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.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

A unique strategy towards high dielectric constant and low loss with multiwall carbon nanotubes anchored onto graphene oxide sheets

Multiwall carbon nanotubes (MWNTs) were anchored onto graphene oxide sheets (GOs) via diazonium and C-C coupling reactions and characterized by spectroscopic and electron microscopic techniques. The thus synthesized MWNT-GO hybrid was then melt mixed with 50/50 polyamide6-maleic anhydride-modified acrylonitrile-butadiene-styrene (PA6-mABS) blend to design materials with high dielectric constant (30) and low dielectric loss. The phase morphology was studied by SEM and it was observed that the MWNT-GO hybrid was selectively localized in the PA6 phase of the blend. The 30 scales with the concentration of MWNT-GO in the blends, which interestingly showed a very low dielectric loss (< 0.2) making them potential candidate for capacitors. In addition, the dynamic storage modulus scales with the fraction of MWNT-GO in the blends, demonstrating their reinforcing capability as well.

Adaptive executions of hyperbolic block-structured AMR applications on GPU systems

A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies.

Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry

Remote sensing of physiological parameters could be a cost effective approach to improving health care, and low-power sensors are essential for remote sensing because these sensors are often energy constrained. This paper presents a power optimized photoplethysmographic sensor interface to sense arterial oxygen saturation, a technique to dynamically trade off SNR for power during sensor operation, and a simple algorithm to choose when to acquire samples in photoplethysmography. A prototype of the proposed pulse oximeter built using commercial-off-the-shelf (COTS) components is tested on 10 adults. The dynamic adaptation techniques described reduce power consumption considerably compared to our reference implementation, and our approach is competitive to state-of-the-art implementations. The techniques presented in this paper may be applied to low-power sensor interface designs where acquiring samples is expensive in terms of power as epitomized by pulse oximetry.

Simultaneous perturbation methods for adaptive labor staffing in service systems

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.

Sulfenyiation of beta-Diketones Using C-H Functionalization Strategy

Sulfenylation of beta-diketones is challenging as beta-diketones undergo deacylation after sulfenylation in the reaction medium. The sulfenylation of beta-diketones without deacylation under metal-free conditions at ambient temperature via a cross dehydrogenative coupling (CDC) strategy is reported. The resultant products can be further manipulated to form alpha,alpha-disubstituted beta-diketones and pyrazoles.

Strategy for Obtaining Bimodal Bubble Size Distribution in Water Blown Polyurethane Foams

Polyurethane foams with multimodal cell distribution exhibit superior mechanical and thermal properties. A technique for generating bimodal bubble size distribution exists in the literature, but it uses supercritical conditions. In the present work, an alternative based on milder operating conditions is proposed. It is a modification of reaction injection molding (RIM), using reactants already seeded with bubbles. The number density of the seeds determines if two nucleating events can occur. A bimodal bubble size distribution is obtained when this happens A mathematical model is used to test this hypothesis by simulating water blown free rise polyurethane foams. The effects of initial concentration of bubbles, temperature of the reactants, and the weight fraction of water are studied. The study reveals that for certain concentrations of initial number of bubbles, when initial temperature and weight fraction of water are high, it is possible to obtain a second nucleation event, leading to bimodal bubble size distribution.

An efficient strategy to develop microwave shielding materials with enhanced attenuation constant

