This research aims to model cardiac pulse wave reflections due to the presence of arterial irregularities such as bifurcations, stiff arteries, stenoses or aneurysms. When an arterial pressure wave encounters an irreg...
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This research aims to model cardiac pulse wave reflections due to the presence of arterial irregularities such as bifurcations, stiff arteries, stenoses or aneurysms. When an arterial pressure wave encounters an irregularity, a backward reflected wave travels upstream in the artery and a forward wave is transmitted downstream. The same process occurs at each subsequent irregularity, leading to the generation of multiple waves. An iterative algorithm is developed and applied to pathological scenarios to predict the pressure waveform of the reflected wave due to the presence of successive arterial irregularities. For an isolated stenosis, analysing the reflected pressure waveform gives information on its severity. The presence of a bifurcation after a stenosis tends do diminish the amplitude of the reflected wave, as bifurcations' reflection coefficients are relatively small compared to the ones of stenoses or aneurysms. In the case of two stenoses in series, local extrema are observed in the reflected pressure waveform which appears to be a characteristic of stenoses in series along an individual artery. Finally, we model a progressive change in stiffness in the vessel's wall and observe that the less the gradient stiffness is important, the weaker is the reflected wave.
Blind image deconvolution is a highly ill-posed problem. As a generalization of the well known Weiner filter, the existing iterative Weiner filter (IWF) method for blind image deconvolution is unstable and suffers fro...
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Blind image deconvolution is a highly ill-posed problem. As a generalization of the well known Weiner filter, the existing iterative Weiner filter (IWF) method for blind image deconvolution is unstable and suffers from serious ringing artifacts. To overcome these drawbacks, in this paper, we propose two novel regularized iterative Weiner filter methods. We assume that both the latent image and the convolution kernel can be estimated by applying two different filters on the observed image. To estimate the filters, we propose to minimize energy functionals combined by the mean square errors with some regularization terms. Both H-1 and total variation (TV) regularization are considered. By applying alternating minimization method and operator splitting technique, we derive iterative algorithms for each regularization method. The proposed methods are effective for blind deconvolution of Gaussian blurred images which is widely observed in real applications such as microscopic images. Numerical experimental results on both synthetic images and real microscopic images are presented. The comparisons show that the proposed regularized algorithms perform better than the closely related state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and visual quality. (C) 2018 Elsevier B.V. All rights reserved.
We present a local as well as a semilocal convergence analysis for some iterative algorithms in order to approximate a locally unique solution of a nonlinear equation in a Banach space setting. In the application part...
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An iterative algorithm of component mode synthesis for a rational reduced-order modeling is proposed. The present algorithm consists of three steps: initial mode selection with a priori criterion, model reduction usin...
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An iterative algorithm of component mode synthesis for a rational reduced-order modeling is proposed. The present algorithm consists of three steps: initial mode selection with a priori criterion, model reduction using the selected modes, and a posteriori error evaluation. The algorithm is based on the Craig-Bampton method, which is one of the most widely used techniques. Specifically, the present algorithm uses, in a judicious manner, both an a priori mode selection criterion employing the moment-matching strategy and an a posteriori error estimation technique in evaluating the relative eigenvalue errors. If desired, an optional step of boundary interface reduction offers an additional possibility of obtaining a more compact model. The proposed iterative algorithm is recommended to researchers as well as commercialized software users in developing linear reduced-order structural dynamics models. Numerical examples are presented for its practical usage and performance.
We present the first exact simulation method for multidimensional reflected Brownian motion (RBM). Exact simulation in this setting is challenging because of the presence of correlated local-time-like terms in the def...
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We present the first exact simulation method for multidimensional reflected Brownian motion (RBM). Exact simulation in this setting is challenging because of the presence of correlated local-time-like terms in the definition of RBM. We apply recently developed so-called epsilon-strong simulation techniques (also known as tolerance-enforced simulation) which allowus to provide a piecewise linear approximation toRBMwith epsilon (deterministic) error in uniform norm. A novel conditional acceptance-rejection step is then used to eliminate the error. In particular, we condition on a suitably designed information structure so that a feasible proposal distribution can be applied.
