Effect of choice of basis functions in neural network for capturing unknown function for dynamic inversion control

The basic requirement for an autopilot is fast response and minimum steady state error for better guidance performance. The highly nonlinear nature of the missile dynamics due to the severe kinematic and inertial coupling of the missile airframe as well as the aerodynamics has been a challenge for an autopilot that is required to have satisfactory performance for all flight conditions in probable engagements. Dynamic inversion is very popular nonlinear controller for this kind of scenario. But the drawback of this controller is that it is sensitive to parameter perturbation. To overcome this problem, neural network has been used to capture the parameter uncertainty on line. The choice of basis function plays the major role in capturing the unknown dynamics. Here in this paper, many basis function has been studied for approximation of unknown dynamics. Cosine basis function has yield the best response compared to any other basis function for capturing the unknown dynamics. Neural network with Cosine basis function has improved the autopilot performance as well as robustness compared to Dynamic inversion without Neural network.

Structural Insights into Proton Conduction in Gallic Acid-Isoniazid Cocrystals

Hydrated cocrystal of gallic acid-isoniazid displays a single crystal-to-single crystal transformation upon dehydration, resulting in a difference of three orders of magnitude in proton conduction. The conduction pathway is shown to follow the Grotthus mechanism, supported by theoretical (DFT) calculations.

Thermal conduction and multiphase gas in cluster cores

We examine the role of thermal conduction and magnetic fields in cores of galaxy clusters through global simulations of the intracluster medium (ICM). In particular, we study the influence of thermal conduction, both isotropic and anisotropic, on the condensation of multiphase gas in cluster cores. Previous hydrodynamic simulations have shown that cold gas condenses out of the hot ICM in thermal balance only when the ratio of the cooling time (t(cool)) and the free-fall time (t(ff)) is less than approximate to 10. Since thermal conduction is significant in the ICM and it suppresses local cooling at small scales, it is imperative to include thermal conduction in such studies. We find that anisotropic (along local magnetic field lines) thermal conduction does not influence the condensation criterion for a general magnetic geometry, even if thermal conductivity is large. However, with isotropic thermal conduction cold gas condenses only if conduction is suppressed (by a factor less than or similar to 0.3) with respect to the Spitzer value.

Structural Diversity in Multinuclear PdII Assemblies that Show Low- Humidity Proton Conduction

Systematic investigation on synergetic effects of geometry, length, denticity, and asymmetry of donors was performed through the formation of a series of uncommon Pd-II aggregates by employing the donor in a multicomponent self-assembly of a cis-blocked 90 degrees Pd-II acceptor and a tetratopic donor. Some of these assemblies represent the first examples of these types of structures, and their formation is not anticipated by only taking the geometry of the donor and the acceptor building units into account. Analysis of the crystal packing of the X-ray structure revealed several H bonds between the counteranions (NO3-) and water molecules (OHON). Moreover, H-bonded 3D-networks of water are present in the molecular pockets, which show water-adsorption properties with some variation in water affinity. Interestingly, these complexes exhibit proton conductivity (1.87x10(-5)-6.52x10(-4)Scm(-1)) at 296K and low relative humidity (ca. 46%) with activation energies of 0.29-0.46eV. Moreover, the conductivities further increase with the enhancement of humidity. The ability of these assemblies to exhibit proton-conducting properties under low-humidity conditions makes these materials highly appealing as electrolytes in batteries and in fuel-cell applications.

Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a `footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by similar to 50% in generator potentials, to similar to 3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Intermittent electrical stimuli for guidance of human mesenchymal stem cell lineage commitment towards neural-like cells on electroconductive substrates

In the context of the role of multiple physical factors in dictating stem cell fate, the present paper demonstrates the effectiveness of the intermittently delivered external electric field stimulation towards switching the stem cell fate to specific lineage, when cultured in the absence of biochemical growth factors. In particular, our findings present the ability of human mesenchymal stem cells (hMSCs) to respond to the electric stimuli by adopting extended neural-like morphology on conducting polymeric substrates. Polyaniline (PANI) is selected as the model system to demonstrate this effect, as the electrical conductivity of the polymeric substrates can be systematically tailored over a broad range (10(-9) to 10 S/cm) from highly insulating to conducting by doping with varying concentrations (10(-5) to 1 M) of HCl. On the basis of the culture protocol involving the systematic delivery of intermittent electric field (dc) stimulation, the parametric window of substrate conductivity and electric field strength was established to promote significant morphological extensions, with minimal cellular damage. A time dependent morphological change in hMSCs with significant filopodial elongation was observed after 7 days of electrically stimulated culture. Concomitant with morphological changes, a commensurate increase in the expression of neural lineage commitment markers such as nestin and PI tubulin was recorded from hMSCs grown on highly conducting substrates, as revealed from the mRNA expression analysis using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) as well as by immune-fluorescence imaging. Therefore, the present work establishes the key role of intermittent and systematic delivery of electric stimuli as guidance cues in promoting neural-like differentiation of hMSCs, when grown on electroconductive substrates. (C) 2014 Elsevier Ltd. All rights reserved.

Modelling of transient heat conduction with diffuse interface methods

We present a survey on different numerical interpolation schemes used for two-phase transient heat conduction problems in the context of interface capturing phase-field methods. Examples are general transport problems in the context of diffuse interface methods with a non-equal heat conductivity in normal and tangential directions to the interface. We extend the tonsorial approach recently published by Nicoli M et al (2011 Phys. Rev. E 84 1-6) to the general three-dimensional (3D) transient evolution equations. Validations for one-dimensional, two-dimensional and 3D transient test cases are provided, and the results are in good agreement with analytical and numerical reference solutions.

An insight to the low temperature conduction mechanism of c-axis grown Al-doped ZnO, a widely used transparent conducting oxide

Al-doped ZnO thin films were synthesized from oxygen reactive co-sputtering of Al and Zn targets. Explicit doping of Al in the highly c-axis oriented crystalline films of ZnO was manifested in terms of structural optical and electrical properties. Electrical conduction with different extent of Al doping into the crystal lattice of ZnO (AZnO) were characterized by frequency dependent (40 Hz-50 MHz) resistance. From the frequency dependent resistance, the ac conduction of them, and correlations of localized charge particles in the crystalline films were studied. The dc conduction at the low frequency region was found to increase from 8.623 mu A to 1.14 mA for the samples AZnO1 (1 wt% Al) and AZnO2 (2 wt% Al), respectively. For the sample AZnO10 (10 wt% Al) low frequency dc conduction was not found due to the electrode polarization effect. The measure of the correlation length by inverse of threshold frequency (omega(0)) showed that on application of a dc electric field such length decreases and the decrease in correlation parameter(s) indicates that the correlation between potentials wells of charge particles decreases for the unidirectional nature of dc bias. The comparison between the correlation length and the extent of correlation in the doped ZnO could not be made due to the observation of several threshold frequencies at the extent of higher doping. Such threshold frequencies were explained by the population possibility of correlated charge carriers that responded at different frequencies. For AZnO2 (2% Al), the temperature dependent (from 4.5 to 288 K) resistance study showed that the variable range hopping mechanism was the most dominating conduction mechanism at higher temperature whereas at low temperature region it was influenced by the small polaronic hopping conduction mechanism. There was no significant influence found in these mechanisms on applications of 1, 2 and 3 V as biases.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

On the Origin of Steep I-V Nonlinearity in Mixed-Ionic-Electronic-Conduction-Based Access Devices

Numerical modeling is used to explain the origin of the large ON/OFF ratios, ultralow leakage, and high ON-current densities exhibited by back-end-of-the-line-friendly access devices based on copper-containing mixed-ionic-electronic-conduction (MIEC) materials. Hall effect measurements confirm that the electronic current is hole dominated; a commercial semiconductor modeling tool is adapted to model MIEC. Motion of large populations of copper ions and vacancies leads to exponential increases in hole current, with a turn-ON voltage that depends on material bandgap. Device simulations match experimental observations as a function of temperature, electrode aspect ratio, thickness, and device diameter.

