990 resultados para V-CENTERS


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Considering voltage stability as a static viability problem, this paper takes a particular concern of Q-V characteristics and reflects on certain notions that do not seem to have been explicitly mentioned or derived in the existing documented literature. The equations of Q-V characteristics are rederived in exactness, some salient points on the curve are discovered and analysed. The results of the analysis are illustrated through a case study

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The fluorescence quenching studies of carboxamide namely (E)-N-(3-Chlorophenyl)-2-(3,4,5-trimethoxybenzylideneamino)-4,5,6,7 tetrahydrobenzob]thiophene-3-carboxamide ENCTTTC] by aniline and carbon tetrachloride in six different solvents namely toluene, cyclohexane, n-hexane, n-heptane, n-decane and n-pentane have been carried out at room temperature with a view to understand the quenching mechanisms. The Stern-Volmer (S-V) plots have been found to be nonlinear with a positive deviation for all the solvents studied. In order to interpret these results we have invoked the ground state complex formation and sphere of action static quenching models. Using these models various quenching rate parameters have been determined. The magnitudes of these parameters suggest that sphere of action static quenching model agrees well with the experimental results. Hence the positive deviation is attributed to the static and dynamic quenching. Further, with the use of Finite Sink approximation model, it was possible to check these bimolecular reactions as diffusion-limited and to estimate independently distance parameter R' and mutual diffusion coefficient D. Finally an effort has been made to correlate the values of R' and D with the values of the encounter distance R and the mutual coefficient D determined using the Edward's empirical relation and Stokes Einstein relation. (C) 2011 Elsevier B.V. All rights reserved.

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CaSiO3:Dy3+ (1-5 mol%) nanophosphors have been prepared by a low temperature solution combustion method. The structural and luminescence (ionoluminescence; IL and photoluminescence; PL) studies have been carried out for pristine and ion irradiated samples. The XRD patterns of pristine sample show a prominent peak at (320) for the monoclinic structure of beta-CaSiO3. Upon ion irradiation, the intensity of the prominent peak is decreased at the fluence of 7.81 x 10(12) ions cm(-2) and at higher fluence of 15.62 x 10(12) ions cm(-2), the prominent peak completely vanishes. The decrease in peak intensity might be due to the stress induced point defects. On-line IL and in situ PL studies have been carried out on pelletized samples bombarded with 100 MeV Si7+ ions with fluences in the range (7.81-15.62) x 10(12) ions cm(-2). The characteristic emission peaks at 481,574, 664 and 754 nm recorded in both IL and PL are attributed to the luminescence centers activated by Dy3+ ions. It is found that IL and PL emissions intensity decreases with increase in Si7+ ion fluence. The decrease in intensity can be due to the destruction of Si-O-Si and O-Si-O type species present on the surface of the sample. FTIR studies also confirm the Si-O-Si and O-Si-O type species observed to be sensitive for swift heavy ion (SHI) irradiated samples. (C) 2012 Elsevier B.V. All rights reserved.

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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.

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In view of the importance of the suicides in the high temperature applications, the diffusion behaviour is compared in different systems for two types of silicides, XSi2 and X5Si3 (X=Nb, Mo, V). Atomic mechanism of diffusion and defects present in the structure are discussed. In both the phases, Si has faster diffusion rate than the metal species. This is expected from the nearest neighbour (NN) bonds present in the XSi2 phase but rather unusual in the X5Si3 phase. Relative mobilities of the species calculated indicate the presence of high concentration of Si antisites. Moreover, the concentration of the defects is different in different systems to find different diffusion rates.

