959 resultados para Fuzzy K Nearest Neighbor


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Let where be a set of points in d-dimensional space with a given metric rho. For a point let r (p) be the distance of p with respect to rho from its nearest neighbor in Let B(p,r (p) ) be the open ball with respect to rho centered at p and having the radius r (p) . We define the sphere-of-influence graph (SIG) of as the intersection graph of the family of sets Given a graph G, a set of points in d-dimensional space with the metric rho is called a d-dimensional SIG-representation of G, if G is isomorphic to the SIG of It is known that the absence of isolated vertices is a necessary and sufficient condition for a graph to have a SIG-representation under the L (a)-metric in some space of finite dimension. The SIG-dimension under the L (a)-metric of a graph G without isolated vertices is defined to be the minimum positive integer d such that G has a d-dimensional SIG-representation under the L (a)-metric. It is denoted by SIG (a)(G). We study the SIG-dimension of trees under the L (a)-metric and almost completely answer an open problem posed by Michael and Quint (Discrete Appl Math 127:447-460, 2003). Let T be a tree with at least two vertices. For each let leaf-degree(v) denote the number of neighbors of v that are leaves. We define the maximum leaf-degree as leaf-degree(x). Let leaf-degree{(v) = alpha}. If |S| = 1, we define beta(T) = alpha(T) - 1. Otherwise define beta(T) = alpha(T). We show that for a tree where beta = beta (T), provided beta is not of the form 2 (k) - 1, for some positive integer k a parts per thousand yen 1. If beta = 2 (k) - 1, then We show that both values are possible.

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Extended x-ray absorption fine-structure studies have been performed at the Zn K and Cd K edges for a series of solid solutions of wurtzite Zn1-xCdxS samples with x = 0.0, 0.1, 0.25, 0.5, 0.75, and 1.0, where the lattice parameter as a function of x evolves according to the well-known Vegard's law. In conjunction with extensive, large-scale first-principles electronic structure calculations with full geometry optimizations, these results establish that the percentage variation in the nearest-neighbor bond distances are lower by nearly an order of magnitude compared to what would be expected on the basis of lattice parameter variation, seriously undermining the chemical pressure concept. With experimental results that allow us to probe up to the third coordination shell distances, we provide a direct description of how the local structure, apparently inconsistent with the global structure, evolves very rapidly with interatomic distances to become consistent with it. We show that the basic features of this structural evolution with the composition can be visualized with nearly invariant Zn-S-4 and Cd-S-4 tetrahedral units retaining their structural integrity, while the tilts between these tetrahedral building blocks change with composition to conform to the changing lattice parameters according to the Vegard's law within a relatively short length scale. These results underline the limits of applicability of the chemical pressure concept that has been a favored tool of experimentalists to control physical properties of a large variety of condensed matter systems.

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We report the localized charge carrier transport of two-phase composite Zn1-x Ni (x) O/NiO (0 a parts per thousand currency sign x a parts per thousand currency sign 1) using the temperature dependence of ac-resistivity rho (ac)(T) across the N,el temperature T (N) (= 523 K) of nickel oxide. Our results provide strong evidence to the variable range hopping of charge carriers between the localized states through a mechanism involving spin-dependent activation energies. The temperature variation of carrier hopping energy epsilon (h)(T) and nearest-neighbor exchange-coupling parameter J (ij)(T) evaluated from the small poleron model exhibits a well-defined anomaly across T (N). For all the composite systems, the average exchange-coupling parameter (J (ij))(AVG) nearly equals to 70 meV which is slightly greater than the 60-meV exciton binding energy of pure zinc oxide. The magnitudes of epsilon (h) (similar to 0.17 eV) and J (ij) (similar to 11 meV) of pure NiO synthesized under oxygen-rich conditions are consistent with the previously reported theoretical estimation based on Green's function analysis. A systematic correlation between the oxygen stoichiometry and, epsilon (h)(T) and J (ij)(T) is discussed.

