is applied to find fault characteristics difference between different data modes, and a sliding window is used to amplify the fault-to-noise ratio and characteristics difference, with an aim to increase the identification performance. To verify this method, the simulations that are based on a numerical simulation model, the Tennessee industrial system and the satellite attitude control system are conducted. The results show that the proposed method is more feasible and more effective in fault identification for closed-loop control systems compared with traditional data-driven identification methods, including distance-based and angle-based identification methods.

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode.

Strap-down inertial navigation system/celestial navigation system ( SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.