Due to the 'soft-field' effect and the challenges posed by ill-posed and ill-conditioned inverse problems, it is difficult to obtain high quality images from an electrical capacitance tomography (ECT) system. ...
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Due to the 'soft-field' effect and the challenges posed by ill-posed and ill-conditioned inverse problems, it is difficult to obtain high quality images from an electrical capacitance tomography (ECT) system. To achieve both high-quality images and fast imaging speed with limited measurement data, an image reconstruction algorithm, which was initially proposed for compressive sensing, is adapted for ECT image reconstruction to optimize the ill-posed nature of its inverse problem. The proposed algorithm leverages deep learning networks inspired by the iterative shrinkage-thresholding algorithm (ISTA), thereby creating a model that is both mathematically interpretable and endowed with trainable parameters. Building upon this foundation, the conventional Landweber iteration is integrated with the ISTA-Net to refine the optimization process for ECT image reconstruction. In order to propose an effective model adapting to the actual multiphase flow characteristics and complex flow pattern changes, the training and test process is driven by a comprehensive dataset generated from dynamic simulations, rather than artificial samples of multiphase distributions. This numerical methodology simulates the dynamic measurement process of a virtual ECT sensor by coupling the gas-liquid two-phase flow field and the ECT electrostatic field. The results of the testing phase indicate that the proposed algorithm outperforms traditional ECT image reconstruction methods. Compared with the linear back projection algorithm, the average image error and gas fraction error have been reduced by 20.44% and 16.74%, respectively, while maintaining a computational speed comparable to that of the Landweber iteration. The accuracy of the new algorithm in reconstructing the two-phase interface and estimating the gas fraction has been validated by static experimental tests, showing its potential for practical application in online gas-liquid two-phase flow measurement scenarios.
A new iterative algorithm for discrete HJB equations is proposed. Monotone convergence has been proved for the *** example shows the efficiency of the algorithm.
A new iterative algorithm for discrete HJB equations is proposed. Monotone convergence has been proved for the *** example shows the efficiency of the algorithm.
The purpose of this paper is to introduce new iterative algorithms for approximating a solution to a class of monotone operator equations. More precisely, we study the split common solution problem with multiple outpu...
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The purpose of this paper is to introduce new iterative algorithms for approximating a solution to a class of monotone operator equations. More precisely, we study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces. In order to solve this problem, we propose three new algorithms and establish strong convergence theorems for them.
An n x n real matrix P is said to be a generalized reflection matrix if P-T = P and P-2 = I (where P-T is the transpose of P). A matrix A is an element of R-nxn is said to be a reflexive (anti-reflexive) matrix with r...
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An n x n real matrix P is said to be a generalized reflection matrix if P-T = P and P-2 = I (where P-T is the transpose of P). A matrix A is an element of R-nxn is said to be a reflexive (anti-reflexive) matrix with respect to the generalized reflection matrix P if A = PAP (A = -PAP). The reflexive and anti-reflexive matrices have wide applications in many fields. In this article, two iterative algorithms are proposed to solve the coupled matrix equations {A(1)XB(1) + (C1XD1)-D-T = M-1, A(2)XB(2) + (C2XD2)-D-T = M-2, over reflexive and anti-reflexive matrices, respectively. We prove that the first (second) algorithm converges to the reflexive (anti-reflexive) solution of the coupled matrix equations for any initial reflexive (anti-reflexive) matrix. Finally two numerical examples are used to illustrate the efficiency of the proposed algorithms.
In this paper, we investigate the split equality common fixed-point problem of firmly quasi-nonexpansive operators in Hilbert spaces. We introduce new iterative algorithms with a way of selecting the step-sizes such t...
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In this paper, we investigate the split equality common fixed-point problem of firmly quasi-nonexpansive operators in Hilbert spaces. We introduce new iterative algorithms with a way of selecting the step-sizes such that its implementation does not need any prior information about the operator norms. The new methods are extended from the method for solving the split common fixed-point problem. The range of the new step-sizes even can be enlarged two times. Under suitable conditions, we establish a weak convergence theorem of the proposed algorithm and a strong convergence theorem of its variant by the viscosity approximation method. Numerical results are reported to show the effectiveness of the proposed algorithm.
With the large-scale integration of renewable energy,the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy ***,in a market-oriented operation ...
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With the large-scale integration of renewable energy,the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy ***,in a market-oriented operation mode,the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power ***,a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed *** model is built on double-layer optimization framework,where the upper-layer model is used for maintenance and generation planning,and the lowerlayer model is primarily used to address the operational security risk arising from the random prediction error and N-1 transmission ***,a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution *** cost is determined using long-term security-constrained unit commitment,and the operational risk cost is obtained using a double-layer N-1 risk assessment *** electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously,thereby improving model convergence *** IEEE 118-bus system is used as a test case for illustration,and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.
In this paper, we introduce parallel and cyclic iterative algorithms for solving the multiple-set split equality common fixed-point problem of demicontractive operators. We propose a way of selecting the stepsizes suc...
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In this paper, we introduce parallel and cyclic iterative algorithms for solving the multiple-set split equality common fixed-point problem of demicontractive operators. We propose a way of selecting the stepsizes such that the implementation of our algorithms does not need any prior information about operator norms. It thus avoids the difficult task of estimating the operator norms. We also combine the process of cyclic and parallel together and propose two mixed iterative algorithms without prior knowledge of operator norms. The weak convergence theorems of the proposed algorithms are established under some suitable control conditions in a real Hilbert space. Some numerical experiments are given for the proposed iterative algorithms.
Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronou...
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Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.
Graph states are special multipartite entangled states that have been proven useful in a variety of quantum information tasks. We address the issue of characterizing and quantifying the genuine multipartite entangleme...
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Graph states are special multipartite entangled states that have been proven useful in a variety of quantum information tasks. We address the issue of characterizing and quantifying the genuine multipartite entanglement of graph states up to eight qubits. The entanglement measures used are the geometric measure, the relative entropy of entanglement, and the logarithmic robustness, have been proved to be equal for the genuine entanglement of a graph state. We provide upper and lower bounds as well as an iterative algorithm to determine the genuine multipartite entanglement.
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