Fast local image inpainting based on the Allen–Cahn model

In this paper, we propose a fast local image inpainting algorithm based on the Allen–Cahn model. The proposed algorithm is applied only on the inpainting domain and has two features. The first feature is that the pixel values in the inpainting domain are obtained by curvature-driven diffusions and utilizing the image information from the outside of the inpainting region. The second feature is that the pixel values outside of the inpainting region are the same as those in the original input image since we do not compute the outside of the inpainting region. Thus the proposed method is computationally efficient. We split the governing equation into one linear equation and one nonlinear equation by using an operator splitting technique. The linear equation is discretized by using a fully implicit scheme and the nonlinear equation is solved analytically. We prove the unconditional stability of the proposed scheme. To demonstrate the robustness and accuracy of the proposed method, various numerical results on real and synthetic images are presented.     

Numerical schemes for the forward–backward heat equation

This paper is concerned with a class of forward–backward heat equations. We use Saulyev's scheme to formulate certain approximation schemes. Then a non-overlap domain decomposition method is presented for the numerical solution. The numerical experiments show that the given algorithm is feasible and effective.     

A local region-based Chan–Vese model for image segmentation

In this paper, a new region-based active contour model, namely local region-based Chan–Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a degraded CV model is proposed, whose segmentation result can be taken as the initial contour of the LRCV model. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the well-know local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour.     

Trusted dynamic level scheduling based on Bayes trust model

A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayes method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling (DLS) algorithm, the Trust-Dynamic Level Scheduling (Trust-DLS) algorithm is proposed. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of Grid tasks in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in Grid environment.     

A formal model for integrity protection based on DTE technique

In order to provide integrity protection for the secure operating system to satisfy the structured protection class' requirements, a DTE technique based integrity protection formalization model is proposed after the implications and structures of the integrity policy have been analyzed in detail. This model consists of some basic rules for configuring DTE and a state transition model, which are used to instruct how the domains and types are set, and how security invariants obtained from initial configuration are maintained in the process of system transition respectively. In this model, ten invariants are introduced, especially, some new invariants dealing with information flow are proposed, and their relations with corresponding invariants described in literatures are also discussed. The thirteen transition rules with well-formed atomicity are presented in a well-operational manner. The basic security theorems correspond to these invariants and transition rules are proved. The rationalities for proposing the invariants are further annotated via analyzing the differences between this model and ones described in literatures. At last but not least, future works are prospected, especially, it is pointed out that it is possible to use this model to analyze SE-Linux security.     

A stochastic model for forward–reverse logistics network design under risk

Attention with reverse logistics networks has increased during the last decade since their economic impact has been increasingly important and as environmental legislation has been becoming stricter. In this paper, A multi-period multi-echelon forward–reverse logistics network design under risk model is developed. The proposed network structure consists of three echelons in the forward direction, (suppliers, facilities and distribution centers) and two echelons, in the reverse direction (disassembly, and redistribution centers), first customer zones in which the demands are stochastic and second customer zones in which the demand is assumed to be deterministic, but it may also assumed to be stochastic. The problem is formulated in a stochastic mixed integer linear programming (SMILP) decision making form as a multi-stage stochastic program. The objective is to maximize the total expected profit.     

A full-Eulerian solid level set method for simulation of fluid–structure interactions

We present a full Eulerian method, termed solid level set (SLS) method, for modeling of a class of fluid–structure interactions (FSI) problems soft solid body can deform significantly but remains nearly incompressible. The SLS method is based on the unified momentum equation framework in which the solid–fluid interactions are modeled by introducing a solid body force term and a solid–fluid interfacial force term into the Navier–Stokes equation. The key idea of the SLS method is that the deformation of the solid body is no longer tracked using a Lagrangian mesh. Instead, the solid body is tracked by introducing a reference coordinate for describing the reference state of the solid body and by introducing three dynamic level set functions on the Cartesian coordinate and one static level set functions on the reference coordinate. The SLS method is easy to implement and addresses several challenges in the simulation of FSIs in which a fixed Cartesian mesh is used for fluid flow and a Lagrangian mesh is used for tracking the solid deformation. The effectiveness of the SLS method is demonstrated by studying two FSI problems. The method is suitable for studying a wide range of problems in microfluidics, e.g., manipulation of cells in confined space and ink-jet printing of biological samples.     

