163 resultados para Visual magnitudes measurements


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The present study examines the shrinkage behaviour of residually derived black cotton (BC) soil and red soil compacted specimens that were subjected to air-drying from the swollen state. The soil specimens were compacted at varying dry density and moisture contents to simulate varied field conditions. The void ratio and moisture content of the swollen specimens were monitored during the drying process and relationship between them is analyzed. Shrinkage is represented as reduction in void ratio with decrease in water content of soil specimens. It is found to occur in three distinct stages. Total shrinkage magnitude depends on the type of clay mineral present. Variation in compaction conditions effect marginally total shrinkage magnitudes of BC soil specimens but have relatively more effect on red soil specimens. A linear relation is obtained between total shrinkage magnitude and volumetric water content of soil specimens in swollen state and can be used to predict the shrinkage magnitude of soils.

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We describe a noniterative method for recovering optical absorption coefficient distribution from the absorbed energy map reconstructed using simulated and noisy boundary pressure measurements. The source reconstruction problem is first solved for the absorbed energy map corresponding to single- and multiple-source illuminations from the side of the imaging plane. It is shown that the absorbed energy map and the absorption coefficient distribution, recovered from the single-source illumination with a large variation in photon flux distribution, have signal-to-noise ratios comparable to those of the reconstructed parameters from a more uniform photon density distribution corresponding to multiple-source illuminations. The absorbed energy map is input as absorption coefficient times photon flux in the time-independent diffusion equation (DE) governing photon transport to recover the photon flux in a single step. The recovered photon flux is used to compute the optical absorption coefficient distribution from the absorbed energy map. In the absence of experimental data, we obtain the boundary measurements through Monte Carlo simulations, and we attempt to address the possible limitations of the DE model in the overall reconstruction procedure.

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Surface models of biomolecules have become crucially important for the study and understanding of interaction between biomolecules and their environment. We argue for the need for a detailed understanding of biomolecular surfaces by describing several applications in computational and structural biology. We review methods used to model, represent, characterize, and visualize biomolecular surfaces focusing on the role that geometry and topology play in identifying features on the surface. These methods enable the development of efficient computational and visualization tools for studying the function of biomolecules.

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Suitable pin-to-hole interference can significantly increase the fatigue life of a pin joint. In practical design, the initial stresses due to interference are high and they are proportional to the effective interference. In experimental studies on such joints, difficulties have been experienced in estimating the interference accurately from physical measurements of pin and hole diameters. A simple photoelastic method has been developed to determine the effective interference to a high degree of accuracy. This paper presents the method and reports illustrative data from a successful application thereof.

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Results of measurements at a high frequency on reverse bias capacitance of copper-doped germanium junctions are reported. Phenomenal increase in capacitance is found in the breakdown region, particularly at low temperatures.

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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.

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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.

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The slow reaction in an Al-5 wt.% Ag alloy has been investigated by resistivity measurements. The "slope change" method gave an activation energy of 1.25 eV for silver diffusion during the slow reaction. The existence of an excess concentration of vacancies in equilibrium with the dislocation loops seems to be responsible for the slow reaction. The presence of silver inhibits the nucleation of dislocation loops by holding up the quenched-in vacancies in solution. There is no indication of the presence of a third stage in the low-temperature ageing process of this alloy.

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This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.

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Starting from beam and target spin systems which are polarized in the usual way by applying external magnetic fields, measurements of appropriate final state tensor parameters, viz., {t0,1k, k=1,...,2j} of particle d with spin j in a reaction a+b→d+c1+c2+. . .are suggested to determine the reaction amplitudes in spin space free from any associated discrete ambiguity.

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A pulsed field gradient spin echo NMR spectrometer has been assembled by interfacing a programmable pulse generator and a data acquisition system designed and fabricated in our laboratory with other imported units. Calibration results of the magnetic field gradients are presented.

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It is shown that thermally stimulated photocurrent measurements provide a simple and effective method of determining the activation energy of thermal regeneration rate of EL2 from the metastable state to the normal state in undoped semi‐insulating GaAs. The thermal regeneration rate r is found to be 2.5×108 exp(−0.26 eV/kT) s−1.