934 resultados para simultaneous interpreting


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This paper presents an advanced single network adaptive critic (SNAC) aided nonlinear dynamic inversion (NDI) approach for simultaneous attitude control and trajectory tracking of a micro-quadrotor. Control of micro-quadrotors is a challenging problem due to its small size, strong coupling in pitch-yaw-roll and aerodynamic effects that often need to be ignored in the control design process to avoid mathematical complexities. In the proposed SNAC aided NDI approach, the gains of the dynamic inversion design are selected in such a way that the resulting controller behaves closely to a pre-synthesized SNAC controller for the output regulation problem. However, since SNAC is based on optimal control theory, it makes the dynamic inversion controller to operate near optimal and enhances its robustness property as well. More important, it retains two major benefits of dynamic inversion: (i) closed form expression of the controller and (ii) easy scalability to command tracking application even without any apriori knowledge of the reference command. Effectiveness of the proposed controller is demonstrated from six degree-of-freedom simulation studies of a micro-quadrotor. It has also been observed that the proposed SNAC aided NDI approach is more robust to modeling inaccuracies, as compared to the NDI controller designed independently from time domain specifications.

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This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

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We report the synthesis and structural characterization of a polymeric ternary copper-cytosine-phenanthroline complex, Cu-4(phen)(3)-(mu(3)-cyt)(2)(mu-OH)(cyt)(OH)Cl-3](n)center dot 16H(2)O, where three cytosine ligands with different binding sites have simultaneously complexed to the four copper metal centres. Interestingly, the complex exhibits two different coordination geometries around the metal centres.

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We report a one-pot hydrothermal synthesis of nitrogen doped reduced graphene oxide (N-rGO) and Ag nanoparticle decorated N-rGO hybrid nanostructures from graphene oxide (GO), metal ions and hexamethylenetetramine (HMT). HMT not only reduces GO and metal ions simultaneously but also acts as the source for the nitrogen (N) dopant. We show that the N-rGO can be used as a metal-free surface enhanced Raman spectroscopy (SERS) substrate, while the Ag nano-particles decorated N-rGO can be used as an effective SERS substrate as well as a template for decorating various other nanostructures on N-rGO.

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We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.

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The nonlinear response of a spherical shallow water model to an imposed heat source in the presence of realistic zonal mean zonal winds is investigated numerically. The solutions exhibit elongated, meridionally tilted ridges and troughs indicative of a poleward dispersion of wave activity. As the speed of the jets is increased, the equatorial Kelvin wave is unaffected but the global Rossby wave train coalesces to form a compact, amplified quadrupole structure that bears a striking resemblance to the observed upper level structure of the Madden-Julian oscillation. In the presence of strong subtropical westerly jets, the advection of planetary vorticity by the meridional flow and relative vorticity by the zonally averaged background flow conspire to create the distinctive quadrupole configuration of flanking Rossby waves.

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Simultaneous measurements of thickness and temperature profile of the lubricant film at chip-tool interface during machining have been studied in this experimental programme. Conventional techniques such as thermography can only provide temperature measurement under controlled environment in a laboratory and without the addition of lubricant. The present study builds on the capabilities of luminescent sensors in addition to direct image based observations of the chip-tool interface. A suite of experiments conducted using different types of sensors are reported in this paper, especially noteworthy are concomitant measures of thickness and temperature of the lubricant. (C) 2014 Elsevier Ltd.

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We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

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A simple yet remarkable, electrochemically activated carbon paste electrode (EACPE) was prepared by successive potential cycling of carbon paste in a 0.1 M NaOH solution and was effectively used for the simultaneous determination of catecholamines such as dopamine (DA), epinephrine (E) and Norepinephrine (NE) in presence of uric acid (UA) and ascorbic acid (AA). Taking DA as the ideal catecholamine, the electrochemical behaviors of DA, UA and AA such as scan rate and pH variation was studied by cyclic voltammetry (CV) in phosphate buffer solution (PBS, pH 7.1). This electrochemical sensor exhibited strong electrocatalytic activity towards the oxidation of a mixture of catecholamines, UA and AA with apparent reduction of overpotentials. Crider optimum conditions, limit of detection (S/N = 3) of DA, E, NE, UA and AA was found to be 0.08, 0.08, 0.07, 0.1 and 6.0 mu M, respectively by differential pulse voltammetry (DPV). The analytical performance of this modified electrode as a biosensor was also demonstrated for the determination of DA, UA and AA in dopamine injection, human urine and vitamin C tablets, respectively, in presence of other interfering substances. (C) 2015 The Electrochemical Society. All-rights reserved.