A mutually miscible homopolymer (here polymethyl methacrylate; PMMA) was employed to tailor the interfacial properties of immiscible polycarbonate/styrene acrylonitrile (PC/SAN) blends. In order to design materials that can shield microwave radiation, one of the key properties i.e. electrical conductivity was targeted here using a conducting inclusion; multiwall carbon nanotubes (MWNTs). Owing to higher polarity, MWNTs prefer PC over SAN which though enhance the electrical conductivity of the blends, they don't improve the interfacial properties and results in poor mechanical properties. Hence, an efficient strategy has been adopted here to simultaneously enhance the mechanical, electrical and microwave attenuation properties. Herein, the MWNTs were wrapped by PMMA via in situ polymerization of MMA (methyl methacrylate). This strategy resulted in the migration of PMMA modified MWNTs towards the blend's interface and resulted in an effective stress transfer across the interface leading to improved mechanical and dynamic mechanical properties. Interestingly, the bulk electrical conductivity of the blends was also enhanced, manifesting the improved dispersion of the MWNTs. The state of dispersion of the MWNTs and the phase morphology were assessed using scanning electron microscopy. The microwave attenuation properties were evaluated using a vector network analyzer (VNA) in the X and K-u-band frequencies. The blends with PMMA wrapped MWNTs manifested a -21 dB of shielding effectiveness which suggests attenuation of more than 99% of the incoming microwave radiation. More interestingly, the attenuation constant could be tuned here employing this unique strategy. This study clearly opens a new tool box in designing materials that show improved mechanical, dynamic mechanical, electrical conductivity and microwave shielding properties.

Deployment strategy for controlled morphologies in sessile, mixed colloidal droplets

Colloidal systems offer an effective medium to micro-engineer complex structures without involving sophisticated fabrication procedures. This article presents a deployment strategy of multiple droplets of different colloidal composition and utilizes the inherent capillary flow driven self assembly of nanoparticles to construct stacks of multiple materials on a given glass substrate. Here we used aqueous nano-crystalline titania and nano-amorphous silica solutions as the two materials. Initially, a pure nanotitania (nanosilica) droplet is deployed and allowed to dry partially. Subsequently, a second droplet of pure nanosilica (nanotitania) is deployed co-axially on the partially dried precipitate. The proposed deployment strategy allowed significant morphological differences when the deployment order of nanosilica and nanotitania were interchanged. Compositional analysis performed using EDX (Energy Dispersive X-ray spectroscopy) showed preferential deposition of nanosilica and nanotitania along the radial as well as the axial plane of the final deposit pattern. The underlying mechanism for such a phenomenon could be attributed to the contact line dynamics of a sessile double droplet. We also observe heteroaggregation of the nanosilica-nanotitania interaction along a narrow interface which resulted in nanotitania particles clustering into isolated islands embedded into a matrix of nanosilica particles. Overall, this work elucidates the evaporation driven dynamics of a mixed colloidal system which displays both macroscopic as well as microscopic phenomena. Such a system could be used to generate ordered arrays of functional materials with engineered micro to nano-scale properties.

Influence of Anisotropy on Pavement Responses Using Adaptive Sparse Polynomial Chaos Expansion

Modeling the spatial variability that exists in pavement systems can be conveniently represented by means of random fields; in this study, a probabilistic analysis that considers the spatial variability, including the anisotropic nature of the pavement layer properties, is presented. The integration of the spatially varying log-normal random fields into a linear-elastic finite difference analysis has been achieved through the expansion optimal linear estimation method. For the estimation of the critical pavement responses, metamodels based on polynomial chaos expansion (PCE) are developed to replace the computationally expensive finite-difference model. The sparse polynomial chaos expansion based on an adaptive regression-based algorithm, and enhanced by the combined use of the global sensitivity analysis (GSA) is used, with significant savings in computational effort. The effect of anisotropy in each layer on the pavement responses was studied separately, and an effort is made to identify the pavement layer wherein the introduction of anisotropic characteristics results in the most significant impact on the critical strains. It is observed that the anisotropy in the base layer has a significant but diverse effect on both critical strains. While the compressive strain tends to be considerably higher than that observed for the isotropic section, the tensile strains show a decrease in the mean value with the introduction of base-layer anisotropy. Furthermore, asphalt-layer anisotropy also tends to decrease the critical tensile strain while having little effect on the critical compressive strain. (C) 2015 American Society of Civil Engineers.

