The Experts below are selected from a list of 712605 Experts worldwide ranked by ideXlab platform
Etienne E. Kerre - One of the best experts on this subject based on the ideXlab platform.
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a new fuzzy color correlated impulse noise Reduction Method
IEEE Transactions on Image Processing, 2007Co-Authors: Stefan Schulte, Samuel Morillas, Valentin Gregori, Etienne E. KerreAbstract:A new impulse noise Reduction Method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based Methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise Reduction performance. In this paper, we discuss an alternative technique which gives a good noise Reduction performance while much less artefacts are introduced. The main difference between the proposed Method and other classical noise Reduction Methods is that the color information is taken into account to develop (1) a better impulse noise detection Method and (2) a noise Reduction Method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed Method provides a significant improvement on other existing filters.
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fuzzy random impulse noise Reduction Method
Fuzzy Sets and Systems, 2007Co-Authors: Stefan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract:A new two-step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with impulse noise is presented in this paper. The filtering Method (entitled as Fuzzy Random Impulse Noise Reduction Method (FRINR)) consists of a fuzzy detection mechanism and a fuzzy filtering Method to remove (random-valued) impulse noise from corrupted images. Based on the criteria of peak-signal-to-noise-ratio (PSNR) and subjective evaluations we have found experimentally, that the proposed Method provides a significant improvement on other state-of-the-art Methods.
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A fuzzy impulse noise detection and Reduction Method
Image Processing IEEE Transactions on, 2006Co-Authors: Stephan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract:Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and Reduction Method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a Reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection Method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering Method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise.
Hong Zheng - One of the best experts on this subject based on the ideXlab platform.
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slope stability analysis using convergent strength Reduction Method
Engineering Analysis With Boundary Elements, 2019Co-Authors: Zhibao Nie, Zhihong Zhang, Hong ZhengAbstract:Abstract The strength Reduction Method (SRM) is becoming more and more popular in the stability analysis of slopes. Nevertheless, the criterion for slope failure associated with SRM is controversial, and divergence exists while approaching to the limit equilibrium state of slopes. In this study, the slip body is discretized with constant boundary elements. Since at least one element on the slip surface, referred to as the critical element, should keep still till the slope reaches the limit equilibrium state, the critical element is forced to be fixed throughout while other elements on the slip surface are specified to be contact elements during the strength Reduction. In this way, convergence is always assured in the open-close iteration of contact between the slip body and the slip bed, no matter how much the strength of the slip surface is reduced. The limit equilibrium state is defined to be the moment at which the strength redundancy of the critical element vanishes. The Method of bisection is applied to find out the factor of safety (FOS). The proposed Method is applied to those benchmark examples and the comparison is made with the limit equilibrium Methods (LEM).
Stefan Schulte - One of the best experts on this subject based on the ideXlab platform.
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a new fuzzy color correlated impulse noise Reduction Method
IEEE Transactions on Image Processing, 2007Co-Authors: Stefan Schulte, Samuel Morillas, Valentin Gregori, Etienne E. KerreAbstract:A new impulse noise Reduction Method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based Methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise Reduction performance. In this paper, we discuss an alternative technique which gives a good noise Reduction performance while much less artefacts are introduced. The main difference between the proposed Method and other classical noise Reduction Methods is that the color information is taken into account to develop (1) a better impulse noise detection Method and (2) a noise Reduction Method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed Method provides a significant improvement on other existing filters.
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fuzzy random impulse noise Reduction Method
Fuzzy Sets and Systems, 2007Co-Authors: Stefan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract:A new two-step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with impulse noise is presented in this paper. The filtering Method (entitled as Fuzzy Random Impulse Noise Reduction Method (FRINR)) consists of a fuzzy detection mechanism and a fuzzy filtering Method to remove (random-valued) impulse noise from corrupted images. Based on the criteria of peak-signal-to-noise-ratio (PSNR) and subjective evaluations we have found experimentally, that the proposed Method provides a significant improvement on other state-of-the-art Methods.
Lu Feng Yang - One of the best experts on this subject based on the ideXlab platform.
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elastic modulus Reduction Method for limit analysis considering initial constant and proportional loadings
Finite Elements in Analysis and Design, 2010Co-Authors: Lu Feng YangAbstract:The paper proposes an efficient linear elastic iterative finite element Method, namely, the elastic modulus Reduction Method (EMRM) to calculate lower-bound limit loads of frame structures under combined action of initial constant and proportional loads. The initial constant loads are scaled down by the limit load achieved in the last step of iterations and regarded as the pseudo-proportional loads (PPLs) on the assumption that the PPLs increase along with the proportional loads and their final values should be equal to the initial constant loads when structure gets to the plastic limit state. Meanwhile, the elastic modulus Reduction Method (EMRM) is developed by defining the element bearing ratio (EBR) on the basis of the generalized yield criterion and the strain energy equilibrium principle (SEEP). Numerical examples demonstrate the applicability and precision of the proposed Method for limit analysis of frame structures under combined action of initial constant and proportional loads.
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elastic modulus Reduction Method for limit analysis of thin plate and shell structures
Thin-walled Structures, 2010Co-Authors: Lu Feng YangAbstract:The paper proposes an efficient numerical Method, namely, the elastic modulus Reduction Method (EMRM) to calculate lower-bound limit loads of thin plate and shell structures. To determine the elastic modulus adjustment parameter q in a rational way, the precisions and convergent properties of four different strategies of elastic modulus adjustment (SEMAs) combined with the EMRM are investigated. Furthermore, to take into consideration the plastic resistances as well as the internal forces in the processes of elastic modulus adjustment, the element bearing ratio (EBR) of thin plate/shell element is defined as the governing parameter based on the generalised yield criterions. The flexibility and accuracy of the proposed EMRM combined with both the strain energy equilibrium principle (SEEP) and the fixed strain Method (FSM) are demonstrated in the numerical example.
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elastic modulus Reduction Method for limit load evaluation of frame structures
Acta Mechanica Solida Sinica, 2009Co-Authors: Lu Feng Yang, Yongping QiaoAbstract:A new strategy for elastic modulus adjustment is proposed based on the element bearing ratio (EBR), and the elastic modulus Reduction Method (EMRM) is proposed for limit load evaluation of frame structures. The EBR is defined employing the generalized yield criterion, and the reference EBR is determined by introducing the extrema and the degree of uniformity of EBR in the structure. The elastic modulus in the element with an EBR greater than the reference one is reduced based on the linear elastic finite element analysis and the equilibrium of strain energy. The lower-bound of limit-loads of frame structures are analyzed and the numerical example demonstrates the flexibility, accuracy and efficiency of the proposed Method.
Shodhan Rao - One of the best experts on this subject based on the ideXlab platform.
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a model Reduction Method for biochemical reaction networks
BMC Systems Biology, 2014Co-Authors: Shodhan Rao, Arjan Van Der Schaft, Karen Van Eunen, Barbara M Bakker, Bayu JayawardhanaAbstract:Background: In this paper we propose a model Reduction Method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The Method proceeds by a stepwise Reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron Reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. Results: We apply our Method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. Conclusions: The Method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise Reduction approach, are not those with the shortest convergence times. It shows that our Reduction approach performs differently from other approaches that are based on time-scale separation. The Method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment.