A human behavior model of multi-agent attention based on actor–observer switching for asynchronous motion tasks with limited field of view

This paper presents an overt visual attention approach for multi-agent systems and asynchronous motion tasks with limited field of view based on human behavior. In a first step, results from a study on human temporal attention switching during a formation task are presented. Three temporally switching human behaviors (observer, actor, idler) are identified and characterized towards distributions of attention foci. In a second step, based on these results, a behavior switching attention strategy is proposed. The strategy is evaluated in simulations of various formation task scenarios illustrating the effectiveness of the approach.     

Maximum principle for forward–backward SDEs with a general cost functional

In this study, we consider a stochastic optimal control problem whose state variables are described by the system of forward and backward stochastic differential equations with a general cost functional which relies on the global terminal condition. In the framework of Fréchet derivatives, we derive the corresponding maximum principle via constructing a series of adjoint equations which need to be solved step by step.     

A general framework of piecewise-polynomial Mumford–Shah model for image segmentation

A new general framework of piecewise-polynomial Mumford–Shah model is proposed. In terms of the fidelity term, we use piecewise polynomials to approximate the inner and outer regions of the contour of the objective image. For more accurate approximation of the image, the proposed model has no constraint on the regularization term for polynomials. Moreover, we apply the anisotropic control to drive the initial contour to the desirable position. The proposed model generalizes the well-known Chan–Vese model and improves Vese's model, which is almost the simplest framework to apply piecewise polynomials to approximate the original Mumford–Shah model. Instead of solving the Euler–Lagrange equation by evolution implementation, we utilize the split Bregman iteration, which is shown to be a fast algorithm. Experimental results demonstrate that the proposed model has more desirable performance in terms of segmentation accuracy, efficiency and robustness, compared with several other variational models in addressing some challenging segmentation scenarios.     

A 3D ellipsoidal volumetric foot–ground contact model for forward dynamics

Foot–ground contact models are an important part of forward dynamic biomechanic models, particularly those used to model gait, and have many challenges associated with them. Contact models can dramatically increase the complexity of the multibody system equations, especially if the contact surface is relatively large or conforming. Since foot–ground contact has a large potential contact area, creating a computationally efficient model is challenging. This is particularly problematic in predictive simulations, which may determine optimal performance by running a model simulation thousands of times. An ideal contact model must find a balance between accuracy for large, conforming surfaces, and computational efficiency.Volumetric contact modelling is explored as a computationally efficient model for foot–ground contact. Previous foot models have used volumetric contact before, but were limited to 2D motion and approximated the surfaces as spheres or 2D shapes. The model presented here improves on current work by using ellipsoid contact geometry and considering 3D motion and geometry. A gait experiment was used to parametrise and validate the model. The model ran over 100 times faster than real-time (in an inverse simulation at 128 fps) and matched experimental normal force and centre of pressure location with less than 7% root-mean-square error.In most gait studies, only the net reaction forces, centre of pressure, and body motions are recorded and used to identify parameters. In this study, contact pressure was also recorded and used as a part of the identification, which was found to increase parameter optimisation time from 10 to 164 s (due to the additional time needed to calculate the pressure distribution) but helped the results converge to a more realistic model. The model matched experimental pressures with 33–45% root-mean-square error, though some of this was due to measurement errors.The same parametrisation was done with friction included in the foot model. It was determined that the velocity-based friction model that was used was inappropriate for use in an inverse-dynamics simulation. Attempting to optimise the model to match experimental friction resulted in a poor match to the experimental friction forces, inaccurate values for the coefficient of friction, and a poorer match to the experimental normal force.     

Partition belief median filter based on Dempster–Shafer theory for image processing

A novel median-type filter controlled by evidence fusion is proposed for removing noise from images. The fusion of evidence based on the Dempster–Shafer evidence theory, providing a way to deal with the uncertainty in the evidence fusion, indicates to what extent a noise is considered. The filter proposed here is obtained as a weighted sum of the current pixel value and the output of the median filter, and the weight is set based on the belief value of the input signal sequence. The efficient step-like function is used to partition the belief space, and the least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Moreover, to improve the performance, the new filter is recursively implemented. Experimental results have demonstrated that the proposed filter can outperform many well-accepted median-based filters in preserving image details while effectively suppressing impulsive noises, and it also works satisfactorily in reducing Gaussian as well as the mixture of Gaussian and impulsive noise.     

