Search

On the basis of successfully preparing nanozirconia coating via reasonable spraying technical parameters, the cross section and surface morphology of coating were determined by scan electron microscopy (SEM), and the ceramic particle sizes of coating were determined by transmission electron microscopy (TEM). Experimental results indicate that plasma sprayed zirconia coating possesses typical layer structure of thermal spray coating, in which columnar grains pile up together. Meanwhile, the non-molten nanopowders coexist with the layer structure. In addition, the equiaxial grains exist in the zirconia coating according to TEM, but the grain size is not uniform.

A standard is of great importance to microgrid application and development. So far, there is no published standard specifically for microgrids in China except for the Guangxi provincial standard, which is drafted by the authors. This paper focuses on the lessons that the drafters have learned from the standard establishment. The background of making such a standard is introduced first, and its purposes are clarified, as well as the difficulties in standard drafting. Then the structure of this standard is explained, and one technical specification is detailed as an example. Further studies on planning and design of microgrids, multi-microgrids integration and limitations of this standard are discussed. This work can provide reference for establishing a national microgrid standard, thus can stimulate the standardization of microgrids in China and promote their application.

The water droplets in the microemulsion system of xylene/water/Span-80 can act as a micro-reactor which solubilize Zr(NO3)4, Y(NO3)3, and dimethyloxalate. The homogeneous precipitation reactions will take place in the restricted spaces determined by the droplets size. The micro-reactors help us obtain spherical nanometer ZrO2(Y2O3) reunion powders successfully. The spherical nanometer ZrO2(Y2O3) reunion powders and the precursor powders were characterized by means of TG-DTA, XRD, SEM and laser particle analyzer. It was investigated that the effect of Span-80 concentration on morphology and crystal phase composition of reunion powders. The result shows that as the volume ratio Vxylene:VSpan-80:Vwater= 40:1.0:8, the well spherical and excellent dispersion reunion powders with size of 4-16um were prepared after the precursor powders were sintered at 600°C for 2h. The Span-80 concentration has no effect on the crystal phase composition of reunion powders, however, has significant effect on the morphology of reunion powders.

