Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Some aspects of multilevel load-frequency control of a power system

The application of multilevel control strategies for load-frequency control of interconnected power systems is assuming importance. A large multiarea power system may be viewed as an interconnection of several lower-order subsystems, with possible change of interconnection pattern during operation. The solution of the control problem involves the design of a set of local optimal controllers for the individual areas, in a completely decentralised environment, plus a global controller to provide the corrective signal to account for interconnection effects. A global controller, based on the least-square-error principle suggested by Siljak and Sundareshan, has been applied for the LFC problem. A more recent work utilises certain possible beneficial aspects of interconnection to permit more desirable system performances. The paper reports the application of the latter strategy to LFC of a two-area power system. The power-system model studied includes the effects of excitation system and governor controls. A comparison of the two strategies is also made.

Some aspects of multilevel load-frequency control of a power system

The application of multilevel control strategies for load-frequency control of interconnected power systems is assuming importance. A large multiarea power system may be viewed as an interconnection of several lower-order subsystems, with possible change of interconnection pattern during operation. The solution of the control problem involves the design of a set of local optimal controllers for the individual areas, in a completely decentralised environment, plus a global controller to provide the corrective signal to account for interconnection effects. A global controller, based on the least-square-error principle suggested by Siljak and Sundareshan, has been applied for the LFC problem. A more recent work utilises certain possible beneficial aspects of interconnection to permit more desirable system performances. The paper reports the application of the latter strategy to LFC of a two-area power system. The power-system model studied includes the effects of excitation system and governor controls. A comparison of the two strategies is also made.

The effect of suction on boundary layer for rotating flows with or without magnetic field

The effect of suction on the steady laminar incompressible boundarylayer flow for a stationary infinite disc with or without magnetic field, when the fluid at a large distance from the surface of the disc undergoes a solid body rotation, has been studied. The governing coupled nonlinear equations have been solved numerically using the shooting method with least square convergence criterion. It has been found that suction tends to reduce the velocity overshoot and damp the oscillation.

Multicarrier analysis of magnetotransport data at low and high electric fields

We present some results on multicarrier analysis of magnetotransport data, Both synthetic as well as data from narrow gap Hg0.8Cd0.2Te samples are used to demonstrate applicability of various algorithms vs. nonlinear least square fitting, Quantitative Mobility Spectrum Analysis (QMSA) and Maximum Entropy Mobility Spectrum Analysis (MEMSA). Comments are made from our experience oil these algorithms, and, on the inversion procedure from experimental R/sigma-B to S-mu specifically with least square fitting as an example. Amongst the conclusions drawn are: (i) Experimentally measured resistivity (R-xx, R-xy) should also be used instead of just the inverted conductivity (sigma(xx), sigma(xy)) to fit data to semiclassical expressions for better fits especially at higher B. (ii) High magnetic field is necessary to extract low mobility carrier parameters. (iii) Provided the error in data is not large, better estimates to carrier parameters of remaining carrier species can be obtained at any stage by subtracting highest mobility carrier contribution to sigma from the experimental data and fitting with the remaining carriers. (iv)Even in presence of high electric field, an approximate multicarrier expression can be used to guess the carrier mobilities and their variations before solving the full Boltzmann equation.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Effect of plasticizers on magnesium-poly(ethyleneoxide) polymer electrolyte

Solid polymer electrolytes (SPEs) of poly(ethyleneoxide) and magnesium triflate, which are plasticized with propylene carbonate (PC), ethylene carbonate (EC) and a mixture of PC and EC, are studied for their conductivity, ac impedance of the Mg I SPE interface, cyclic voltammetry, infrared spectroscopy and differential scanning calorimetry. in the presence of plasticizers, the ionic conductivity (a) increases from a value of 1 x 10(-8) S cm(-1) to about 1 x 10(-4) S cm(-1) at ambient temperature. The a is found to follow a VTF relationship with temperature. The values of the activation energy, pre-exponential factor and equilibrium glass transition temperature are shown to depend on the concentration of plasticizer. Ac impedance studies indicate lower interfacial impedance of Mg/plasticized SPE than stainless steel/plasticized SPE. The impedance spectra are analyzed using a non-linear least square curve fitting technique and the interfacial resistance of Mg/plasticized SPE is evaluated. The cyclic voltammetric results suggest a quasireversible type of Mg/Mg2+ couple in plasticized SPE. (C) 2000 Elsevier Science B.V. All rights reserved.