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By using ab initio electronic structure calculations within density functional theory, we study the structural, electronic, and magnetic properties of Si doped with a transition metal impurity. We consider the transition metals of the 3d series V, Cr, Mn, Fe, Co, and Ni. To get insight into the level filling mechanism and the magnetization saturation, we first investigate the transition metal-Si alloys in the zinc-blende structure. Next, we investigate the doping of bulk Si with a transition metal atom, in which it occupies the substitutional site, the interstitial site with tetrahedral symmetry, and the interstitial site with hexagonal symmetry. It is found that all of these transition metal impurities prefer an interstitial position in Si. Furthermore, we show that it is possible to interpret the electronic and magnetic properties by using a simple level filling picture and a comparison is made to Ge doped with the same transition metal atoms. In order to get insight into the effect of a strained environment, we calculate the formation energy as a function of an applied homogeneous pressure and we show that an applied pressure can stabilize the substitutional position of transition metal impurities in Si. Finally, the energies of the ferromagnetic states are compared to those of the antiferromagnetic states. It is shown that the interstitial site of the Mn dopant helps us to stabilize the nearest neighbor substitutional site to realize the ferromagnetic state. For doping of Si with Cr, a ferrimagnetic behavior is predicted.

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A novel analog-computation system using a quantum-dot cell network is proposed to solve complex problems. Analog computation is a promising method for solving a mathematical problem by using a physical system analogous to the problem. We designed a novel quantum-dot cell consisting of three-stacked. quantum dots and constructed a cell network utilizing the nearest-neighbor interactions between the cells. We then mapped a graph 3-colorability problem onto the network so that the single-electron configuration of the network in the ground state corresponded to one of the solutions. We calculated the ground state of the cell network and found solutions to the problems. The results demonstrate that analog computation is a promising approach for solving complex problems.

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A novel analog-computation system using a quantum-dot cell network is proposed to solve complex problems. Analog computation is a promising method for solving a mathematical problem by using a physical system analogous to the problem. We designed a novel quantum-dot cell consisting of three-stacked. quantum dots and constructed a cell network utilizing the nearest-neighbor interactions between the cells. We then mapped a graph 3-colorability problem onto the network so that the single-electron configuration of the network in the ground state corresponded to one of the solutions. We calculated the ground state of the cell network and found solutions to the problems. The results demonstrate that analog computation is a promising approach for solving complex problems.

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完整性是数据质量的一个重要维度,由于数据本身固有的不确定性、采集的随机性及不准确性,导致现实应用中产生了大量具有如下特点的数据集:1)数据规模庞大;2)数据往往是不完整、不准确的.因此将大规模数据集分段到不同的数据窗口中处理是数据处理的重要方法,但缺失数据估算的相关研究大都忽视了数据集的特点和窗口的应用,而且回定大小的数据窗17容易造成算法的准确性和性能受窗口大小及窗口内数据值分布的影响.假设数据满足一定的领域相关的约束,首先提出了一种新的基于时间的动态自适应数据窗口检测算法,并基于此窗口提出了一种改进的模糊k-均值聚类算法来进行不完整数据的缺失数据估算.实验表明较之其他算法,不仅能更适应数据集的特点,具有较好的性能,而且能够保证准确性.

