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Optimum Partition Parameter of Divide-And-Conquer Algorithm ab 48.99 € als Taschenbuch: Solving Closest-Pair Problem. Aus dem Bereich: Bücher, Wissenschaft, Technik,

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The possible dynamics of one dimensional, piecewise-linear, discontinuous systems can be varied and very complex. Just a small variation in the parameters can lead to completely different bifurcations. This creates the problem of effectively investigating such a system. The solution that has been found in previous works is to investigate so-called organizing centers.An organizing center is a point with high codimension in the parameter space of a dynamical system. It has the property that a large number of bifurcation curves intersect there. Usually these bifurcation curves do not exist only near the organizing center, but cover a large part of the parameter space. This means that a large part of the parameter space contains the same bifurcations than the neighborhood of the organizing center. So, to understand this large part of the parameter space it is sufficient to investigate the neighborhood of the organizing center. The consequence of that is that the parameter space of a dynamical system can be investigated very effectively by first finding organizing centers in it and by then investigating the neighborhoods of these organizing centers. The result of such an investigation is one or several bifurcation scenarios, that the organizing center is said to “produce”.In the past this was done for several specific one dimensional, piecewise-linear, discontinuous systems with one point of discontinuity (two-partition systems). For each of these specific systems the organizing centers were identified and the bifurcation structures that they produce were investigated. In contrast to these previous works that investigated specific two-partition systems this work now presents a comprehensive overview over the organizing centers of piecewise-linear two-partition systems in general, thus presenting an overview over all asymptotic dynamics that are possible in these systems. Note that there is strong evidence that the results of this work also apply to systems with one point of discontinuity which are not piecewise-linear but piecewise-smooth.Two partition systems have in the general case four parameters: the offsets and the slopes. It is shown in this work that all organizing centers of these systems can be described by setting the slopes to fixed values and then describing the resulting offset plane. Furthermore, the possible slope combinations can be grouped into 16 different cases, so that the organizing centers that correspond to a specific case all produce similar bifurcation scenarios. These 16 cases can be reduced to 10 cases due to topological conjugacy.In addition to the description of organizing centers in piecewise-linear two-partition systems, this work also contains an investigation of organizing centers in one dimensional, piecewise-linear, discontinuous systems with two points of discontinuity (three-partition systems). This investigation is less comprehensive than the description of the dynamics of two-partition systems. The reason for that is that a full description of the dynamics of three-partition systems would be beyond the scope of this work. Instead, the investigation concentrates on a specific type of organizing center which produces a specific bifurcation scenario, the socalled nested period incrementing (NPI) scenario.The NPI scenario appears in piecewise-linear three-partition systems that contain a positive slope, a negative slope and a slope that is zero. Depending on the specific system it can appear in a fully and a partially developed form. The fully developed NPI scenario contains a specific set of periodic orbits. The partially developed NPI scenario only contains a subset of these periodic orbits. Both forms of the scenario are described in detail.

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The solvation parameter model is now well established as a useful tool for obtaining quantitative structure-property relationships for chemical, biomedical and environmental processes. The model correlates a free-energy related property of a system to six free-energy derived descriptors describing molecular properties. In this book the use of gas chromatography, reversed-phase liquid chromatography, micellar electrokinetic chromatography, and two-phase partitioning for determining solute descriptors is described. A large database of experimental retention factors and partition coefficients is constructed after first applying selection tools to remove unreliable experimental values and an optimized collection of varied compounds with descriptor values suitable for calibrating chromatographic systems is presented.

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59,00 € *

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The book discusses the effects of data nonnormality, model misspecification, sample size, and effect size on testing latent variable interactions through an inspection of the Jöreskog and Yang's (1996) model. Mattson's (1997) method was used to generate nonnormal latent variables in this Monte Carlo study. One covariance parameter was deleted for investigating the influence of misspecified models. The simulation involved a balanced experimental design, with 3 × 2 × 3 × 3 = 54 combinations. Data analysis focused on bias of estimating parameters, standard errors, model fit indexes. Variance partition was conducted to further examine the unique and combined influence of the factors (i.e., data nonnormality, model specification, sample size, effect size). Results indicated that data nonnormality and model misspecification had large effects on fit indexes (e.g., SRMR, RMSEA). Also, severe nonnormality led to a large bias of estimating the interaction effect. Implications of and recommendations for testing latent variable interactions are discussed.

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The automatic analysis of data acquired with hyperdimensional sensors is rather challenging since it should be carried out in hyperdimensional feature spaces. The huge size of the feature space involves the so-called curse of dimensionality. This latter is due to the unbalancing between the number of features and the number of samples. In this book, it is proposed to exploit the whole information available in the original hyperdimensional feature space by means of the fusion of multiple regression methods. The development of the proposed multiple regression systems will include three main steps. The first one is related to the partition of the original hyperdimensional feature space into subspaces of reduced dimensionality. The second step consists in training in each of the subspaces obtained in the previous step a regression method. Finally, in the third and final step, the results provided by the different regression methods will be combined in order to produce a global estimate of the physical parameter of interest with an expected higher accuracy with respect to what can be achieved by the classical regression approach based on feature selection.

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The present research project encompasses Robust stability and performance analysis of power systems and it has six definite parts : - The unification of the structural configuration dealing with stability of the closed - loop feedback systems. Stability studies of capacitive loading of synchronous generator of a thermal power station. Regulator excitation dynamics of a synchronous generator. The design of stabilizers and the determination of the largest radius of the hyper-sphere in the parameter space. The determination of the controller stability of a close-loop system by investigating and examining encirclement of the 1+ j0 point by the locus of G (jw) C (jw). D partition method for stability studies with the range of K have been discussed. The analysis and design of a family of voltage controllers based on numerical convex optimization.

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49,00 € *

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Divide and Conquer is a well known algorithmic procedure for solving closest pair problems.In this procedure, the problem is partitioned into two parts until the problem is trivially solvable. It is theoretically and practically observed that sometimes partitioning the problem space into more than two parts can give better performances. In this work we proposed that dividing the problem space into (n) number of parts can give better result.We organized the book as follows,in Chapter 2, we discussed Divide-and-Conquer algorithm and Closest-Pair problem. In Chapter 3, we reviewed previous works about Divide-and- Conquer algorithm to solve Closest-Pair problem. In Chapter 4, we present detailed version of the Basic-2 algorithm and give the correspondent proof of correctness as presented by Jos e C. Pereira & Fernando G. Lobo (2010). In Chapter 5, some previous proofs are given where is proven that 2-ary system is not the best choice for all the algorithms. The results in this chapter are based on [Kayk98]. In Chapter 6, a new procedure of divide and conquer rule is presented to solve the closest pair of points problem. In Chapter 7, we discussed future work and conclude our work.

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The goal of clustering is to decompose or partition a data set into groups such that both the intra-group similarity and the inter-group dissimilarity are maximized. In many applications, the size of the data that needs to be clustered is much more than what can be processed at a single site. Further, the data to be clustered could be inherently distributed. The increasing demand to scale up to these massive data sets which are inherently distributed over networks with limited bandwidth and computational resources has led to methods for parallel and distributed data clustering. In this book, we present a cohesive framework for cluster identification and outlier detection for distributed data. The data is either distributed originally because of its production at different locations or is distributed in order to gain a computational speed up. We use a parameter free clustering algorithm to cluster the data at local sites.

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96,90 CHF *

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Anbieter: Orell Fuessli CH

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