Genetic diversity and proviral DNA load in different neural compartments of HIV-1 subtype C infection

In India, the low prevalence of HIV-associated dementia (HAD) in the Human immunodeficiency virus type 1 (HIV-1) subtype C infection is quite paradoxical given the high-rate of macrophage infiltration into the brain. Whether the direct viral burden in individual brain compartments could be associated with the variability of the neurologic manifestations is controversial. To understand this paradox, we examined the proviral DNA load in nine different brain regions and three different peripheral tissues derived from ten human subjects at autopsy. Using a highly sensitive TaqMan probe-based real-time PCR, we determined the proviral load in multiple samples processed in parallel from each site. Unlike previously published reports, the present analysis identified uniform proviral distribution among the brain compartments examined without preferential accumulation of the DNA in any one of them. The overall viral DNA burden in the brain tissues was very low, approximately 1 viral integration per 1000 cells or less. In a subset of the tissue samples tested, the HIV DNA mostly existed in a free unintegrated form. The V3-V5 envelope sequences, demonstrated a brain-specific compartmentalization in four of the ten subjects and a phylogenetic overlap between the neural and non-neural compartments in three other subjects. The envelope sequences phylogenetically belonged to subtype C and the majority of them were R5 tropic. To the best of our knowledge, the present study represents the first analysis of the proviral burden in subtype C postmortem human brain tissues. Future studies should determine the presence of the viral antigens, the viral transcripts, and the proviral DNA, in parallel, in different brain compartments to shed more light on the significance of the viral burden on neurologic consequences of HIV infection.

Injection barrier induced deviations in space charge limited conduction in doped poly(3-methylthiophene) based devices

The carrier density dependent current-voltage (J V) characteristics of electrochemically prepared poly(3-methylthiophene) (P3MeT) have been investigated in Pt/P3MeT/Al devices, as a function of temperature from 280 to 84 K. In these devices, the charge transport is found to be mainly governed by different transport regimes of space charge limited conduction (SCLC). In a lightly doped device, SCLC controlled by exponentially distributed traps (Vl+1 law, l > 1) is observed in the intermediate voltage range (0.5-2 V) at all temperatures. However, at higher bias (> 2 V), the current deviates from the usual Vl+1 law where the slope is found to be less than 2 of the logJ-logV plot, which is attributed to the presence of the injection barrier. These deviations gradually disappear at higher doping level due to reduction in the injection barrier. Numerical simulations of the Vl+1 law by introducing the injection barrier show good agreement with experimental data. The results show that carrier density can tune the charge transport mechanism in Pt/P3MeT/Al devices to understand the non-Ohmic behavior. The plausible reasons for the origin of injection barrier and the transitions in the transport mechanism with carrier density are discussed. (C) 2015 AIP Publishing LLC.

Computationally efficient models for simulation of non-ideal DC-DC converters operating in continuous and discontinuous conduction modes

This paper discusses dynamic modeling of non-isolated DC-DC converters (buck, boost and buck-boost) under continuous and discontinuous modes of operation. Three types of models are presented for each converter, namely, switching model, average model and harmonic model. These models include significant non-idealities of the converters. The switching model gives the instantaneous currents and voltages of the converter. The average model provides the ripple-free currents and voltages, averaged over a switching cycle. The harmonic model gives the peak to peak values of ripple in currents and voltages. The validity of all these models is established by comparing the simulation results with the experimental results from laboratory prototypes, at different steady state and transient conditions. Simulation based on a combination of average and harmonic models is shown to provide all relevant information as obtained from the switching model, while consuming less computation time than the latter.

Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators

There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.

Parameters Inversion of Fluid-Saturated Porous Media Based on Neural Networks

The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.