Clogging of fines in reservoir significantly declines the extraction efficiency. Microscale mechanisms and development process of fines clogging and the pressure drop evolution at tight reservoir remain unclear, parti...
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Clogging of fines in reservoir significantly declines the extraction efficiency. Microscale mechanisms and development process of fines clogging and the pressure drop evolution at tight reservoir remain unclear, partially due to the limitation of meshing-particle size-pore throat size. A kernel function-based CFD-DEM method was implemented to address this limitation, with fines type, flow velocity, flow field pattern, and adhesive force as parameters of interest. For non-buoyant fines (i.e., silt), bridging was the main clogging mechanisms, and the decrement of cohesive force had limited effects in declining bridging probability in convergent radial flow condition due to the gradually increment of flow velocity along the radial direction. For buoyant fines (i.e., clay), clogging formed due to the gradual deposition or bridging after surface deposition formed, categorizing pressure drop into two stages with that increasing slightly (deposition accumulation) and dramatically (clogging formation). A two-stage pressure drop temporal evolution model considering the processes of deposition accumulation and stochastic clogging were constructed. The critical retention volume for forming clogging in convergent radial flow condition (0.68%) was smaller than that in unidirectional flow condition (0.88%). Moreover, convergent radial flow enhanced the circumferential motion of fines, and pre-clogged clogging promoted clogging phenomenon was found in this scenario.
In this manuscript, we examine linear optimization problems formulated in the standard format. A novel kernel function is employed to devise a new interior-point algorithm for these problems. The proposed method reduc...
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In this manuscript, we examine linear optimization problems formulated in the standard format. A novel kernel function is employed to devise a new interior-point algorithm for these problems. The proposed method reduces the number of iterations required for the Netlib test problems. The outcomes are subsequently derived using the self-dual embedding technique. The application of the kernel function facilitates the determination of search directions and the quantification of the distance between the current iteration and the mu-center of the algorithm. Incorporating specific lemmas tailored to this methodology is essential for establishing the optimal limit on iteration complexity. The methodology delineated in the work of K. Roos provides the framework for our investigation. Finally, numerical instances were examined to elucidate the theoretical findings and demonstrate the efficacy of the proposed innovative approach.
Although kernel functions play a pivotal role in meshfree approximation, the selection of kernel functions is often experience-based and lacks a theoretical basis. As an attempt to resolve this issue, a rational match...
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Although kernel functions play a pivotal role in meshfree approximation, the selection of kernel functions is often experience-based and lacks a theoretical basis. As an attempt to resolve this issue, a rational matching between kernel functions and nodal supports is proposed in this work for Galerkin meshfree methods, where the quadratic through quintic B-spline kernel functions are particularly investigated. The foundation of this rational matching is the design of an efficient quantification of relative interpolation errors. The proposed relative interpolation error measures are not problem-dependent and can be easily and efficiently evaluated. More importantly, these relative interpolation error measures effectively reflect the variation of the real interpolation errors for meshfree approximation, which essentially control the solution accuracy of the Galerkin meshfree formulation with consistent numerical integration. Consequently, an optimal selection of kernel functions that match the nodal supports of meshfree approximation can be readily realized via minimizing the relative interpolation errors of meshfree approximation. The efficacy of the proposed rational matching between kernel functions and nodal supports is well demonstrated by meshfree numerical solutions.
The current electromagnetism environment is fast changing and levity, the methods for evaluation Suppont vector machine(SVM) kernel functions which are used in radar signal recognition can not suit it. So kernel space...
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The current electromagnetism environment is fast changing and levity, the methods for evaluation Suppont vector machine(SVM) kernel functions which are used in radar signal recognition can not suit it. So kernel space separate, stability and parameter numbers were proposed in this paper to review the performance of kernel function, and a novel method for estimating kernel function was designed. By simulation, this novel method can estimate the performance of kernel function roundly, and can choose the best kernel function in different application demand for recognition.
This paper, constituting an extension to the conference paper [1], corrects the proof of the Theorem 2 from the Gower's paper [2, page 5]. The correction is needed in order to establish the existence of the kernel...
