Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Density matrix renormalization group algorithm for Bethe lattices of spin-1/2 or spin-1 sites with Heisenberg antiferromagnetic exchange

A density matrix renormalization group (DMRG) algorithm is presented for the Bethe lattice with connectivity Z = 3 and antiferromagnetic exchange between nearest-neighbor spins s = 1/2 or 1 sites in successive generations g. The algorithm is accurate for s = 1 sites. The ground states are magnetic with spin S(g) = 2(g)s, staggered magnetization that persists for large g > 20, and short-range spin correlation functions that decrease exponentially. A finite energy gap to S > S(g) leads to a magnetization plateau in the extended lattice. Closely similar DMRG results for s = 1/2 and 1 are interpreted in terms of an analytical three-site model.

Construction of Equivalent Circuit of a Single and Isolated Transformer Winding From FRA Data Using the ABC Algorithm

This paper reports the results of employing an artificial bee colony search algorithm for synthesizing a mutually coupled lumped-parameter ladder-network representation of a transformer winding, starting from its measured magnitude frequency response. The existing bee colony algorithm is suitably adopted by appropriately defining constraints, inequalities, and bounds to restrict the search space and thereby ensure synthesis of a nearly unique ladder network corresponding to each frequency response. Ensuring near-uniqueness while constructing the reference circuit (i.e., representation of healthy winding) is the objective. Furthermore, the synthesized circuits must exhibit physical realizability. The proposed method is easy to implement, time efficient, and problems associated with the supply of initial guess in existing methods are circumvented. Experimental results are reported on two types of actual, single, and isolated transformer windings (continuous disc and interleaved disc).

An Online Actor-Critic Algorithm with Function Approximation for Constrained Markov Decision Processes

We develop an online actor-critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable policy-dependent long-run averages of certain sample path functions. The Lagrange multiplier method is used to handle the inequality constraints. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal solution. We also provide the results of numerical experiments on a problem of routing in a multi-stage queueing network with constraints on long-run average queue lengths. We observe that our algorithm exhibits good performance on this setting and converges to a feasible point.

Damage-tolerant design optimization of laminated composite structures using dispersion of ply angles by genetic algorithm

This article aims to obtain damage-tolerant designs with minimum weight for a laminated composite structure using genetic algorithm. Damage tolerance due to impacts in a laminated composite structure is enhanced by dispersing the plies such that too many adjacent plies do not have the same angle. Weight of the structure is minimized and the Tsai-Wu failure criterion is considered for the safe design. Design variables considered are the number of plies and ply orientation. The influence of dispersed ply angles on the weight of the structure for a given loading conditions is studied by varying the angles in the range of 0 degrees-45 degrees, 0 degrees-60 degrees and 0 degrees-90 degrees at intervals of 5 degrees and by using specific ply angles tailored to loading conditions. A comparison study is carried out between the conventional stacking sequence and the stacking sequence with dispersed ply angles for damage-tolerant weight minimization and some useful designs are obtained. Unconventional stacking sequence is more damage tolerant than the conventional stacking sequence is demonstrated by performing a finite element analysis under both tensile as well as compressive loading conditions. Moreover, a new mathematical function called the dispersion function is proposed to measure the dispersion of ply angles in a laminate. The approach for dispersing ply angles to achieve damage tolerance is especially suited for composite material design space which has multiple local minima.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses. © 2012 American Physical Society.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses.

Synchrotron Small-Angle X-ray Scattering Studies of Hemoglobin Nonaggregation Confined inside Polymer Capsules

The effect of confinement on the structure of hemoglobin (Hb) within polymer capsules was investigated here. Hemoglobin transformed from an aggregated state in solution to a nonaggregated state when confined inside the polymer capsules. This was directly confirmed using synchrotron small-angle X-ray scattering (SAXS) studies. The radius of gyration (R-g) and polydispersity (p) of the proteins in the confined state were smaller compared to those in solution. In fact, the R-g value is very similar to theoretical values obtained using protein structures generated from the Protein Databank. In the temperature range (25-85 degrees C, Tm 59 degrees C), the R-g values for the confined Hb remained constant. This observation is in contrary to the increasing R-g values obtained for the bare Hb in solution. This suggested higher thermal stability of Hb when confined inside the polymer capsule than when in solution. Changes in protein configuration were also reflected in the protein function. Confinement resulted in a beneficial enhancement of the electroactivity of Hb. While Hb in solution showed dominance of the cathodic process (Fe3+ -> Fe2+), efficient reversible Fe3+/Fe2+ redox response is observed in the case of the confined Hb. This has important protein functional implications. Confinement allows the electroactive heme to take up positions favorable for various biochemical activities such as sensing of analytes of various sizes from small to macromolecules and controlled delivery of drugs.