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This report mainly focused on methodology of spatiotemporal patterns (STP) of cognitive potentials or event-related potentials (ERP). The representation of STP of brain wave is an important issue in the research of neural assemblies. This paper described methods of parametric 3D head or brain modeling and its corresponding interpolation for functional imaging based on brain waves. The 3D interpolation method is an extension of cortical imaging technique. It can be used with transformed domain features of brain wave on realistic head or brain models. The simulating results suggests that it is a better method in comparison with the global nearest neighbor technique. A stable and definite STP of brainwave referred as microstate may become basic element for comprehending sophisticated cognitive processes. Fuzzy c-mean algorithm was applied to segmentation STPs of ERP into microstates and corresponding membership functions. The optimal microstate number was estimated with both the trends of objective function against increasing clustering number and the decorrelation technique base don microstate shape similarity. Comparable spatial patterns may occur at different moments in time with fuzzy indices and thus the serial processing limit generated from behavioral methods has been break through. High-resolution frequency domain analysis was carried out with multivariate autoregressive model. Bases on a 3D interpolation mentioned above, visualization of dynamical coordination of cerebral network was realized with magnitude-squared partial coherence. Those technique illustrated with multichannel ERP of 9 subjects when they undertook Strop task. Stroop effects involves several regions during post-perception stage with technique of statistical parameter mapping based F-test [SPM(F)]. As SPM(F) suggested task effects occurred within 100 ms after stimuli presentation involved several sensory regions, it may reflect the top-down processing effect.

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We investigate numerically the ground state phase diagram of the one-dimensional extended Hubbard model, including an on--site interaction U and a nearest--neighbor interaction V. We focus on the ground state phases of the model in the V >> U region, where previous studies have suggested the possibility of dominant superconducting pairing fluctuations before the system phase separates at a critical value V=V_PS. Using quantum Monte Carlo methods on lattices much larger than in previous Lanczos diagonalization studies, we determine the boundary of phase separation, the Luttinger Liquid correlation exponent K_rho, and other correlation functions in this region. We find that phase separation occurs for V significantly smaller than previously reported. In addition, for negative U, we find that a uniform state re-enters from phase separation as the electron density is increased towards half filling. For V < V_PS, our results show that superconducting fluctuations are not dominant. The system behaves asymptotically as a Luttinger Liquid with K_rho < 1, but we also find strong low-energy (but gapped) charge-density fluctuations at a momentum not expected for a standard Luttinger Liquid.

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A quantum Monte Carlo algorithm is constructed starting from the standard perturbation expansion in the interaction representation. The resulting configuration space is strongly related to that of the Stochastic Series Expansion (SSE) method, which is based on a direct power series expansion of exp(-beta*H). Sampling procedures previously developed for the SSE method can therefore be used also in the interaction representation formulation. The new method is first tested on the S=1/2 Heisenberg chain. Then, as an application to a model of great current interest, a Heisenberg chain including phonon degrees of freedom is studied. Einstein phonons are coupled to the spins via a linear modulation of the nearest-neighbor exchange. The simulation algorithm is implemented in the phonon occupation number basis, without Hilbert space truncations, and is exact. Results are presented for the magnetic properties of the system in a wide temperature regime, including the T-->0 limit where the chain undergoes a spin-Peierls transition. Some aspects of the phonon dynamics are also discussed. The results suggest that the effects of dynamic phonons in spin-Peierls compounds such as GeCuO3 and NaV2O5 must be included in order to obtain a correct quantitative description of their magnetic properties, both above and below the dimerization temperature.

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One-and two-dimensional cellular automata which are known to be fault-tolerant are very complex. On the other hand, only very simple cellular automata have actually been proven to lack fault-tolerance, i.e., to be mixing. The latter either have large noise probability ε or belong to the small family of two-state nearest-neighbor monotonic rules which includes local majority voting. For a certain simple automaton L called the soldiers rule, this problem has intrigued researchers for the last two decades since L is clearly more robust than local voting: in the absence of noise, L eliminates any finite island of perturbation from an initial configuration of all 0's or all 1's. The same holds for a 4-state monotonic variant of L, K, called two-line voting. We will prove that the probabilistic cellular automata Kε and Lε asymptotically lose all information about their initial state when subject to small, strongly biased noise. The mixing property trivially implies that the systems are ergodic. The finite-time information-retaining quality of a mixing system can be represented by its relaxation time Relax(⋅), which measures the time before the onset of significant information loss. This is known to grow as (1/ε)^c for noisy local voting. The impressive error-correction ability of L has prompted some researchers to conjecture that Relax(Lε) = 2^(c/ε). We prove the tight bound 2^(c1log^21/ε) < Relax(Lε) < 2^(c2log^21/ε) for a biased error model. The same holds for Kε. Moreover, the lower bound is independent of the bias assumption. The strong bias assumption makes it possible to apply sparsity/renormalization techniques, the main tools of our investigation, used earlier in the opposite context of proving fault-tolerance.

