Metamodeling approach to predict friction factor of alluvial channel

The flow resistance of an alluvial channel flow is not only affected by the Reynolds number and the roughness conditions but also the Froude number. Froude number is the most basic parameter in the case of the alluvial channel, thus effect of Froude number on resistance to flow should be considered in the formulation of the friction factor, which is not in the case of present available resistance equations. At present, no generally acceptable quantitative description of the effects of the Froude number on hydraulic resistance has been developed. Metamodeling technique, which is particularly useful in modeling a complex processes or where knowledge of the physics is limited, is presented as a tool complimentary to modeling friction factor in alluvial channels. Present work uses, a radial basis metamodel, which is a type of neural network modeling, to find the effect of Froude number on the flow resistance. Based on the experimental data taken from different sources, it has been found that the predicting capability of the present model is on acceptable level. Present work also tries in formulating an empirical equation for resistance in alluvial channel comprising all the three majorm, parameters, namely, roughness parameter, Froude number and Reynolds number. (C) 2009 Elsevier B.V. All rights reserved.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Assessment of SET Logic Robustness Through Noise Margin Modeling

A compact model for noise margin (NM) of single-electron transistor (SET) logic is developed, which is a function of device capacitances and background charge (zeta). Noise margin is, then, used as a metric to evaluate the robustness of SET logic against background charge, temperature, and variation of SET gate and tunnel junction capacitances (CG and CT). It is shown that choosing alpha=CT/CG=1/3 maximizes the NM. An estimate of the maximum tolerable zeta is shown to be equal to plusmn0.03 e. Finally, the effect of mismatch in device parameters on the NM is studied through exhaustive simulations, which indicates that a isin [0.3, 0.4] provides maximum robustness. It is also observed that mismatch can have a significant impact on static power dissipation.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Testing and Modeling of MR Damper and Its Application to SDOF Systems Using Integral Backstepping Technique

Magnetorheological dampers are intrinsically nonlinear devices, which make the modeling and design of a suitable control algorithm an interesting and challenging task. To evaluate the potential of magnetorheological (MR) dampers in control applications and to take full advantages of its unique features, a mathematical model to accurately reproduce its dynamic behavior has to be developed and then a proper control strategy has to be taken that is implementable and can fully utilize their capabilities as a semi-active control device. The present paper focuses on both the aspects. First, the paper reports the testing of a magnetorheological damper with an universal testing machine, for a set of frequency, amplitude, and current. A modified Bouc-Wen model considering the amplitude and input current dependence of the damper parameters has been proposed. It has been shown that the damper response can be satisfactorily predicted with this model. Second, a backstepping based nonlinear current monitoring of magnetorheological dampers for semi-active control of structures under earthquakes has been developed. It provides a stable nonlinear magnetorheological damper current monitoring directly based on system feedback such that current change in magnetorheological damper is gradual. Unlike other MR damper control techniques available in literature, the main advantage of the proposed technique lies in its current input prediction directly based on system feedback and smooth update of input current. Furthermore, while developing the proposed semi-active algorithm, the dynamics of the supplied and commanded current to the damper has been considered. The efficiency of the proposed technique has been shown taking a base isolated three story building under a set of seismic excitation. Comparison with widely used clipped-optimal strategy has also been shown.

Combined Atomic Force Microscopy and Modeling Study of The Evolution of Octadecylamine Films on a Mica Surface

The time evolution of the film thickness and domain formation of octadecylamine molecules adsorbed oil a mica surface is investigated Using atomic force microscopy. The adsorbed Film thickness is determined by measuring the height profile across the mica-amine interface of a mica surface partially immersed in a 15 mM solution of octadecylamine in chloroform. Using this novel procedure, adsorption of amine on mica is found to occur in three distinct stages, with morphologically distinct domain Formation and growth occurring during each stage. In the first stage, where adsorption is primarily in the thin-film regime, all average Film thickness of 0.2 (+/- 0.3) nm is formed for exposure times below 30 s and 0.8 (+/- 0.2) nm for 60 s of immersion time. During this stage, large sample spanning domains are observed. The second stage, which occurs between 60-300 s, is associated with it regime of rapid film growth, and the film thickness increases from about 0.8 to 25 nm during this stage. Once the thick-film regime is established, further exposure to the amine solution results in all increase in the domain area, and it regime of lateral domain growth is observed. In this stage, the domain area coverage grows from 38 to 75%, and the FTIR spectra reveal an increased level of crystallinity in the film. Using it diffusion-controlled model and it two-step Langmuir isotherm, the time evolution of the film growth is quantitatively captured. The model predicts the time at which the thin to thick film transition occurs as well its the time required for complete film growth at longer times. The Ward-Tordai equation is also solved to determine the model parameters in the monolayer (thin-film) regime, which occurs during the initial stages of film growth.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Modeling and analysis of a tunable piezoelectric structure for transverse shear wave generation