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A unique approach was adopted to drive the multiwall carbon nanotubes (MWNTs) to the interface of immiscible PVDF-ABS blends by wrapping the nanotubes with a mutually miscible homopolymer (PMMA). A tailor made interface with an improved stress transfer was achieved in the blends with PMMA wrapped MWNTs. This manifested in an impressive 108% increment in the tensile strength and 48% increment in the Young's modulus with 3 wt% PMMA wrapped MWNTs in striking contrast to the neat blends. As the PMMA wrapped MWNTs localized at the interface of PVDF-ABS blends, the electrical conductivity could be tuned with respect to only MWNTs, which were selectively localized in the PVDF phase, driven by thermodynamics. The electromagnetic shielding properties were assessed using a vector network analyser in a broad range of frequency, X-band (8-12 GHz) and Ku-band (12-18 GHz). Interestingly, enhanced EM shielding was achieved by this unique approach. The blends with only MWNTs shielded the EM waves mostly by reflection however, the blends with PMMA wrapped MWNTs (3 wt%) shielded mostly by absorption (62%). This study opens new avenues in designing materials, which show simultaneous improvement in mechanical, electrical conductivity and EM shielding properties.

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In bacteria, alternate mechanisms are known to synthesize N-10-formyltetrahydrofolate (N10-formyl-THF) and formyl glycinamide ribotide (fGAR), which are important in purine biosynthesis. In one of the mechanisms, a direct transfer of one carbon unit from formate allows Fhs to convert tetrahydrofolate to N-10-formyl-THF, and PurT to convert glycinamide ribotide (GAR) to fGAR. Our bioinformatics analysis of fhs and purT genes (encoding Fhs and PurT) showed that in a majority of bacteria (similar to 94%), their presence was mutually exclusive. A large number of organisms possessing fhs lacked purT and vice versa. The phenomenon is so penetrating that even within a genus (Bacillus) if a species possessed fhs it lacked purT and vice versa. To investigate physiological importance of this phenomenon, we used Escherichia coli, which naturally lacks fhs (and possesses purT) as model. We generated strains, which possessed fhs and purT genes in singles or together. Deletion of purT from E. coli in the presence or absence of fhs did not confer a detectable growth disadvantage in pure cultures. However, growth competition assays revealed that the strains possessing either of the single genes outcompeted those possessing both the genes suggesting that mutual exclusion of purT and fhs in organisms confers fitness advantage in mixed cultures.

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NMR-based approach to metabolomics typically involves the collection of two-dimensional (2D) heteronuclear correlation spectra for identification and assignment of metabolites. In case of spectral overlap, a 3D spectrum becomes necessary, which is hampered by slow data acquisition for achieving sufficient resolution. We describe here a method to simultaneously acquire three spectra (one 3D and two 2D) in a single data set, which is based on a combination of different fast data acquisition techniques such as G-matrix Fourier transform (GFT) NMR spectroscopy, parallel data acquisition and non-uniform sampling. The following spectra are acquired simultaneously: (1) C-13 multiplicity edited GFT (3,2)D HSQC-TOCSY, (2) 2D H-1- H-1] TOCSY and (3) 2D C-13- H-1] HETCOR. The spectra are obtained at high resolution and provide high-dimensional spectral information for resolving ambiguities. While the GFT spectrum has been shown previously to provide good resolution, the editing of spin systems based on their CH multiplicities further resolves the ambiguities for resonance assignments. The experiment is demonstrated on a mixture of 21 metabolites commonly observed in metabolomics. The spectra were acquired at natural abundance of C-13. This is the first application of a combination of three fast NMR methods for small molecules and opens up new avenues for high-throughput approaches for NMR-based metabolomics.

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We prove that given a Hecke-Maass form f for SL(2, Z) and a sufficiently large prime q, there exists a primitive Dirichlet character chi of conductor q such that the L-values L(1/2, f circle times chi) and L(1/2, chi) do not vanish.