Robust/optimal temperature profile control using neural networks

An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order, finite-dimensional lumped parameter model for this problem is obtained by using Galerkin projection and basis functions designed through the 'Proper Orthogonal Decomposition' technique (POD) and the 'snap-shot' solutions. A suboptimal neurocontroller is obtained with a single-network-adaptive-critic (SNAC). Further contribution of this paper is to develop an online robust controller to account for unmodeled dynamics and parametric uncertainties. A weight update rule is presented that guarantees boundedness of the weights and eliminates the need for persistence of excitation (PE) condition to be satisfied. Since, the ADP and neural network based controllers are of fairly general structure, they appear to have the potential to be controller synthesis tools for nonlinear distributed parameter systems especially where it is difficult to obtain an accurate model.

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Projection Error Propagation-based Regularization (PEPR) method for resistivity reconstruction in Electrical Impedance Tomography (EIT)

A novel Projection Error Propagation-based Regularization (PEPR) method is proposed to improve the image quality in Electrical Impedance Tomography (EIT). PEPR method defines the regularization parameter as a function of the projection error developed by difference between experimental measurements and calculated data. The regularization parameter in the reconstruction algorithm gets modified automatically according to the noise level in measured data and ill-posedness of the Hessian matrix. Resistivity imaging of practical phantoms in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm as well as with Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) with PEPR. The effect of PEPR method is also studied with phantoms with different configurations and with different current injection methods. All the resistivity images reconstructed with PEPR method are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR) techniques. The results show that, the PEPR technique reduces the projection error and solution error in each iterations both for simulated and experimental data in both the algorithms and improves the reconstructed images with better contrast to noise ratio (CNR), percentage of contrast recovery (PCR), coefficient of contrast (COC) and diametric resistivity profile (DRP). (C) 2013 Elsevier Ltd. All rights reserved.

Microstructural characterization of ultrafine-grain interstitial-free steel by X-ray diffraction line profile analysis

This paper highlights the microstructural features of commercially available interstitial free (IF) steel specimens deformed by equal channel angular pressing (ECAP) up to four passes following the route A. The microstructure of the samples was studied by different techniques of X-ray diffraction peak profile analysis as a function of strain (epsilon). It was found that the crystallite size is reduced substantially already at epsilon=2.3 and it does not change significantly during further deformation. At the same time, the dislocation density increases gradually up to epsilon=4.6. The dislocation densities estimated from X-ray diffraction study are found to correlate very well with the experimentally obtained yield strength of the samples.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.

Depth profile composition studies of thin film CdS: Cu,S solar cells using XPS and AES

Surface composition and depth profile studies of hemiplated thin film CdS:CuzS solar cells have been carried out using x-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) techniques. These studies indicate that the junction is fairly diffused in the as-prepared cell. However, heat treatment of the cell at 210°C in air relatively sharpens the junction and improves the cell performance. Using the Cu(2p3p)/S(2p) ratio as well as the Cu(LVV)/(LMM) Auger intensity ratio, it can be inferred that the nominal valency of copper in the layers above the junction is Cut and it is essentially in the CUSS form. Copper signals are observed from layers deep down in the cell. These seem to appear mostly from the grain boundary region. From the observed concentration of Cd, Cu and S in these deeper layers and the Cu(LVV)/(LMM) ratio it appears that the signals from copper essentially originate partly from copper in CuS and partly from Cu2t trapped in the lattice. It is significant to note that the nominal valence state of copper changes rather abruptly from Cut to Cuz+ across the junction.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.

The Electronic Structure of Graphite from Compton Profile Measurements

Compton profile data are used to investigate the ground state wavefunction of graphite. The results of two new $\gamma$-ray measurements are reported and compared with the results of earlier $\gamma$-ray and electron scattering measurements. A tight-binding calculation has been carried out and the results of earlier calculations based on a molecular model and a pseudo-potential wavefunction are considered. The analysis, in terms of the reciprocal form factor, shows that none of the calculations gives an adequate description of the data in the basal plane although the pseudo-potential calculation describes the anisotropy in the plane reasonably well. In the basal plane the zero-crossing theorem appears to be violated and this problem must be resolved before more accurate models can be derived. In the c-axis direction the molecular model and the tight binding calculation give better agreement with the experimental data than does the pseudopotential calculation.

