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.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Hybrid peptide hairpins containing alpha- and omega-amino acids: Conformational analysis of decapeptides with unsubstituted beta-, gamma-, and delta-residues at positions 3 and 8

The effects of inserting unsubstituted omega-amino acids into the strand segments of model beta-hairpin peptides was investigated by using four synthetic decapeptides, Boc-Lcu-Val-Xxx-Val-D-Pro-Gly-Leu-Xxx-Val-Val- OMe: pepticle 1 (Xxx=Gly), pepticle 2 (Xxx=beta Gly=beta hGly=homoglycine, beta-glycine), pepticle 3 (Xxx=gamma Abu=gamma-aminobutyric acid), pepticle 4 (Xxx= delta Ava=delta-aminovaleric acid). H-1 NMR studies (500 MHz, methanol) reveal several critical cross-strand NOEs, providing evidence for P-hairpin conformations in peptides 2-4. In peptide 3, the NMR results support the formation of the nucleating turn, however, evidence for cross-strand registry is not detected. Single-crystal X-ray diffraction studies of peptide 3 reveal a beta-hairpin conformation for both molecules in the crystallographic asymmetric unit, stabilized by four cross-strand hydrogen bonds, with the gamma Abu residues accommodated within the strands. The D-Pro-Gly segment in both molecules (A,B) adopts a type II' beta-turn conformation. The circular dichroism spectrum for peptide 3 is characterized by a negative CD band at 229 rim, whereas for peptides 2 and 4, the negative band is centered at 225 nm, suggesting a correlation between the orientation of the amide units in the strand segments and the observed CD pattern.

Polypeptide Helices in Hybrid Peptide Sequences

A new class of polypeptide helices in hybrid sequences containing alpha-, beta-, and gamma-residues is described. The molecular conformations in crystals determined for the synthetic peptides Boc-Leu-Phe-Val-Aib-beta Phe-Leu-Phe-Val-OMe 1 (beta Phe: (S)-beta(3)-homophenylalanine) and Boc-Aib-Gpn-AibGpn-OM2(Gpn:1-(aminomethyl)cycl hexaneacetic acid) reveal expanded helical turns in the hybrid sequences (alpha alpha beta)(n) and (ay), In 1, a repetitive helical structure composed Of C-14 hydrogen-bonded units is observed, whereas 2 provides an example of a repetitive C-12 hydrogen-bonded structure. Using experimentally determined backbone torsion angles for the hydrogen-bonded units formed by hybrid sequences, we have generated energetically favorable hybrid helices. Conformational parameters are provided for C-11, C-12, C-13, C-14, and C-15 helices in hybrid sequences.

Multiple conformational states in crystals and in solution in alpha gamma hybrid peptides. Fragility of the C-12 helix in short sequences

The conformational properties of foldamers generated from alpha gamma hybrid peptide sequences have been probed in the model sequence Boc-Aib-Gpn-Aib-Gpn-NHMe. The choice of alpha-aminoisobutyryl (Aib) and gabapentin (Gpn) residues greatly restricts sterically accessible coil formational space. This model sequence was anticipated to be a short segment of the alpha gamma C-12 helix, stabilized by three successive 4 -> 1 hydrogen bonds, corresponding to a backbone-expanded analogue of the alpha polypeptide 3(10)-helix. Unexpectedly, three distinct crystalline polymorphs were characterized in the solid state by X-ray diffraction. In one form, two successive C-12 hydrogen bonds were obtained at the N-terminus, while a novel C-17 hydrogen-bonded gamma alpha gamma turn was observed at the C-terminus. In the other two polymorphs, isolated C-9 and C-7 hydrogen-bonded turns were observed at Gpn (2) and Gpn (4). Isolated C-12 and C-9 turns were also crystallographically established in the peptides Boc-Aib-Gpn-Aib-OMe and Boc-Gpn-Aib-NHMe, respectively. Selective line broadening of NH resonances and the observation of medium range NH(i)<-> NH(i+2) NOEs established the presence of conformational heterogeneity for the tetrapeptide in CDCl3 solution. The NMR results are consistent with the limited population of the continuous C-12 helix conformation. Lengthening of the (alpha gamma)(n) sequences in the nonapeptides Boc-Aib-Gpn-Aib-Gpn-Aib-Gpn-Aib-Gpn-Xxx (Xxx = Aib, Leu) resulted in the observation of all of the sequential NOEs characteristic of an alpha gamma C-12 helix. These results establish that conformational fragility is manifested in short hybrid alpha gamma sequences despite the choice of conformationally constrained residues, while stable helices are formed on chain extension.

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

The Hybrid approach introduced by the authors for at-site modeling of annual and periodic streamflows in earlier works is extended to simulate multi-site multi-season streamflows. It bears significance in integrated river basin planning studies. This hybrid model involves: (i) partial pre-whitening of standardized multi-season streamflows at each site using a parsimonious linear periodic model; (ii) contemporaneous resampling of the resulting residuals with an appropriate block size, using moving block bootstrap (non-parametric, NP) technique; and (iii) post-blackening the bootstrapped innovation series at each site, by adding the corresponding parametric model component for the site, to obtain generated streamflows at each of the sites. It gains significantly by effectively utilizing the merits of both parametric and NP models. It is able to reproduce various statistics, including the dependence relationships at both spatial and temporal levels without using any normalizing transformations and/or adjustment procedures. The potential of the hybrid model in reproducing a wide variety of statistics including the run characteristics, is demonstrated through an application for multi-site streamflow generation in the Upper Cauvery river basin, Southern India. (C) 2004 Elsevier B.V. All rights reserved.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A hybrid computer for X-ray crystallography

A hybrid computer for structure factor calculations in X-ray crystallography is described. The computer can calculate three-dimensional structure factors of up to 24 atoms in a single run and can generate the scatter functions of well over 100 atoms using Vand et al., or Forsyth and Wells approximations. The computer is essentially a digital computer with analog function generators, thus combining to advantage the economic data storage of digital systems and simple computing circuitry of analog systems. The digital part serially selects the data, computes and feeds the arguments into specially developed high precision digital-analog function generators, the outputs of which being d.c. voltages, are further processed by analog circuits and finally the sequential adder, which employs a novel digital voltmeter circuit, converts them back into digital form and accumulates them in a dekatron counter which displays the final result. The computer is also capable of carrying out 1-, 2-, or 3-dimensional Fourier summation, although in this case, the lack of sufficient storage space for the large number of coefficients involved, is a serious limitation at present.

HFinFET: A Scalable, High Performance, Low Leakage Hybrid n-Channel FET

In this letter, we propose the design and simulation study of a novel transistor, called HFinFET, which is a hybrid of an HEMT and a FinFET, to obtain excellent performance and good OFF-state control. Followed by the description of the design, 3-D device simulation has been performed to predict the characteristics of the device. The device has been benchmarked against published state of the art HEMT as well as planar and nonplanar Si n-MOSFET data of comparable gate length using standard benchmarking techniques.