CaCu 3 Ti 4 O 12 (CCTO) has a large dielectric permittivity that is independent of the 1 arXiv:1810.08949v3 [cond-mat.mtrl-sci] 17 Nov 2018 probing frequency near the room temperature, which complicated due to the existence of several dynamic processes. Here, we consider the combined effects of localized charge carriers (polarons) and thermally activated charge carriers using a recently proposed statistical model to fit and understand the permittivity of CCTO measured at different frequencies over the whole temperature range accessible by our experiments. We found that the small permittivity at the lowest temperature is related to polaron frozen, while at higher temperatures the rapid increase is associated with the thermal excitation of polarons inducing the Maxwell-Wagner effect, and the final increase of the permittivity is attributed to the thermally activated conductivity. Such analysis enables us to separate the contributions from localized polarons and conductive charge carriers and quantify their activation energies.
While doping is widely used for tuning physical properties of perovskites in experiments, it remains a challenge to exactly know how doping achieves the desired effects. Here, we propose an empirical and computationally tractable model to understand the effects of doping with Fe-doped BaTiO 3 as an example. This model assumes that the lattice sites occupied by Fe ion and its nearest six neighbors lose their ability to polarize, giving rise to a small cluster of defective dipoles. Employing this model in Monte-Carlo simulations, many important features like reduced polarization and the convergence of phase transition temperatures, which have been observed experimentally in acceptor doped systems, are successfully obtained. Based on microscopic information of dipole configurations, we provide insights into the driving forces behind doping effects and propose that active dipoles, which exist in proximity to the defective dipoles, can account for experimentally observed phenomena. Close attention to these dipoles are necessary to understand and predict doping effects.
Abstract. Aiming at the long-running time and the defogging image darkening problem in the dark channel prior algorithm, a fast deaeration algorithm based on the guided filter and improved two-dimensional gamma function for dark channel prior image is proposed. The algorithm uses the guided filter instead of the soft matting to obtain the image transmittance. The summation operation in the window replaces the quadrature operation in the window to reduce the complexity of the algorithm, and the image is processed by the two-dimensional gamma function. The brightness is adjusted to increase the brightness of the dark areas of the image, improve the contrast of the image, and enhance the image's performance in detail. The experimental results show that compared with the dark channel prior defogging algorithm and other image dehazing algorithms, the image fast dehazing algorithm based on dark channel prior improvement has high effective detail intensity, image information entropy and average gradient. The running time of the dark channel prior defogging algorithm is reduced, which effectively solves the long running time and the defogging image darkness problem of the dark channel prior defogging algorithm and has good robustness, and improves the quality and display effects of defogging image.
Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.
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