Effect of pulse profile variations on measurement of eccentricity in orbits of Cen X-3 and SMC X-1

It has long been argued that better timing precision allowed by satellites like Rossi X-ray Timing Explorer (RXTE) will allow us to measure the orbital eccentricity and the angle of periastron of some of the bright persistent high-mass X-ray binaries (HMXBs) and hence a possible measurement of apsidal motion in these system. Measuring the rate of apsidal motion allows one to estimate the apsidal motion constant of the mass losing companion star and hence allows for the direct testing of the stellar structure models for these giant stars present in the HMXBs. In the present paper, we use the archival RXTE data of two bright persistent sources, namely Cen X-3 and SMC X-1, to measure the very small orbital eccentricity and the angle of periastron. We find that the small variations in the pulse profiles of these sources, rather than the intrinsic time resolution provided by RXTE, limit the accuracy with which we can measure arrival time of the pulses from these sources. This influences the accuracy with which one can measure the orbital parameters, especially the very small eccentricity and the angle of periastron in these sources. The observations of SMC X-1 in the year 2000 were taken during the high-flux state of the source and we could determine the orbital eccentricity and omega using this data set.

Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Vertical profile of aerosol single scattering albedo over west coast of India during W_ICARB

Altitude profile of aerosol Single Scattering Albedo (SSA), derived from simultaneous in-situ airborne measurements of the coefficients of aerosol absorption and scattering off the west coast of India over the Arabian Sea (AS), during January 2009 is presented. While both the absorption and scattering coefficients decreased with altitude, their vertical structure differed significantly. Consequently, the derived SSA, with a surface value of 0.94, decreased with altitude, illustrating increasing relative dominance of aerosol absorption at higher altitudes. Altitude profile of SSA, when examined in conjunction with that of hemispheric backscatter fraction, revealed that the continental influence on the aerosol properties was higher at higher altitude, rather than the effect of marine environment. During an east-west transect across the peninsular India at an altitude of similar to 2500 m (free troposphere), it was found that the aerosol scattering coefficients remained nearly the same over both east and west coasts. (C) 2010 Elsevier Ltd. All rights reserved.

Implications of Energy Profile and Storage on Energy Harvesting Sensor Link Performance

Energy harvesting sensors (EHS), which harvest energy from the environment in order to sense and then communicate their measurements over a wireless link, provide the tantalizing possibility of perpetual lifetime operation of a sensor network. The wireless communication link design problem needs to be revisited for these sensors as the energy harvested can be random and small and not available when required. In this paper, we develop a simple model that captures the interactions between important parameters that govern the communication link performance of a EHS node, and analyze its outage probability for both slow fading and fast fading wireless channels. Our analysis brings out the critical importance of the energy profile and the energy storage capability on the EHS link performance. Our results show that properly tuning the transmission parameters of the EHS node and having even a small amount of energy storage capability improves the EHS link performance considerably.

Gene expression profile of epithelial cells and mesenchymal cells derived from limbal explant culture

Purpose: Limbal stem cell deficiency is a challenging clinical problem and the current treatment involves replenishing the depleted limbal stem cell (LSC) pool by either limbal tissue transplantation or use of cultivated limbal epithelial cells (LEC). Our experience of cultivating the LEC on denuded human amniotic membrane using a feeder cell free method, led to identification of mesenchymal cells of limbus (MC-L), which showed phenotypic resemblance to bone marrow derived mesenchymal stem cells (MSC-BM). To understand the transcriptional profile of these cells, microarray experiments were carried out.Methods: RNA was isolated from cultured LEC, MC-L and MSC-BM and microarray experiments were carried out by using Agilent chip (4x44 k). The microarray data was validated by using Realtime and semiquntitative reverse transcription polymerase chain reaction. Results: The microarray analysis revealed specific gene signature of LEC and MC-L, and also their complementary role related to cytokine and growth factor profile, thus supporting the nurturing roles of the MC-L. We have also observed similar and differential gene expression between MC-L and MSC-BM.Conclusions: This study represents the first extensive gene expression analysis of limbal explant culture derived epithelial and mesenchymal cells and as such reveals new insight into the biology, ontogeny, and in vivo function of these cells.