Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method

Strong Eu2+ light emission in Eu silicate through Eu3+ reduction in Eu2O3/Si multilayer deposited on Si substrates

Eu(2)O(3)/Si multilayer nanostructured films are deposited on Si substrates by magnetron sputtering. Transmission electron microscopy and X-ray diffraction measurements demonstrate that multicrystalline Eu silicate is homogeneously distributed in the film after high-temperature treatment in N(2). The Eu(2+) silicate is formed by the reaction of Eu(2)O(3) and Si layers, showing an intense and broad room-temperature photoluminescence peak centered at 610 nm. It is found that the Si layer thickness in nanostructures has great influence on Eu ion optical behavior by forming different Eu silicate crystalline phases. These findings open a promising way to prepare efficient Eu(2+) materials for photonic application

Hypoxic BMSC-derived exosomal miRNAs promote metastasis of lung cancer cells via STAT3-induced EMT

Abstract Background Metastasis is the main cause of lung cancer mortality. Bone marrow-derived mesenchymal stem cells (BMSCs) are a component of the cancer microenvironment and contribute to cancer progression. Intratumoral hypoxia affects both cancer and stromal cells. Exosomes are recognized as mediators of intercellular communication. Here, we aim to further elucidate the communication between BMSC-derived exosomes and cancer cells in the hypoxic niche. Methods Exosomal miRNA profiling was performed using a microRNA array. Lung cancer cells and an in vivo mouse syngeneic tumor model were used to evaluate the effects of select exosomal microRNAs. Hypoxic BMSC-derived plasma exosomal miRNAs were assessed for their capacity to discriminate between cancer patients and non-cancerous controls and between cancer patients with or without metastasis. Results We demonstrate that exosomes derived from hypoxic BMSCs are taken by neighboring cancer cells and promote cancer cell invasion and EMT. Exosome-mediated transfer of select microRNAs, including miR-193a-3p, miR-210-3p and miR-5100, from BMSCs to epithelial cancer cells activates STAT3 signaling and increases the expression of mesenchymal related molecules. The diagnostic accuracy of individual microRNA showed that plasma exosomal miR-193a-3p can discriminate cancer patients from non-cancerous controls. A panel of these three plasma exosomal microRNAs showed a better diagnostic accuracy to discriminate lung cancer patients with or without metastasis than individual exosomal microRNA. Conclusions Exosome-mediated transfer of miR-193a-3p, miR-210-3p and miR-5100, could promote invasion of lung cancer cells by activating STAT3 signalling-induced EMT. These exosomal miRNAs may be promising noninvasive biomarkers for cancer progression