An analytical investigation of the transverse shear wave mode tuning with a resonator mass (packing mass) on a Lead Zirconium Titanate (PZT) crystal bonded together with a host plate and its equivalent electric circuit parameters are presented. The energy transfer into the structure for this type of wave modes are much higher in this new design. The novelty of the approach here is the tuning of a single wave mode in the thickness direction using a resonator mass. First, a one-dimensional constitutive model assuming the strain induced only in the thickness direction is considered. As the input voltage is applied to the PZT crystal in the thickness direction, the transverse normal stress distribution induced into the plate is assumed to have parabolic distribution, which is presumed as a function of the geometries of the PZT crystal, packing mass, substrate and the wave penetration depth of the generated wave. For the PZT crystal, the harmonic wave guide solution is assumed for the mechanical displacement and electric fields, while for the packing mass, the former is solved using the boundary conditions. The electromechanical characteristics in terms of the stress transfer, mechanical impedance, electrical displacement, velocity and electric field are analyzed. The analytical solutions for the aforementioned entities are presented on the basis of varying the thickness of the PZT crystal and the packing mass. The results show that for a 25% increase in the thickness of the PZT crystal, there is ~38% decrease in the first resonant frequency, while for the same change in the thickness of the packing mass, the decrease in the resonant frequency is observed as ~35%. Most importantly the tuning of the generated wave can be accomplished with the packing mass at lower frequencies easily. To the end, an equivalent electric circuit, for tuning the transverse shear wave mode is analyzed.

Modeling and Analysis of Noise Margin in SET Logic

In this paper the static noise margin for SET (single electron transistor) logic is defined and compact models for the noise margin are developed by making use of the MIB (Mahapatra-Ionescu-Banerjee) model. The variation of the noise margin with temperature and background charge is also studied. A chain of SET inverters is simulated to validate the definition of various logic levels (like VIH, VOH, etc.) and noise margin. Finally the noise immunity of SET logic is compared with current CMOS logic.

Prediction of flame liftoff height of diffusion/partially premixed jet flames and modeling of mild combustion burners

In this article, a new flame extinction model based on the k/epsilon turbulence time scale concept is proposed to predict the flame liftoff heights over a wide range of coflow temperature and O-2 mass fraction of the coflow. The flame is assumed to be quenched, when the fluid time scale is less than the chemical time scale ( Da < 1). The chemical time scale is derived as a function of temperature, oxidizer mass fraction, fuel dilution, velocity of the jet and fuel type. The present extinction model has been tested for a variety of conditions: ( a) ambient coflow conditions ( 1 atm and 300 K) for propane, methane and hydrogen jet flames, ( b) highly preheated coflow, and ( c) high temperature and low oxidizer concentration coflow. Predicted flame liftoff heights of jet diffusion and partially premixed flames are in excellent agreement with the experimental data for all the simulated conditions and fuels. It is observed that flame stabilization occurs at a point near the stoichiometric mixture fraction surface, where the local flow velocity is equal to the local flame propagation speed. The present method is used to determine the chemical time scale for the conditions existing in the mild/ flameless combustion burners investigated by the authors earlier. This model has successfully predicted the initial premixing of the fuel with combustion products before the combustion reaction initiates. It has been inferred from these numerical simulations that fuel injection is followed by intense premixing with hot combustion products in the primary zone and combustion reaction follows further downstream. Reaction rate contours suggest that reaction takes place over a large volume and the magnitude of the combustion reaction is lower compared to the conventional combustion mode. The appearance of attached flames in the mild combustion burners at low thermal inputs is also predicted, which is due to lower average jet velocity and larger residence times in the near injection zone.

Process variability-aware statistical hybrid modeling of dynamic power dissipation in 65 nm CMOS designs

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65mn gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of Response. Surface Methodology (RSM) using Design of Experiments (DOE) and Least Squares Method (LSM), are employed to generate a "hybrid model" for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by atleast 6X on a normalized basis, against worst case design.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Modeling of formation of gold nanoparticles by citrate method

Properties of nanoparticles are size dependent, and a model to predict particle size is of importance. Gold nanoparticles are commonly synthesized by reducing tetrachloroauric acid with trisodium citrate, a method pioneered by Turkevich et al (Discuss. Faraday Soc. 1951, 11, 55). Data from several investigators that used this method show that when the ratio of initial concentrations of citrate to gold is varied from 0.4 to similar to 2, the final mean size of the particles formed varies by a factor of 7, while subsequent increases in the ratio hardly have any effect on the size. In this paper, a model is developed to explain this widely varying dependence. The steps that lead to the formation of particles are as follows: reduction of Au3+ in solution, disproportionation of Au+ to gold atoms and their nucleation, growth by disproportionation on particle surface, and coagulation. Oxidation of citrate results in the formation of dicarboxy acetone, which aids nucleation but also decomposes into side products. A detailed kinetic model is developed on the basis of these steps and is combined with population balance to predict particle-size distribution. The model shows that, unlike the usual balance between nucleation and growth that determines the particle size, it is the balance between rate of nucleation and degradation of dicarboxy acetone that determines the particle size in the citrate process. It is this feature that is able to explain the unusual dependence of the mean particle size on the ratio of citrate to gold salt concentration. It is also found that coagulation plays an important role in determining the particle size at high concentrations of citrate.