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Density-functional theory (DFT) is used to examine the basal and prism surfaces of ice Ih. Similar surface energies are obtained for the two surfaces; however, in each case a strong dependence of the surface energy on surface proton order is identified. This dependence, which can be as much as 50% of the absolute surface energy, is significantly larger than the bulk dependence (< 1%) on proton order, suggesting that the thermodynamic ground state of the ice surface will remain proton ordered well above the bulk order-disorder temperature of about 72 K. On the basal surface this suggestion is supported by Monte Carlo simulations with an empirical potential and solution of a 2D Ising model with nearest neighbor interactions taken from DFT. Order parameters that define the surface energy of each surface in terms of nearest neighbor interactions between dangling OH bonds (those which point out of the surface into vacuum) have been identified and are discussed. Overall, these results suggest that proton order-disorder effects have a profound impact on the stability of ice surfaces and will most likely have an effect on ice surface reactivity as well as ice crystal growth and morphology. S Supplementary data are available from stacks.iop.org/JPhysCM/22/074209/mmedia

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Copper zinc tin sulfide (CZTS) is a promising Earthabundant thin-film solar cell material; it has an appropriate band gap of ~1.45 eV and a high absorption coefficient. The most efficient CZTS cells tend to be slightly Zn-rich and Cu-poor. However, growing Zn-rich CZTS films can sometimes result in phase decomposition of CZTS into ZnS and Cu2SnS3, which is generally deleterious to solar cell performance. Cubic ZnS is difficult to detect by XRD, due to a similar diffraction pattern. We hypothesize that synchrotron-based extended X-ray absorption fine structure (EXAFS), which is sensitive to local chemical environment, may be able to determine the quantity of ZnS phase in CZTS films by detecting differences in the second-nearest neighbor shell of the Zn atoms. Films of varying stoichiometries, from Zn-rich to Cu-rich (Zn-poor) were examined using the EXAFS technique. Differences in the spectra as a function of Cu/Zn ratio are detected. Linear combination analysis suggests increasing ZnS signal as the CZTS films become more Zn-rich. We demonstrate that the sensitive technique of EXAFS could be used to quantify the amount of ZnS present and provide a guide to crystal growth of highly phase pure films.

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The main purpose of the study is to extent concept of the class of spaces called ‘generalized metric spaces’ to fuzzy context and investigates its properties. Any class of spaces defined by a property possessed by all metric spaces could technically be called as a class of ‘generalized metric spaces’. But the term is meant for classes, which are ‘close’ to metrizable spaces in some under certain kinds of mappings. The theory of generalized metric spaces is closely related to ‘metrization theory’. The class of spaces likes Morita’s M- spaces, Borges’s w-spaces, Arhangelskii’s p-spaces, Okuyama’s  spaces have major roles in the theory of generalized metric spaces. The thesis introduces fuzzy metrizable spaces, fuzzy submetrizable spaces and proves some characterizations of fuzzy submetrizable spaces, and also the fuzzy generalized metric spaces like fuzzy w-spaces, fuzzy Moore spaces, fuzzy M-spaces, fuzzy k-spaces, fuzzy -spaces study of their properties, prove some equivalent conditions for fuzzy p-spaces. The concept of a network is one of the most useful tools in the theory of generalized metric spaces. The -spaces is a class of generalized metric spaces having a network.

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We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga et al. [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga et al. in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga et al. in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.