Rough set–BPSO model for predicting vitamin D deficiency in apparently healthy Kuwaiti women based on hair mineral analysis

Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent researches show that, the vitamin D deficiency is associated with mineral status of patient. Therefore, it is important to assess the mineral status of patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 120 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by hair and serum samples, respectively. This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The model introduces a two-stage reduction technique based on BPSO and rough set theory as attribute reduction and rules extraction to predicting vitamin D deficiency. The results show that the proposed model (RS + BPSO), not only can effectively detect the deficiency in vitamin D, but can also provide valuable information with regard to the mineral imbalance as a cause of deficiency which should be addressed in any treatment management. To the best of our knowledge, this is the first work that predicts vitamin D deficiency based on hair minerals analysis.

     

Parallel distributed compensator design of tank level control based on fuzzy Takagi–Sugeno model

In this paper, a fuzzy controller is designed based on parallel distributed compensation (PDC) method and it is implemented in an experimental tank level control system. Firstly, a mathematical model of the system is obtained experimentally. An important feature of the plant is its nonlinearity. To control the level of water in the tank over the whole range, the nonlinear model of the system is linearized around three different operating points. Then, three PI controllers are designed for the operating points, using Skogestad's method. By using the PDC method, an overall fuzzy controller is designed by the fuzzy blending of the three PI-controllers. To evaluate the practical performance of the PDC-based fuzzy controller, the control system is implemented in the experimental system. The evaluation criteria considered are step response and disturbance rejection. The comparison results showed the superiority of the PDC-controller over the classical PI-controller.     

A conflict–congestion model for pedestrian–vehicle mixed evacuation based on discrete particle swarm optimization algorithm

A simulation model based on temporal–spatial conflict and congestion for pedestrian–vehicle mixed evacuation has been investigated. Assuming certain spatial behaviors of individuals during emergency evacuation, a discrete particle swarm optimization with neighborhood learning factor algorithm has been proposed to solve this problem. The proposed algorithm introduces a neighborhood learning factor to simulate the sub-group phenomenon among evacuees and to accelerate the evacuation process. The approach proposed here is compared with methods from the literatures, and simulation results indicate that the proposed algorithm achieves better evacuation efficiency while maintaining lower pedestrian–vehicle conflict levels.     

A parameter-free dynamic diffusion method for advection–diffusion–reaction problems

In this paper, we present a two-scale finite element formulation, named Dynamic Diffusion (DD), for advection–diffusion–reaction problems. By decomposing the velocity field in coarse and subgrid scales, the latter is used to determine the smallest amount of artificial diffusion to minimize the coarse-scale kinetic energy. This is done locally and dynamically, by imposing some constraints on the resolved scale solution, yielding a parameter-free consistent method. The subgrid scale space is defined by using bubble functions, whose degrees of freedom are locally eliminated in favor of the degrees of freedom that live on the resolved scales. Convergence tests on a two-dimensional example are reported, yielding optimal rates. In addition, numerical experiments show that DD method is robust for a wide scope of application problems.     

An image segmentation method based on Mumford–Shah model with mask factor and neighborhood factor

Pattern Analysis and Applications - A novel image segmentation model is proposed to improve the stability of existing segmentation methods. In the proposed model, we introduce two factors into the...     

Reaction–diffusion network for geometric multiscale high speed image processing

In the framework of heavy mid-level processing for high speed imaging, a nonlinear bi-dimensional network is proposed, allowing the implementation of active curve algorithms. Usually this efficient type of algorithm is prohibitive for real-time image processing due to its calculus charge and the inadequate structure for the use of serial or parallel architectures. Another kind of implementation philosophy is proposed here, by considering the active curve generated by a propagation phenomenon inspired from biological modeling. A programmable nonlinear reaction–diffusion system is proposed under front control and technological constraints. Geometric multiscale processing is presented and this opens a discussion about electronic implementation.     

Necessary condition for near optimal control of linear forward–backward stochastic differential equations

This paper investigates the near optimal control for a kind of linear stochastic control systems governed by the forward–backward stochastic differential equations, where both the drift and diffusion terms are allowed to depend on controls and the control domain is not assumed to be convex. In the previous work (Theorem 3.1) of the second and third authors, some problem of near optimal control with the control dependent diffusion is addressed and our current paper can be viewed as some direct response to it. The necessary condition of the near-optimality is established within the framework of optimality variational principle developed by Yong and obtained by the convergence technique to treat the optimal control of FBSDEs in unbounded control domains by Wu. Some new estimates are given here to handle the near optimality. In addition, an illustrating example is discussed as well.