An Online Implementable Differential Evolution Tuned Optimal Guidance Law

This paper proposes a novel application of differential evolution to solve a difficult dynamic optimisation or optimal control problem. The miss distance in a missile-target engagement is minimised using differential evolution. The difficulty of solving it by existing conventional techniques in optimal control theory is caused by the nonlinearity of the dynamic constraint equation, inequality constraint on the control input and inequality constraint on another parameter that enters problem indirectly. The optimal control problem of finding the minimum miss distance has an analytical solution subject to several simplifying assumptions. In the approach proposed in this paper, the initial population is generated around the seed value given by this analytical solution. Thereafter, the algorithm progresses to an acceptable final solution within a few generations, satisfying the constraints at every iteration. Since this solution or the control input has to be obtained in real time to be of any use in practice, the feasibility of online implementation is also illustrated.

Prediction of Compressibility of Overconsolidated Uncemented Soils

Overconsolidated soils exhibit a bilinear e-log p relationship. During virgin compression, microstructural units form larger stable groups, thereby reducing the operating specific surface and, in turn, net osmotic repulsive forces in the soil. The rebound portion of the e-log p curve is consequently flatter. The generalized relationship for compressibility of uncemented soils in the overconsolidated state has been developed in the form of e/eL = 1.122 = 0.188 log pc - 0.0463 log p in which e/eL is the generalized soil state parameter, pc is the preconsolidation pressure in kPa, p is the effective overburden pressure in kPa, e is the in situ void ratio, and eL is the void ratio corresponding to the liquid limit water content (wLG). This relationship can be usefully employed to predict both the preconsolidation pressure and compressibility responses of overconsolidated uncemented soils.

Prediction of Breakdown Voltages of Binary Gas Mixtures in Uniform Electric Fields

A simple equation to predict the breakdown voltages for binary mixtures (Vmix) of electronegative gases (SF6, CCl2F2) and buffer gases (N2, N2O, CO2, air) under uniform electric field has been proposed. Values of Vmix evaluated using this equation for mixtures of SF6-N2, SF6-air, SF6-N2O, SF6-CO2 and CCl2F2-N2 over a wide range of pd show an excellent agreement with the experimentally measured data available in the literature.

Prediction of bubble growth rates and departure volumes in nucleate boiling at isolated sites

A model has been developed to predict heat transfer rates and sizes of bubbles generated during nucleate pool boiling. This model assumes conduction and a natural convective heat transfer mechanism through the liquid layer under the bubble and transient conduction from the bulk liquid. The temperature of the bulk liquid in the vicinity of the bubble is obtained by assuming a turbulent natural convection process from the hot plate to the liquid bulk. The shape of the bubble is obtained by equilibrium analysis. The bubble departure condition is predicted by a force balance equation. Good agreement has been found between the bubble radii predicted by the present theory and the ones obtained experimentally.

Prediction of frequency and amplitude of foundations at resonance

This note presents the statistical analysis carried out on some of the available experimental results to predict the resonant frequency and maximum displacement amplitude of a machine foundation – soil system under vertical vibration as a function of the size and weight of the foundation and of the excitation level. A total of 442 experimental results of Fry, Novak, and Raman have been analysed using nonlinear regression analysis. The results obtained compared well with predictions obtained from the popular theoretical models, and the coefficient of correlation obtained from the analysis was satisfactory in most of the cases.

Dynamic Space Warping Of Strokes For Recognition Of Online Handwritten Characters

This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.

Prediction of Intrinsic kinetics in diffusion disguised immobilized enzyme systems

When immobilized enzyme kinetics are disguised by inter- and intraparticle diffusion effects, an approximate mathematical procedure is indicated whereby experimental data obtained in the limiting ranges of first- and zeroth-order Michaelis-Menten kinetics could be used for the prediction of the kinetic constants.