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This paper, constituting an extension to the conference paper [1], corrects the proof of the Theorem 2 from the Gower's paper [2, page 5]. The correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for k-means clustering algorithm, on the grounds of distance matrix. The correction encompasses the missing if-part proof and dropping unnecessary conditions.
Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of i...
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ISBN:
(纸本)9780769551593
Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing kernel Hilbert space) space and using the methods in linear space to solve nonlinear problems. Meanwhile, feature transformation is achieved by defining the inner product of samples in the feature space after its characteristics are changed. Experimental results show that the support vector machine which is made up of new kernel function can greatly improve the recognition rate of action under the conditions of Mercer, providing theoretical basis for modeling of lower limb rehabilitation training system of stroke patients.
In this paper, the uplink energy efficiency and uplink outage probability for two-tier cellular access networks (TTCANs) are investigated. To model of the uplink energy efficiency and uplink outage probability in TTCA...
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In this paper, the uplink energy efficiency and uplink outage probability for two-tier cellular access networks (TTCANs) are investigated. To model of the uplink energy efficiency and uplink outage probability in TTCANs, a closed-form expression of signal-to-interference ratio (SIR) is derived by considering the on/off states of femtocell access points (APs). Moreover, a second order kernel function is firstly used to solve the analytical interference model with femtocell APs turning on in TTCANs. Simulation results show that femtocell user's intensity has great impact on the uplink energy efficiency and uplink outage probability in a TTCAN. These results provide some guidelines for developing new energy saving schemes in practical TTCANs deployment.
In this paper, an interior point method (IPM) based on a new kernel function for solving linearly constrained convex optimization problems is presented. So, firstly a survey on several trigonometric kernel functions d...
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In this paper, an interior point method (IPM) based on a new kernel function for solving linearly constrained convex optimization problems is presented. So, firstly a survey on several trigonometric kernel functions defined in literature is done and some properties of them are studied. Then some common characteristics of these functions which help us to define a new trigonometric kernel function are obtained. We generalize the growth term of the kernel function by applying a positive parameterpand rewritten the trigonometric kernel functions defined in the literature. By the help of some simple analysis tools, we show that the IPM based on the new kernel function obtains O(root n log n log n/epsilon) iteration complexity bound for large-update methods. Finally, we illustrate some numerical results of performing IPMs based on the kernel functions for solving non-negative matrix factorization problems.
In this paper, we propose a full-Newton step interior-point method for the weighted linear complementarity problem (wLCP) model of the Fisher market equilibrium problem. The weighted complementarity problem (wCP) is a...
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In this paper, we propose a full-Newton step interior-point method for the weighted linear complementarity problem (wLCP) model of the Fisher market equilibrium problem. The weighted complementarity problem (wCP) is a generalization of the complementarity problem (CP) with the nonnegative weight vector, where the zero on the right-hand side is replaced by the nonnegative weight vector. The importance of a wCP lies in the fact that many equilibrium problems in science, engineering, and economics can be reformulated as wCPs, which lead to the development of highly efficient algorithms. We extend a full-Newton step interior-point method (IPM) to the wLCP model of the Fisher problem. New search directions are obtained by using a kernel function in the scaled Newton system. The algorithm takes only full-Newton steps, thus, avoiding the calculation of the step size which is computationally advantageous. Under the suitable assumptions, the algorithm is shown to have global convergence and polynomial complexity. Some numerical results indicate the efficiency of the algorithm. To the best of our knowledge, this is the first full-Newton step IPM for Fisher problems, which uses the kernel function to obtain new search directions.
In this paper,we present a primal-dual interior point algorithm for semidefinite optimization problems based on a new class of kernel *** functions constitute a combination of the classic kernel function and a barrier...
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In this paper,we present a primal-dual interior point algorithm for semidefinite optimization problems based on a new class of kernel *** functions constitute a combination of the classic kernel function and a barrier *** derive the complexity bounds for large and small-update methods *** show that the best result of iteration bounds for large and small-update methods can be achieved,namely O(q√n(log√n)^q+1/q logn/ε)for large-update methods and O(q^3/2(log√q)^q+1/q√nlogn/ε)for small-update *** test the efficiency and the validity of our algorithm by running some computational tests,then we compare our numerical results with results obtained by algorithms based on different kernel functions.
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