ON THE 82-TH SMARANDACHE’S PROBLEM

The main purpose of this paper is using the elementary method to study the asymptotic properties of the integer part of the k-th root positive integer, and give two interesting asymptotic formulae

バイオマスガス化におけるタールの触媒的除去及び水素の生産

筑波大学 (University of Tsukuba)201

Computational Flame Diagnostics with Bifurcation Analysis and Chemical Explosive Mode Analysis

Limit flame phenomena, such as flame ignition, extinction and onset of instabilities, are important for fire safety, engine efficiency and pollutant emissions. Systematic identification of such limit phenomena and understanding of the underlying physicochemical processes are critical to develop a predictive capability for practical combustion systems. In the present study, systematic approaches for computational flame diagnostics are developed based on eigen-analysis of the governing equations of combustion systems to systematically extract information of the controlling processes for the limit phenomena. Specifically, a bifurcation analysis is developed based on the full Jacobian of the governing equations including both chemical and non-chemical source terms. The bifurcation analysis identifies bifurcation points of steady state combustion systems, across which the stability of the system changes, as demonstrated with perfectly stirred reactors (PSRs) as representative steady state combustion systems featuring the “S”-curve behaviors. It was shown that flame extinction may occur either at the upper turning point on the “S”-curve, which is widely accepted as the extinction state of strongly burning flames, or at a Hopf bifurcation point on the upper branch of the “S”-curve, particularly when the negative temperature coefficient (NTC) behaviors are involved. A bifurcation index is further defined to quantify the contribution of each reaction to the bifurcation points, such that the physicochemical processes controlling the limit phenomena can be identified. The bifurcation analysis is further exploited to obtain highly reduced mechanisms and to understand jet fuel combustion at high-temperature conditions. Chemical explosive mode analysis (CEMA) as another approach for computational flame diagnostics, defined based on the Jacobian of the chemical source term, is further investigated to extract salient flame features, e.g. local ignition, extinction and flame fronts, from a variety of combustion systems, including 0-D auto-ignition, PSRs, 1-D laminar premixed flames, and a turbulent flame simulated with direct numerical simulation (DNS) under the homogeneous charge compression ignition (HCCI) condition for n-heptane-air mixtures featuring NTC behaviors

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation

Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM

Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201