Solution cation-binding properties of segmented polyethylene oxide-A C-13 NMR spectroscopic study

Two segmented polyethylene oxides, SPEO-3 and SPEO-4, were prepared using a novel transetherification methodology. Their structures were confirmed by H-1 and C-13 NMR spectroscopy. The complexation of these SPEO's with alkali-metal ions in solution was investigated by C-13 NMR spectroscopy. The mole-fraction method was used to determine the complexation ratio of SPEO with LIClO4 at 25 degrees C, which showed that these formed 1:1 (polymer repeat unit/salt) complexes. The association constant, K, for the complex formation was calculated from the variation of the chemical shift values with salt concentration, using a standard nonlinear least-square fitting procedure. The maximum change in chemical shift (Delta delta) and the K values suggest that both SPEO-3 and SPEO-4 formed stronger complexes with lithium salts than with sodium salts. Unexpectedly, the K values were found to be different, when the variation of delta of different carbons was used in the fitting procedure. This suggests that several possible complexed species may be in equilibrium with the uncomplexed one. Structurally similar model compounds were also prepared and their complexation studies indicated that all of them also formed 1:1 complexes with Li salts. Interestingly, it was observed that the polymers gave higher K values suggesting the formation of more stable complexes in polymers when compared to the model analogues. (C) 2000 John Wiley & Sons, Inc.

Anharmonic Effects in Germanium

A precise X-ray investigation is carried out to probe the lowest-order anharmonic contribution of the atomic potential of the germanium atom. A total number of 1052 reflections (h + k + l = 4n and 4n +/- 1) are precisely measured at room temperature using a spherical single crystal of germanium and using a Nonius CAD-4 X-ray diffractometer with crystal monochromatized MoKalpha radiation. A least-square refinement program is used to refine the harmonic and anharmonic thermal parameters of the crystal. The refinement gives beta(Ge) = (-0.749 +/- 1.79) x 10-(16) J nm-3 with B(Ge) = (0.528 +/- 0.004) x 10(-2) nm2. The reliability index (R) amounts to 1.71% for germanium.

Kinetics of hydrogen evolution on submicron size Co, Ni, Pd and CoNi alloy powder electrodes by d.c. polarization and a.c. impedance studies

Submicron size Co, Ni and Co-Ni alloy powders have been synthesized by the polyol method using the corresponding metal malonates and Pd powder by reduction of PdOx in methanol. The kinetics of the hydrogen evolution reaction ( HER) in 6 M KOH electrolyte have been studied on electrodes made from the pressed powders. The d.c. polarization measurements have resulted in a value close to 120 mV decade(-1) for the Tafel slope, suggesting that the HER follows the Volmer-Heyrovsky mechanism. The values of exchange current density (i(o)) are in the range 1-10 mA cm(-2) for electrodes fabricated in the study. The a.c. impedance spectra measured at several potentials in the HER region showed a single semicircle in the Nyquist plots. Exchange current density (i(o)) and energy transfer coefficient (alpha) have been calculated by employing a nonlinear least square-fitting program.

Methodology for alleviation of voltage excursions in large power systems

This paper presents a method for minimizing the sum of the square of voltage deviations by a least-square minimization technique, and thus improving the voltage profile in a given system by adjusting control variables, such as tap position of transformers, reactive power injection of VAR sources and generator excitations. The control variables and dependent variables are related by a matrix J whose elements are computed as the sensitivity matrix. Linear programming is used to calculate voltage increments that minimize transmission losses. The active and reactive power optimization sub-problems are solved separately taking advantage of the loose coupling between the two problems. The proposed algorithm is applied to IEEE 14-and 30-bus systems and numerical results are presented. The method is computationally fast and promises to be suitable for implementation in real-time dispatch centres.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Topology optimization-based design of a compliant aircraft compliant aircraft wing for morphing leading and trailing edges

In this paper, we present a methodology for designing a compliant aircraft wing, which can morph from a given airfoil shape to another given shape under the actuation of internal forces and can offer sufficient stiffness in both configurations under the respective aerodynamic loads. The least square error in displacements, Fourier descriptors, geometric moments, and moment invariants are studied to compare candidate shapes and to pose the optimization problem. Their relative merits and demerits are discussed in this paper. The `frame finite element ground structure' approach is used for topology optimization and the resulting solutions are converted to continuum solutions. The introduction of a notch-like feature is the key to the success of the design. It not only gives a good match for the target morphed shape for the leading and trailing edges but also minimizes the extension of the flexible skin that is to be put on the airfoil frame. Even though linear small-displacement elastic analysis is used in optimization, the obtained designs are analysed for large displacement behavior. The methodology developed here is not restricted to aircraft wings; it can be used to solve any shape-morphing requirement in flexible structures and compliant mechanisms.