Enhanced dielectric response of ZrO2 upon Ti doping and introduction of O vacancies

We determine the electronic properties and dielectric response of zirconia (ZrO2) with oxygen vacancies (O vacancies) and Ti doping using first-principles density functional theory calculations based on pseudopotentials and a plane wave basis. We find significantly enhanced static dielectric response in zirconia with Ti doping and introduction of oxygen vacancies. Softening of phonon modes are responsible for the enhanced dielectric response of doped samples compared to pure zirconia.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Manifestation of geometric frustration on magnetic and thermodynamic properties of the pyrochlores Sm2X2O7 (X=Ti,Zr)

We present here magnetization, specific heat, and Raman studies on single-crystalline specimens of the first pyrochlore member Sm2Ti2O7 of the rare-earth titanate series. Its analogous compound Sm2Zr2O7 in the rare-earth zirconate series is also investigated in the polycrystalline form. The Sm spins in Sm2Ti2O7 remain unordered down to at least T=0.5 K. The absence of magnetic ordering is attributed to very small values of exchange (θcw∼−0.26 K) and dipolar interaction (μeff∼0.15 μB) between the Sm3+ spins in this pyrochlore. In contrast, the pyrochlore Sm2Zr2O7 is characterized by a relatively large value of Sm-Sm spin exchange (θcw∼−10 K); however, long-range ordering of the Sm3+ spins is not established at least down to T=0.67 K due to frustration of the Sm3+ spins on the pyrochlore lattice. The ground state of Sm3+ ions in both pyrochlores is a well-isolated Kramers doublet. The higher-lying crystal field excitations are observed in the low-frequency region of the Raman spectra of the two compounds recorded at T=10 K. At higher temperatures, the magnetic susceptibility of Sm2Ti2O7 shows a broad maximum at T=140 K, while that of Sm2Zr2O7 changes monotonically. Whereas Sm2Ti2O7 is a promising candidate for investigating spin fluctuations on a frustrated lattice, as indicated by our data, the properties of Sm2Zr2O7 seem to conform to a conventional scenario where geometrical frustration of the spin excludes their long-range ordering.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Phase diagram of the system Ca-Ti-O at 1200 K

Phase relations in the system Ca-Ti-O have been established by equilibration of several samples at 1200 K for prolonged periods and identification of phases in quenched samples by optical and scanning electron microscopy, XRD and EDS. Samples representing 20 compositions in the ternary system were analyzed. There was negligible solid solubility of Ca in the phases along the binary Ti-O, and of Ti in CaO. Four ternary oxides were identified: CaTiO3, Ca4Ti3O10 and Ca3Ti2O7 containing tetravalent titanium, and CaTi2O4 containing trivalent titanium. Tie-lines link calcium titanite (CaTi2O4) with the three calcium titanates (CaTiO3, Ca4Ti3O10 and Ca3Ti2O7), CaO, oxygen excess TiO1+delta and stoichiometric TiO. Tie-lines connect CaTiO3 with TiO2-x, Magneli phases TinO2n-1 (28 >= n >= 4), Ti3O5, Ti2O3 and TiO1+delta. CaO was found to coexist with TiO, and Ti-O solid solutions alpha and beta. The phase diagram is useful for understanding the mechanisms and kinetics of direct calciothermic reduction of TiO2 to metal and electrochemical reduction of TiO2 using graphite anode and molten CaCl2 electrolyte.

Multilevel Dodecagonal Space Vector Generation for Open-end Winding Induction Motor Drive Using Conventional Three Level Inverters

A novel dodecagonal space vector structure for induction motor drive is presented in this paper. It consists of two dodecagons, with the radius of the outer one twice the inner one. Compared to existing dodecagonal space vector structures, to achieve the same PWM output voltage quality, the proposed topology lowers the switching frequency of the inverters and reduces the device ratings to half. At the same time, other benefits obtained from existing dodecagonal space vector structure are retained here. This includes the extension of the linear modulation range and elimination of all 6+/-1 harmonics (n=odd) from the phase voltage. The proposed structure is realized by feeding an open-end winding induction motor with two conventional three level inverters. A detailed calculation of the PWM timings for switching the space vector points is also presented. Simulation and experimental results indicate the possible application of the proposed idea for high power drives.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.

Study of (Ba3MMWO9)-M-II-W-IV (M-II = Ca, Zn; M-IV = Ti, Zr) perovskite oxides: Competition between 3C and 6H perovskite structures

We describe an investigation of (Ba3MMWO9)-M-II-W-IV oxides for M-II = Ca, Zn, and other divalent metals and M-IV = Ti, Zr. In general, a 1:2-ordered 6H (hexagonal, P6(3)/mmc) perovskite structure is stabilized at high temperatures (1300 degrees C) for all of the (Ba3MTiWO9)-Ti-II oxides investigated. An intermediate phase possessing a partially ordered 1:1 double perovskite (3C) structure with the cation distribution, Ba-2(Zn2/3Ti1/3)(W2/3Ti1/3)O-6, is obtained at 1200 degrees C for Ba3ZnTiWO9. Sr substitution for Ba in the latter stabilizes the cubic 3C structure instead of the 6H structure. A metastable Ba3CaZrWO9 that adopts the 3C (cubic, Fm (3) over barm) structure has also been synthesized by a low-temperature metathesis route. Besides yielding several new perovskite oxides that may be useful as dielectric ceramics, the present investigation provides new insights into the complex interplay of crystal chemistry (tolerance factor) and chemical bonding (anion polarization and d(0)-induced distortion of metal-oxygen octahedra) in the stabilization of 6H versus 3C perovskite structures for the (Ba3MMWO9)-M-II-W-IV series.

Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particularentrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for Reduced Harmonic Distortion and Switching Loss

Novel switching sequences can be employed in spacevector-based pulsewidth modulation (PWM) of voltage source inverters. Differentswitching sequences are evaluated and compared in terms of inverter switching loss. A hybrid PWM technique named minimum switching loss PWM is proposed, which reduces the inverter switching loss compared to conventional space vector PWM (CSVPWM) and discontinuous PWM techniques at a given average switching frequency. Further, four space-vector-based hybrid PWM techniques are proposed that reduce line current distortion as well as switching loss in motor drives, compared to CSVPWM. Theoretical and experimental results are presented.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.