Uncertainty And Evolutionary Optimization: A Novel Approach

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201

Circular dichroism of magneto-phonon resonance in doped graphene

Polarization resolved, Raman scattering response due to E$_{2g}$ phonon in monolayer graphene has been investigated in magnetic fields up to 29 T. The hybridization of the E$_{2g}$ phonon with only the fundamental inter Landau level excitation (involving the n=0 Landau level) is observed and only in one of the two configurations of the circularly crossed polarized excitation and scattered light. This polarization anisotropy of the magneto-phonon resonance is shown to be inherent to relatively strongly doped graphene samples, with carrier concentration typical for graphene deposited on SiO$_2$

Purification and characterization of cyclodextrin glucanotransferase from alkalophilic Bacillus sp. G1

A cyclodextrin glucanotransferase (CGTase) was successively purified by ammonium sulphate precipitation, and affinity chromatography on a-CD (epoxy)-Sepharose 6B column. The specific activity of the CGTase was increased approximately 2200-fold, from 8.43 U/mg protein to 18,866 U/mg protein. SDS-PAGE showed that the purified CGTase was homogeneous and the molecular weight of the purified CGTase was about 75 kDa. The molecular weight of the enzyme that was estimated by gel filtration under native condition was 79 kDa. This has indicated that Bacillus sp. G1 CGTase is a monomeric protein. The isoelectric point (pI) of the enzyme was about 8.8. Characterization of the enzyme exhibited optimum pH and temperature of 6.0 and 60 8C, respectively. The enzyme was stable from pH 7.0 to 9.0 and retained its high activity up to 60 8C. However, in the presence of 20 mM Ca2+, the purified CGTase is able to prolong its thermal stability up to 70 8C. CGTase was strongly inhibited by ZnSO4, CuSO4, CoCl2, FeSO4, FeCl3 and EDTA. Km and Vmax for the purified enzyme were 0.15 mg/ml and 60.39 mg bcyclodextrin/( ml min), respectively, with soluble starch as substrate. In cyclodextrin production, tapioca starch was found to be the best substrate used to produce CDs. The enzyme produced g- and b-CD in the ratio of 0.11:0.89 after 24 h incubation at 60 8C, without the presence of any selective agents

Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis

Wheat is one of the most important crops in Australia, and the identification of young plants is an important step towards developing an automated system for monitoring crop establishment and also for differentiating crop from weeds. In this paper, a framework to differentiate early narrow-leaf wheat from two common weeds from their digital images is developed. A combination of colour, texture and shape features is used. These features are reduced to three descriptors using Principal Component Analysis. The three components provide an effective and significant means for distinguishing the three grasses. Further analysis enables threshold levels to be set for the discrimination of the plant species. The PCA model was evaluated on an independent data set of plants and the results show accuracy of 88% and 85% in the differentiation of ryegrass and brome grass from wheat, respectively. The outcomes of this study can be integrated into new knowledge in developing computer vision systems used in automated weed management

Nonlinear analysis of drainage systems to examine surface deformation: an example from Potwar Plateau (Northern Pakistan)

We devise a procedure in order to characterize the relative vulnerability of the Earth's surface to tectonic deformation using the geometrical characteristics of drainage systems. The present study focuses on the nonlinear analysis of drainage networks extracted from Digital Elevation Models in order to localize areas strongly influenced by tectonics. We test this approach on the Potwar Plateau in northern Pakistan. This area is regularly affected by damaging earthquakes. Conventional studies cannot pinpoint the zones at risk, as the whole region is characterized by a sparse and diffuse seismicity. Our approach is based on the fact that rivers tend to linearize under tectonic forcing. Thus, the low fractal dimensions of the Swan, Indus and Jehlum Rivers are attributed to neotectonic activity. A detailed textural analysis is carried out to investigate the linearization, heterogeneity and connectivity of the drainage patterns. These textural aspects are quantified using the fractal dimension, as well as lacunarity and succolarity analysis. These three methods are complimentary in nature, i.e. objects with similar fractal dimensions can be distinguished further with lacunarity and/or succolarity analysis. We generate maps of fractal dimensions, lacunarity and succolarity values using a sliding window of 2.5 arc minutes by 2.5 arc minutes (2.5'×2.5'). These maps are then interpreted in terms of land surface vulnerability to tectonics. This approach allowed us to localize several zones where the drainage system is highly structurally controlled on the Potwar Plateau. The region located between Muree and Muzaffarabad is found to be prone to destructive events whereas the area westward from the Indus seems relatively unaffected. We conclude that a nonlinear analysis of the drainage system is an efficient additional tool to locate areas likely to be affected by massive destructing events affecting the Earth's surface and therefore threaten human activities

OlinInfo, September 2009

Newsletter of the Franklin W. Olin Library at Rollins Colleg

Two-dimensional block transforms and their properties

Includes bibliographical references.For two-dimensional (2-D) digital filters implemented by a block recursive equation, explicit relations between their frequency characteristics and those of scalar filter are obtained. Specifically, these include the relation between the discrete-time Fourier transform (DTFT) of the block recursive equation and that of the scalar 2-D difference equation, and the relation between the block matrix transfer function of the block processor and the scalar transfer function. These relations that are independent of the type of realization of the block processor have been obtained using the eigenvalue properties of a special type of circulant matrix introduced in this correspondence

Detection and classification of buried dielectric anomalies using neural networks–further results

Includes bibliographical references.The development of a neural network-based detection and classification system for use with buried dielectric anomalies is the main focus of this paper. Several methods of data representation are developed to study their effects on the trainability and generalization capabilities of the neural networks. The method of Karhonen-Loeve (KL) transform is used to extract energy dependent features and to reduce the dimensionality of the weight space of the original data set. To extract the shape-dependent features of the data, another data preprocessing method known as Zernike moments is also studied for its use in the detector/classifier system. The effects of different neural network paradigms, architectural variations, and selection of proper training data on detection and classification rates are studied. Simulation results for nylon and wood targets indicate superior performance when compared to conventional schemes.This work was supported by the U.S. Army Belvoir RDandE Center under contract No. DAAL03-86-D-0001