This paper deals with the problem of passivity analysis for stochastic neural networks with leakage, discrete and distributed delays. By using delay partitioning technique, free weighting matrix method and stochastic analysis technique, several sufficient conditions for the passivity of the addressed neural networks are established in terms of linear matrix inequalities (LMIs), in which both the time-delay and its time derivative can be fully considered. A numerical example is given to show the usefulness and effectiveness of the obtained results., {"references":["M. Galicki, H. Witte, J.D¨orschel, M. Eiselt, G. Griessbach, Common\noptimization of adaptive preprocessing units and a neural network during\nlearning period. Application in EEG pattern recognition, Neural Networks\n10 (1997) 1153-1163.","D. Zhou, J. Cao, Globally exponential stability conditions for cellular\nneural networks with time-varying delays, Appl. Math. Comput. 131\n(2002) 487-496.","J. Cao, J. Wang, Global asymptotic and robust stability of recurrent neural\nnetworks with time delays, IEEE Trans. Circuits Syst. I, Reg. Papers, 52\n(2) (2005) 417-426.","Z. Wu, P. Shi, H. Su, J. Chu, Passivity analysis for discrete-time stochastic\nMarkovian jump neural networks with mixed time delays, IEEE Trans.\nNeural Netw. 22 (2011) 1566-1575.","A. Rawat, R.N. Yadav, S.C. Shrivastava, Neural network applications in\nsmart antenna arrays: a review, Int. J. Electron. Commun. (AE¨U ) 66\n(2012) 903-912.","H. Huang, G. Feng, Delay-dependent stability for uncertain stochastic\nneural networks with time-varying delay, Physica A 381 (15) (2007)\n93C103.","J. Zhang, P. Shi, J. Qiu, Novel robust stability criteria for uncertain\nstochastic Hopfield neural networks with time-varying delays, Nonlinear\nAnal. Real World Appl. 8 (4) (2007) 1349-1357.","W. Chen, X. Lu, Mean square exponential stability of uncertain stochastic\ndelayed neural networks, Phys. Lett. A. 372 (7) (2008) 1061-1069.","H. Zhang, Y. Wang, Stability analysis of Markovian jumping stochastic\nCohenCGrossberg neural networks with mixed time delays, IEEE Trans.\nNeural Netw. 19 (2) (2008) 366-370.\n[10] W. Yu, Passivity analysis for dynamic multilayer neuro identifier, IEEE\nTrans. Circuits Syst. I 50 (1) (2003) 173-178.\n[11] H. Li, H. Gao, P. Shi, New passivity analysis for neural networks with\ndiscrete and distributed delays, IEEE Trans, Neural Netw. 21 (11) (2010)\n1842-1847.\n[12] R. Sakthivel, A. Arunkumar, K. Mathiyalagan, S. Marshal Anthoni,\nRobust passivity analysis of fuzzy Cohen-Grossberg BAM neural\nnetworks with time-varying delays, Appl. Math. Comput. 218 (7) (2011)\n3799-3809.\n[13] P. Balasubramaniam, G. Nagamani, R. Rakkiyappan, Passivity analysis\nfor neural networks of neutral type with Markovian jumping parameters\nand time delay in the leakage term. Commun. Nonlinear SCI. Numer.\nSimulat. 16 (2011) 4422-4437.\n[14] Q. Song, J. Liang, Z. Wang, Passivity analysis of discrete-time stochastic\nneural networks with time-varying delays, Neurocomputing 72 (7) (2009)\n1782-1788.\n[15] H. Li, C. Wang, P. Shi, New passivity results for uncertain discrete-time\nstochastic neural networks with mixed time delays, Neurocomputing 73\n(16) (2010) 3291-3299.\n[16] J. Fu, H. Zhang, T. Ma, On passivity analysis for stochastic neural\nnetworks with interval time-varying delay, Neurocomputing 73 (4) (2010)\n795-801.\n[17] Y. Chen, H. Wang, A. Xue, Passivity analysis of stochastic time-delay\nneural networks, Nonlinear Dynam. 61 (1-2) (2010) 71-82.\n[18] K. Mathiyalagan, R. Sakthivel, S. Marshal Anthoni, New robust passivity\ncriteria for stochastic fuzzy BAM neural networks with time-varying\ndelays, Commun. Nonlinear SCI. Numer. Simulat. 17 (3) (2012) 1392-\n1407.\n[19] P. Balasubramaniam, G. Nagamani, Global robust passivity analysis for\nstochastic interval neural networks with interval time-varying delays and\nMarkovian jumping parameters, J. Optim. Theory Appl. 149 (1) (2011)\n197-215.\n[20] P. Balasubramaniam, G. Nagamani, Global robust passivity analysis for\nstochastic fuzzy interval neural networks with time-varying delays, Expert\nSyst. Appl. 39 (1) (2012) 732-742.\n[21] X. Chen, W.X. Zheng, Stochastic state estimation for neural networks\nwith distributed delays and Markovian jump, Neural Netw. 25 (2012)\n14-20.\n[22] Z. Zhao, Q. Song, S. He, Passivity analysis of stochastic neural networks\nwith time-varying delays and leakage delay, Neurocomputing 125 (2014)\n22-27.\n[23] Z. Wang, H. Shu, Y. Liu, DWC. Ho, X. Liu, Robust stability analysis\nof generalized neural networks with discrete and distributed time delays,\nChaos Solitons Fract 30 (4) (2006) 886-896.\n[24] T. Li, X. Yang, P. Yang, S. Fei, New delay-variation-dependent stability\nfor neural networks with time-varying delay, Neurocomputing 101 (2013)\n361-169."]}