Immune responses against hepatitis C virus genotype 3a virus-like particles in mice: A novel VLP prime-adenovirus boost strategy

Chronic hepatitis C virus (HCV) infection represents a major health threat to global population. In India, approximately 15-20% of cases of chronic liver diseases are caused by HCV infection. Although, new drug treatments hold great promise for HCV eradication in infected individuals, the treatments are highly expensive. A vaccine for preventing or treating HCV infection would be of great value, particularly in developing countries. Several preclinical trials of virus-like particle (VLP) based vaccine strategies are in progress throughout the world. Previously, using baculovirus based system, we have reported the production of hepatitis C virus-like particles (HCV-LPs) encoding structural proteins for genotype 3a, which is prevalent in India. In the present study, we have generated HCV-LPs using adenovirus based system and tried different immunization strategies by using combinations of both kinds of HCV-LPs with other genotype 3a-based immunogens. HCV-LPs and peptides based ELISAs were used to evaluate antibody responses generated by these combinations. Cell-mediated immune responses were measured by using T-cell proliferation assay and intracellular cytokine staining. We observed that administration of recombinant adenoviruses expressing HCV structural proteins as final booster enhances both antibody as well as T-cell responses. Additionally, reduction of binding of VLP and JFH1 virus to human hepatocellular carcinoma cells demonstrated the presence of neutralizing antibodies in immunized sera. Taken together, our results suggest that the combined regimen of VLP followed by recombinant adenovirus could more effectively inhibit HCV infection, endorsing the novel vaccine strategy. (C) 2015 Elsevier Ltd. All rights reserved.

A strategy to achieve enhanced electromagnetic interference shielding at ultra-low concentration of multiwall carbon nanotubes in P alpha MSAN/PMMA blends in the presence of a random copolymer PS-r-PMMA

A unique strategy was adopted to achieve an ultra-low electrical percolation threshold of multiwall carbon nanotubes (MWNTs) (0.25 wt%) in a classical partially miscible blend of poly-alpha-methylstyrene-co-acrylonitrile and poly(methyl methacrylate) (P alpha MSAN/PMMA), with a lower critical solution temperature. The polymer blend nanocomposite was prepared by standard melt-mixing followed by annealing above the phase separation temperature. In a two-step mixing protocol, MWNTs were initially melt-mixed with a random PS-r-PMMA copolymer and subsequently diluted with 85/15 P alpha MSAN/PMMA blends in the next mixing step. Mediated by the PS-r-PMMA, the MWNTs were mostly localized at the interface and bridged the PMMA droplets. This strategy led to enhanced electromagnetic interference (EMI) shielding effectiveness at 0.25 wt% MWNTs through multiple scattering from MWNT-covered droplets, as compared to the blends without the copolymer, which were transparent to electromagnetic radiation.

Stabilization of a Tetrahedral (Mn5+O4) Chromophore in Ternary Barium Oxides as a Strategy toward Development of New Turquoise/Green-Colored Pigments

An experimental investigation of the stabilization of the turquoise-colored chrornophore (Mn5+O4) in various oxide hosts, viz., A(3)(VO4)(2) (A = Ba, Sr, Ca), YVO4, and Ba2MO4 (M = Ti, Si), has been carried out. The results reveal that substitution of Mn5+O4 occurs in Ba-3(VO4)(2) forming the entire solid solution series Ba-3(V1-x MnxO4)(2) (0 < x <= 1.0), while with the corresponding strontium derivative, only up to about 10% of Mn5+O4 substitution is possible. Ca-3(VO4)(2) and YVO4 do not stabilize Mn5+O4 at all. With Ba2MO4 (M = Ti, Si), we could prepare only partially substituted materials, Ba2M1-xMn5+O4+x/2 for x up to 0.15, that are turquoise-colored. We rationalize the results that a large stabilization of the O 2p-valence band states occurs in the presence of the electropositive barium that renders the Mn5+ oxidation state accessible in oxoanion compounds containing PO43-, VO43-, etc. By way of proof-of-concept, we synthesized new turquoise-colored Mn5+O4 materials, Ba-5(BO3)(MnO4)(2)Cl and Ba-5(BO3)(PO4)(MnO4)Cl, based on the apatite-Ba-5(PO4)(3)Cl-structure.