Comparison of Prediction Based LSF Quantization Methods using Split VQ

Further improvement in performance, to achieve near transparent quality LSF quantization, is shown to be possible by using a higher order two dimensional (2-D) prediction in the coefficient domain. The prediction is performed in a closed-loop manner so that the LSF reconstruction error is the same as the quantization error of the prediction residual. We show that an optimum 2-D predictor, exploiting both inter-frame and intra-frame correlations, performs better than existing predictive methods. Computationally efficient split vector quantization technique is used to implement the proposed 2-D prediction based method. We show further improvement in performance by using weighted Euclidean distance.

Prediction of protein-protein interactions between Helicobacter pylori and a human host

A lack of information on protein-protein interactions at the host-pathogen interface is impeding the understanding of the pathogenesis process. A recently developed, homology search-based method to predict protein-protein interactions is applied to the gastric pathogen, Helicobacter pylori to predict the interactions between proteins of H. pylori and human proteins in vitro. Many of the predicted interactions could potentially occur between the pathogen and its human host during pathogenesis as we focused mainly on the H. pylori proteins that have a transmembrane region or are encoded in the pathogenic island and those which are known to be secreted into the human host. By applying the homology search approach to protein-protein interaction databases DIP and iPfam, we could predict in vitro interactions for a total of 623 H. pylori proteins with 6559 human proteins. The predicted interactions include 549 hypothetical proteins of as yet unknown function encoded in the H. pylori genome and 13 experimentally verified secreted proteins. We have recognized 833 interactions involving the extracellular domains of transmembrane proteins of H. pylori. Structural analysis of some of the examples reveals that the interaction predicted by us is consistent with the structural compatibility of binding partners. Examples of interactions with discernible biological relevance are discussed.

Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition

The rapid increase in genome sequence information has necessitated the annotation of their functional elements, particularly those occurring in the non-coding regions, in the genomic context. Promoter region is the key regulatory region, which enables the gene to be transcribed or repressed, but it is difficult to determine experimentally. Hence an in silico identification of promoters is crucial in order to guide experimental work and to pin point the key region that controls the transcription initiation of a gene. In this analysis, we demonstrate that while the promoter regions are in general less stable than the flanking regions, their average free energy varies depending on the GC composition of the flanking genomic sequence. We have therefore obtained a set of free energy threshold values, for genomic DNA with varying GC content and used them as generic criteria for predicting promoter regions in several microbial genomes, using an in-house developed tool `PromPredict'. On applying it to predict promoter regions corresponding to the 1144 and 612 experimentally validated TSSs in E. coli (50.8% GC) and B. subtilis (43.5% GC) sensitivity of 99% and 95% and precision values of 58% and 60%, respectively, were achieved. For the limited data set of 81 TSSs available for M. tuberculosis (65.6% GC) a sensitivity of 100% and precision of 49% was obtained.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

CAE-based prediction of projectile residual velocity for impact on single and multi-layered metallic armour plates

The present, paper deals with the CAE-based study Of impact of jacketed projectiles on single- and multi-layered metal armour plates using LS-DYNA. The validation of finite element modelling procedure is mainly based on the mesh convergence study using both shell and solid elements for representing single-layered mild steel target plates. It, is shown that the proper choice of mesh density and the strain rate-dependent material properties are essential for all accurate prediction of projectile residual velocity. The modelling requirements are initially arrived at by correlating against test residual velocities for single-layered mild steel plates of different depths at impact velocities in the ran.-c of approximately 800-870 m/s. The efficacy of correlation is adjudged, in terms of a 'correlation index', defined in the paper: for which values close to unity are desirable. The experience gained for single-layered plates is next; used in simulating projectile impacts on multi-layered mild steel target plates and once again a high degree of correlation with experimental residual velocities is observed. The study is repeated for single- and multi-layered aluminium target plates with a similar level of success in test residual velocity prediction. TO the authors' best knowledge, the present comprehensive study shows in particular for the first time that, with a. proper modelling approach, LS-DYNA can be used with a great degree of confidence in designing perforation-resistant single and multi-layered metallic armour plates.

Frechet distance based approach for searching online handwritten documents

We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.

Experimental study on shrinkage behaviour and prediction of shrinkage magnitudes of residual soils

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.