Research on Fault Diagnosis Method Based on Rule Base Neural Network

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method

Preparation of Co-YSZ/HAp nano-composite coating on Ti substrate by electrochemical method

Co-YSZ/HAp nano-composite coating and nano-HAp single coating were deposited on Ti substrate using the combination of electrocodeposition and electrophoretic deposition, and electrophoretic depositions, respectively. Surface and cross section morphologies of the coatings were observed by scanning electron microscope. Element distribution and crystal phase of the coatings were analyzed by energy dispersive spectrum and X-ray diffraction. The adhesive strength of the coating and Ti substrate was determined by shear strength testing. The results indicate that the adhesive strength of Co-YSZ/HAp nano-composite coating and Ti substrate is higher than that of nano-HAp single coating and Ti substrate. It shows that Co-YSZ as an interlayer reduces the mismatch of the thermal expansion coefficients between HAp and titanium and Co-YSZ/HAp nano-composite coating has better mechanical properties

Research on Fault Diagnosis Method Based on Rule Base Neural Network

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method

Improvement of PLLA Ductility by Blending with PVDF: Localization of Compatibilizers at Interface and Its Glycidyl Methacrylate Content Dependency

In this work, the localization of reactive compatibilizer (RC, containing poly(methyl methacrylate) (PMMA) backbone with randomly distributed glycidyl methacrylate (GMA) on it) at the polyvinylidene fluoride/poly(l-lactic acid) (PVDF/PLLA) interface has been manipulated by means of GMA contents. At the very beginning of mixing, RC tends to stay in the PVDF phase due to the miscibility between PVDF and PMMA. Upon further shearing, more and more PLLA chains have been grafted on PMMA backbone, producing PLLA–g–PMMA copolymer. The balanced stress on two sides accounts for the localization of compatibilizers at the PVDF/PLLA interface. Finally, the stress of the PLLA side has been enhanced remarkably due to the higher graft density of PLLA, resulting in the enrichment of the copolymer in the PLLA matrix. The migration of RC from the PVDF phase to the immiscible interface and PLLA matrix can be accelerated by employing RC with higher GMA content. Furthermore, the compatibilizer localization produces a significant influence on the morphology and ductility of the PVDF/PLLA blend. Only when the compatibilizers precisely localize at the interface, the blend exhibits the smallest domain and highest elongation at break. Our results are of great significance for not only the fabrication of PLLA with high ductility, but also the precise localization of compatibilizers at the interface of the immiscible blend

Discovery of imidazo[1,2-a]-pyridine inhibitors of pan-PI3 kinases that are efficacious in a mouse xenograft model

Alterations in PI3K/AKT signaling are known to be implicated with tumorigenesis. The PI3 kinases family of lipid kinases has been an attractive therapeutic target for cancer treatment. Imidazopyridine compound 1, a potent, selective, and orally available pan PI3K inhibitor, identified by scaffold morphing of a benzothiazole hit, was further optimized in order to achieve efficacy in PTEN-deleted A2780 ovarian cancer mouse xenograft model. With a hypothesis that a planar conformation between the core and the 6-heteroaryl ring will allow for the accommodation of larger 5’-substituents in a hydrophobic area under P-loop, SAR efforts focused on 5’-alkoxy heteroaryl rings at the 6-position of imidazopyridine and imidazopyridazine cores that have the same dihedral angle of zero degrees. 6’-Alkoxy 5’-aminopyrazines in the imidazopyridine series were identified as the most potent compounds in the A2780 cell line. Compound 14 with 1,1,1-trifluoroisopropoxy group at 6’-position demonstrated excellent potency and selectivity, good oral exposure in rats and in vivo efficacy in A2780 tumor bearing mouse. Also, we disclose the X-ray co-crystal structure of one enantiomer of compound 14 in PI3Kα, confirming that the trifluoromethyl group fits nicely in the hydrophobic hot spot under P-loop