In this paper, together with some improved Lyapunov-Krasovskii functional and effective mathematical techniques, several sufficient conditions are derived to guarantee the error system is globally asymptotically stable with H∞ performance, in which both the time-delay and its time variation can be fully considered. In order to get less conservative results of the state estimation condition, zero equalities and reciprocally convex approach are employed. The estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities. A numerical example is provided to illustrate the usefulness and effectiveness of the obtained results., {"references":["M. Zhengjiang, Y. Baozeng, Analysis and optimal design of continuous\nneural networks with applications to associate memory, Neural Networks\n10 (1999) 257-271.","A. Rawat, R.N. Yadav, S.C. Shrivastava, Neural network applications in\nsmart antenna arrays: a review, Int. J. Electron. Commun. (AE¨U ) 66\n(2012) 903-912.","M. Galicki, H. Witte, J.D¨orschel, M. Eiselt, G. Griessbach, Common\noptimization of adaptive preprocessing units and a neural network during\nlearning period. Application in EEG pattern recognition, Neural Networks\n10 (1997) 1153-1163.","P. Balasubramaniam, G. Nagamani, R. Rakkiyappan, Passivity analysis\nfor neural networks of nueral type with Markovian jumping parameters\nand time delay in the leakage term. Commun. Nonlinear SCI. Numer.\nSimulat. 16 (2011) 4422-4437.","O.M. Kwon, S.M. Lee, J.H. Park, E.J Cha, New approaches on stability\ncriteria for neural networks with interval time-varying delays, Appl. Math.\nComput. 218 (2012) 9953-9964.","O. Faydasicok, S. Arik, A new robust stability criterion for dynamical\nneural networks with multiple time delays, Neurocomputing 99 (2013)\n290-297.","S.M. Lee, O.M. Kwon, J.H. Park, A novel delay-dependent criterion for\ndelayed neural networks of neutral type, Phys. Lett. A. 374 (2010) 1843-\n1848.","H. Mai, X.Liao, C. Li, A semi-free weighting matrices approach for\nneutral-type delayed neural networks, J. Comput. Appl. Math. 225 (2009)\n44-55.","C. Liu, C. li, S. Duan, Stabilization of oscillating neural networks with\ntime-delay by intermittent control, Int. J. Contr. Autom. Syst. 9 (2011)\n1074-1079.\n[10] C.K. Ahn, Delay-dependent state estimation for T-S fuzzy delayed\nHopfiled neural networks, Nonlinear Dynam. 61 (2010) 483-489.\n[11] T. Li, X. Yang, P. Yang, S. Fei, new delay-variation-dependent stability\nfor neural networks with time-varying delay, Neurocomputing 101 (2013)\n361-169.\n[12] Z. Wu, P. Shi, H. Su, J. Chu, Exponential synchronization of\nneural networks with discrete and diatributed delays under time-varying\nsampling, IEEE Trans. Neural Netw. Learn. Syst. 23 (9) (2012) 1368-\n1376.\n[13] P.G. Park, J.W. Ko, C. Jeong, Reciprocally convex approach to stability\nof systems with time-varying delays, Automatica 47 (2011) 235-238.\n[14] X. Liu, N. Jiang, Roubust stability analysis of generlized neural\nnetworks with multiple discrete delays and multiple distributed delays,\nNeurocomputing 72 (2009) 1789-1796.\n[15] J. Sun, G.P. Liu, J. Chen, Delay-dependent stability and stabilization of\nneutral time-delay systems, Int. J. Robust Nonlinear Control 19 (2009)\n1364-1375.\n[16] Q. Ma, S. Xu, Y. Zou, J. Lu, Stability of stochastic Markovian jump\nneural networks with mode-dependent delays, Neurocomputing 74 (2011)\n2157-2163.\n[17] O.M. Kwon, Ju H. Park, Improved delay-dependent stability criterion\nfor neural networks with time-varying delays, Phys. Lett. A 373 (2009)\n529-535.\n[18] Y. Zhang, D. Yue, E. Tian, New stability criteria of neural networks\nwith interval time-varying delays: a piecewise delay method, Appl. Math.\nComput. 208 (2009) 249-259.\n[19] X. Chen, W.X. Zheng, Stochastic state estimation for neural networks\nwith distributed delays and Markovian jump, Neural Netw. 25 (2012)\n14-20.\n[20] S. Lakshmanan, K. Mathiyalagan, J.H. Park, R. Sakthival, Fathalla A.\nRihan, Delay-dependent H∞ state estimation of neural networks with\nmixed time-varying delays, Neurocomputing, 2013.\n[21] S. Lakshmanan, J.H. Park, H.Y. Jung, P. Balasubramanian, Design of\nstate estimator for neural networks with leakage, discrete and distributed\ndelays, Appl. Math. Comput. 218 (2012) 11297-11310.\n[22] J. Chang, H. Zhu, S. Zhong, Y. Zhang, Finite-time boundness of H∞ filitering for switched discreted-time systems, Int. J. Control Autom. Syst.\n10 (6) (2012) 1129-1135.\n[23] J. Liang, J. Lam, Z. Wang, State estimation for Markov-type genetic\nregulatory networks with delays and uncertain mode transtion rates, Phys,\nLett. A 373 (2009) 4328-4337.\n[24] J.H. Park, O.M. Kwon, S.M. Lee, State estimation for neural networks\nof neutral-type with interval time-varying delays, Appl. Math. Comput.\n203 (2008) 217-223.\n[25] P. Balasubramaniam, M. Kalpana, R. Rakkiyappan, State estimation for\nfuzzy celluar neural networks with time delay in the leakage term, discrete\nand unbounded distributed delays, Comput. Math. Appl. 62 (2011) 3959-\n3972.\n[26] Y. Liu, S.M. Lee, O.M. Kwon, J.H. Park, A study on H∞ state\nestimation of static neural networks with time-varying delays, Appl. Math.\nComput. 226 (2014) 589-597.\n[27] M.S. Mahmoud, New exponentially convergent state estiamtion method\nfor delayed neural networks, Neurocomputing 72 (16-18) (2009) 3935-\n3942.\n[28] H. Huang, G. Feng, J. Cao, Guaranteed performance state estimation of\nstatic neural networks with time-varying delay, Neurocomputing 74 (4)\n(2011) 606-616.\n[29] Q. Duan, H. Su, Z.-G. Wu,H∞ state estimation of static neural networks\nwith time-varying delay, Neurocomputing 97 (2012) 16-21.\n[30] C. Li, T. Huang, On the stability of nonlinear systems with leakage\ndelay, J. Franklin Inst. 346 (2009) 366-377.\n[31] H. Bao, J. Cao, Robust state estimation for uncertain stochastic\nbidirectional associative memory networks with time-varying delays,\nPhys. Scr. 83 (2011) 065004."]}