Averting biodiversity collapse in tropical forest protected areas

The rapid disruption of tropical forests probably imperils global biodiversity more than any other contemporary phenomenon(1-3). With deforestation advancing quickly, protected areas are increasingly becoming final refuges for threatened species and natural ecosystem processes. However, many protected areas in the tropics are themselves vulnerable to human encroachment and other environmental stresses(4-9). As pressures mount, it is vital to know whether existing reserves can sustain their biodiversity. A critical constraint in addressing this question has been that data describing a broad array of biodiversity groups have been unavailable for a sufficiently large and representative sample of reserves. Here we present a uniquely comprehensive data set on changes over the past 20 to 30 years in 31 functional groups of species and 21 potential drivers of environmental change, for 60 protected areas stratified across the world's major tropical regions. Our analysis reveals great variation in reserve `health': about half of all reserves have been effective or performed passably, but the rest are experiencing an erosion of biodiversity that is often alarmingly widespread taxonomically and functionally. Habitat disruption, hunting and forest-product exploitation were the strongest predictors of declining reserve health. Crucially, environmental changes immediately outside reserves seemed nearly as important as those inside in determining their ecological fate, with changes inside reserves strongly mirroring those occurring around them. These findings suggest that tropical protected areas are often intimately linked ecologically to their surrounding habitats, and that a failure to stem broad-scale loss and degradation of such habitats could sharply increase the likelihood of serious biodiversity declines.

As(3)p: A Fast Algorithm to Search Structurally Similar Protiens

Protein structure comparison is essential for understanding various aspects of protein structure, function and evolution. It can be used to explore the structural diversity and evolutionary patterns of protein families. In view of the above, a new algorithm is proposed which performs faster protein structure comparison using the peptide backbone torsional angles. It is fast, robust, computationally less expensive and efficient in finding structural similarities between two different protein structures and is also capable of identifying structural repeats within the same protein molecule.

AN ITERATIVE ALGORITHM FOR PHASE RETRIEVAL WITH SPARSITY CONSTRAINTS: APPLICATION TO FREQUENCY DOMAIN OPTICAL COHERENCE TOMOGRAPHY

We address the problem of phase retrieval, which is frequently encountered in optical imaging. The measured quantity is the magnitude of the Fourier spectrum of a function (in optics, the function is also referred to as an object). The goal is to recover the object based on the magnitude measurements. In doing so, the standard assumptions are that the object is compactly supported and positive. In this paper, we consider objects that admit a sparse representation in some orthonormal basis. We develop a variant of the Fienup algorithm to incorporate the condition of sparsity and to successively estimate and refine the phase starting from the magnitude measurements. We show that the proposed iterative algorithm possesses Cauchy convergence properties. As far as the modality is concerned, we work with measurements obtained using a frequency-domain optical-coherence tomography experimental setup. The experimental results on real measured data show that the proposed technique exhibits good reconstruction performance even with fewer coefficients taken into account for reconstruction. It also suppresses the autocorrelation artifacts to a significant extent since it estimates the phase accurately.

Translocation and encapsulation of siRNA inside carbon nanotubes

We report spontaneous translocation of small interfering RNA (siRNA) inside carbon nanotubes (CNTs) of various diameters and chirality using all atom molecular dynamics simulations with explicit solvent. We use umbrella sampling method to calculate the free energy landscape of the siRNA entry and translocation event. Free energy profiles show that siRNA gains free energy while translocating inside CNT, and barrier for siRNA exit from CNT ranges from 40 to 110 kcal/mol depending on CNT chirality and salt concentration. The translocation time tau decreases with the increase of CNT diameter with a critical diameter of 24 angstrom for the translocation. In contrast, double strand DNA of the same sequence does not translocate inside CNT due to large free energy barrier for the translocation. This study helps in understanding the nucleic acid transport through nanopores at microscopic level and may help designing carbon nanotube based sensor for siRNA. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4773302]

Impact of pit-toilet leachate on groundwater chemistry and role of vadose zone in removal of nitrate and E-coli pollutants in Kolar District, Karnataka, India

Assessment of chemistry of groundwater infiltrated by pit-toilet leachate and contaminant removal by vadose zone form the focus of this study. The study area is Mulbagal Town in Karnataka State, India. Groundwater level measurements and estimation of unsaturated permeability indicated that the leachate recharged the groundwater inside the town at the rate of 1 m/day. The average nitrate concentration of groundwater inside the town (148 mg/L) was three times larger than the permissible limit (45 mg/L), while the average nitrate concentration of groundwater outside the town (30 mg/L) was below the permissible limit. The groundwater inside the town exhibited E. coli contamination, while groundwater outside the town was free of pathogen contamination. Infiltration of alkalis (Na+, K+) and strong acids (Cl-, SO4 (2-)) caused the mixed Ca-Mg-Cl type (60 %) and Na-Cl type (28 %) facies to predominate groundwater inside the town, while, Ca-HCO3 (35 %), mixed Ca-Mg-Cl type (35 %) and mixed Ca-Na-HCO3 type (28 %) facies predominated groundwater outside/periphery of town. Reductions in E. coli and nitrate concentrations with vadose zone thickness indicated its participation in contaminant removal. A 4-m thickness of unsaturated sand + soft, disintegrated weathered rock deposit facilitates the removal of 1 log of E. coli pathogen. The anoxic conditions prevailing in the deeper layers of the vadose zone (> 19 m thickness) favor denitrification resulting in lower nitrate concentrations (28-96 mg/L) in deeper water tables (located at depths of -29 to -39 m).