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Recently nano scale zero valent iron particles (nZVI) have been considered as smart adsorbent for environmental and groundwater remediation. Although several synthetic methods are available for the preparation of nZVI, air stable nZVI are not available for remediation works. Further, challenges demand synthesis of nZVI without stabilizers and capping agents. A modified methodology for the synthesis of air stable nZVI has been developed without any capping agents and characterized by powder X-Ray Diffraction (XRD), Scanning Electron Microscopy Energy-dispersive X-Ray (SEM-EDS), Transmission Electron Microscopy (TEM) and X-Ray Photoelectron Spectroscopy (XPS). The results of the present study suggest that the synthetic nZVI are air-stable over a period of one year and consists of particles of 30-40 nm in diameter. Although a layer of less than 3 am thick oxide/hydroxide is observed by TEM and XPS, it appears to be due to oxidation of outer surface during analysis. Adsorption study has shown that the synthetic nZVI are more effective adsorbent than the commercial nZVI and can remove simultaneously arsenite As-III] and arsenate As-V] from water without prior reduction of As-V to As-III. The removal process is adsorptive rather than precipitative and the removal of As-III is greater than that of As-V.

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A 12 V Substrate-Integrated PbO2-Activated Carbon hybrid ultracapacitor (SI-PbO2-AC HUCs) with silica-gel sulfuric acid electrolyte is developed and performance tested. The performance of the silica-gel based hybrid ultracapacitor is compared with flooded and AGM-based HUCs. These HUCs comprise substrate-integrated PbO2 (SI-PbO2) as positive electrodes and high surface-area activated carbon with dense graphite-sheet substrate as negative electrodes. 12 V SI-PbO2-AC HUCs with flooded, AGM and gel electrolytes are found to have capacitance values of 308 F, 184 F, and 269 F at C-rate and can be pulse charged and discharged for 100,000 cycles with only a nominal decrease in their capacitance values. The best performance is exhibited by gel-electrolyte HUCs.

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Users can rarely reveal their information need in full detail to a search engine within 1--2 words, so search engines need to "hedge their bets" and present diverse results within the precious 10 response slots. Diversity in ranking is of much recent interest. Most existing solutions estimate the marginal utility of an item given a set of items already in the response, and then use variants of greedy set cover. Others design graphs with the items as nodes and choose diverse items based on visit rates (PageRank). Here we introduce a radically new and natural formulation of diversity as finding centers in resistive graphs. Unlike in PageRank, we do not specify the edge resistances (equivalently, conductances) and ask for node visit rates. Instead, we look for a sparse set of center nodes so that the effective conductance from the center to the rest of the graph has maximum entropy. We give a cogent semantic justification for turning PageRank thus on its head. In marked deviation from prior work, our edge resistances are learnt from training data. Inference and learning are NP-hard, but we give practical solutions. In extensive experiments with subtopic retrieval, social network search, and document summarization, our approach convincingly surpasses recently-published diversity algorithms like subtopic cover, max-marginal relevance (MMR), Grasshopper, DivRank, and SVMdiv.

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This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

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This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

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Raman spectroscopic study on Oxyfluoro Vanadate glasses containing various proportions of lithium fluoride and rubidium fluoride was carried out to see an effect of mixture of alkali on vanadium-oxygen (V-O) bond length. Glasses with a general formula 40V(2)O(5) - 30BaF(2) - (30 - x) LiF - xRbF (x = 0-30) were prepared. Room temperature Raman spectra of these glass samples were recorded in back scattering geometry. The data presented is in ``reduced Raman intensity'' form with maximum peak scaled to 100. We have used v = Aexp(BR), where A and B are fitting parameters, to correlate the bond length R with Raman scattering frequency v. We observed that variation in bond length and its distribution about a most probable value can be correlated to the alkali environment present in these glasses. We also observed that all rubidium environment around the network forming unit is more homogenous than all lithium environment.

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A porous carbon foam (CF) electrode modified with a reduced graphene oxide-Ag (rGO-Ag) nanocomposite has been fabricated to purify water. It can perform as an antibacterial device by killing pathogenic microbes with the aid of a 1.5 V battery, with very little power consumption. The device is recycled ten times with good performance for long term usage. It is shown that the device may be implemented as a fast water purifier to deactivate the pathogens in drinking water.

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The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.