In order to compensate for the fact that direct current electrical fields could not reduce or correct spinal-cord edema after trauma, methylprednisolone was used 2 hr after spinal-cord injury to promote the efficacy of the direct current electrical field. Thirty-three dogs were randomly divided into three groups and their spinal cords were injured with the Allen method (weight-dropping technique). Group A, a control group; Group B, implantation of an electrical stimulator 6 hr after spinal-cord injury; and Group C, given an injection of a large dose of methylprednisolone 2 hr after injury. Electric stimulators were implanted into the dogs' bodies 6 hr post-injury. The nerve functions, evoked potential cortical somatosensory and three kinds of nerve morphometric changes, were observed 1 to 3 months after injury. The indexes of Groups B and C were much better than Group A (p

In this paper, a strategy for view maintenance in peer data management systems (PDMSs) is proposed. First, a hybrid peer architecture is presented, in which peers prefer peer-to-peer architecture to super-peer based architecture. Then, if a view is reformulated along a semantic path, it can retrieve data from many data sources. So the peer view, local view and global view are proposed. A global view maintenance is turned into the maintenance of all related local views if join operations are confined in each local PDMS, and a query can gain the same result if it is posed over any peer in the same semantic path. Furthermore, a view may be a conjunctive query which relates to several tables in PDMSs. According to the application, Mork’s rules are extended. Based on the new rule system, the update data are propagated in PDMS. According to the new rule system, a view maintenance algorithm in PDMS is proposed. Finally, simulation experiments are carried out in the SPDMS (schema-mapping-based PDMS). The simulation results show that the proposed view maintenance strategy has better performance than that of Mork’s.