This paper develops an integrated production-delivery lot sizing model in which a manufacturer supplies a product a retailer. The manufacturer produces the product at a finite rate and the production rate is assumed t...
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This paper develops an integrated production-delivery lot sizing model in which a manufacturer supplies a product a retailer. The manufacturer produces the product at a finite rate and the production rate is assumed to be smaller than market demand rate. Meanwhile, we assume that the lead time demand is stochastic and that shortages are permitted. The lead time and the reorder point are in this model as decision variables. The objective is to determine the optimal ordering quantity, reorder point, lead time and the delivery number during each production cycle which minimize the expected total cost per unit time of the manufacturer and the retailer by giving an effective iterative algorithm. A numerical example is given as an illustration of this algorithm for the integrated system.
Boundary effect refers to the issue of ambiguous allocation of crashes occurred on or near the boundaries of neighboring zones in zonal safety analysis. It results in bias estimates for associate measure between crash...
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Boundary effect refers to the issue of ambiguous allocation of crashes occurred on or near the boundaries of neighboring zones in zonal safety analysis. It results in bias estimates for associate measure between crash occurrence and possible zonal factors. It is a fundamental problem to compensate for the boundary effect and enhance the model predictive performance. Compared to conventional approaches, it might be more reasonable to assign the boundary crashes according to the crash predisposing agents, since the crash occurrence is generally correlated to multiple sources of risk factors. In this study, we proposed a novel iterative aggregation approach to assign the boundary crashes, according to the ratio of model-based expected crash number in adjacent zones. To verify the proposed method, a case study using a dataset of 738 Traffic Analysis Zones (TAZs) from the county of Hillsborough in Florida was conducted. Using Bayesian spatial models (BSMs), the proposed approach demonstrated the capability in reasonably compensating for the boundary effect with better model estimation and predictive performance, as compared to three conventional approaches (i.e., half and half ratio method, one to one ratio method, and exposure ratio method). Results revealed that several factors including the number of intersections, road segment length with 35 mph speed limit, road segment length with 65 mph speed limit and median household income, were sensitive to the boundary effect.
This paper studies a set-based unknown input observer based on zonotopes for discrete-time descriptor systems affected by uncertainties with application to state estimation and robust fault detection. In this paper, t...
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This paper develops a closed-form frequency-domain transfer function to predict the initial image's contributions in an iterative Landweber reconstruction for a given number of iterations. The transfer function sh...
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This paper develops a closed-form frequency-domain transfer function to predict the initial image's contributions in an iterative Landweber reconstruction for a given number of iterations. The transfer function shows that the initial image's high frequency components play an important role when the iterative algorithm is stopped before convergence. The contributions from the initial image diminish as the iteration number increases if the imaging matrix has full rank.
This paper studies an H-infinity model reduction problem for interval frequency negative imaginary (IFNI) systems. For a given IFNI system, our goal is to find a reduced-order IFNI system satisfying a pre-specified H-...
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This paper studies an H-infinity model reduction problem for interval frequency negative imaginary (IFNI) systems. For a given IFNI system, our goal is to find a reduced-order IFNI system satisfying a pre-specified H-infinity approximation error bound over the finite-frequency interval. Necessary and sufficient conditions in terms of matrix inequalities are derived for the existence and construction of an H-infinity reduced-order IFNI system. An improved iterative algorithm is provided to solve the matrix inequalities and to minimize the H-infinity approximation error. The proposed method is further clarified via the application to the electrical circuits, such as high-order Sallen-Key low-pass filter, piezoelectric tube scanner, and RLC circuit. The simulation results on these electrical circuits are compared with the finite-frequency interval Gramians-based model reduction method both in the frequency domain and time domain.
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