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.
The problem we will consider in this paper is binary image restoration. It is, in essence, difficult to solve because of the combinatorial nature of the problem. To overcome this difficulty, we propose a new minimizat...
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The problem we will consider in this paper is binary image restoration. It is, in essence, difficult to solve because of the combinatorial nature of the problem. To overcome this difficulty, we propose a new minimization model by making use of a new variable to enforce the image to be binary. Based on the proposed minimization model, we present a fast alternating minimization algorithm for binary image restoration. We prove the convergence of the proposed alternating minimization algorithm. Experimental results show that the proposed method is feasible and effective for binary image restoration.
With the advent of advanced energy management systems in distribution systems,there is a growing interest in rapid and reliable code for distribution system state estimation(DSSE)in large-scale *** DSSE methods employ...
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With the advent of advanced energy management systems in distribution systems,there is a growing interest in rapid and reliable code for distribution system state estimation(DSSE)in large-scale *** DSSE methods employed in the industry are based on load scaling as they are well suited to the abundance of *** to the paucity of real-time measurements in DSSE,phasor measurement units(PMUs)have been proposed as a potential solution to increase the estimation ***,load scaling methodologies are not extendable for exploiting *** paper proposes a high-performance DSSE method that can handle the PMUs together with all common measurement types in industrial *** using Wirtinger calculus,the method operates entirely in complex variables and employs the latest version of advanced vector extensions(AVX-2)to reap the maximum potential of computer processing *** paper highlights the derivation of complex DSSE in matrix form,from which one can infer the implications on code reliability and *** results are reported on large-scale multi-phase distribution systems,and they are contrasted with a publicly available code for DSSE in real *** simulation results show that loop unrolling in AVX-2 contributes about a two-fold increase in the solving speed.
In order to obtain faster and more accuracy transient tracking performances for non-positive plants, a fast proportional integral difference (PID) type parameter optimal iterative learning control algorithm is propose...
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In order to obtain faster and more accuracy transient tracking performances for non-positive plants, a fast proportional integral difference (PID) type parameter optimal iterative learning control algorithm is proposed. In the algorithm, the PID type operators are introduced to enhance convergence speed and a suitable set of basis functions is added to avoid the algorithm plunge into local optimal when the plant is not positive. Theoretic proof shows that the algorithm monotone convergence to zero no matter the system plant is positive or not. Finally, simulations show that the algorithm also has a faster convergence speed compare with other similar algorithms.
In order to obtain faster and more accuracy transient tracking performances in iterative domain, a high-order proportional integral difference type parameter optimal iterative learning control algorithm based on norm ...
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In order to obtain faster and more accuracy transient tracking performances in iterative domain, a high-order proportional integral difference type parameter optimal iterative learning control algorithm based on norm performance index is proposed. In the algorithm, the proportional integral difference type operator is introduced to expend the dimension of the algorithm and to increase the free-degree of the optimal parameter. Theoretic proof shows that the convergence of the algorithm is monotonic no matter the plant is positive or not, and the tracking error will converge monotonically to zero when the plant is positive. Finally, simulations show that the tracking error of the proposed algorithm converges monotonically and faster than other similar algorithms.
Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these...
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Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.
Femtocell networks are expected to offer significant performance improvement with low cost. However, adaptive transmission based on maximising the sum rate of macro and femto networks requires perfect channel quality ...
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Femtocell networks are expected to offer significant performance improvement with low cost. However, adaptive transmission based on maximising the sum rate of macro and femto networks requires perfect channel quality information (CQI) of the macro and femto links. This assumption requires an infinite resolution feedback link, which is not always practical since it requires an excessive amount of bandwidth. This study considers the problem of maximising average sum rate under the constraint of average maximum power transmission at macro and femto users. A suboptimal iterative algorithm is developed for finding the optimal CQI quantisers as well as the discrete power and rate at macro and femto transmitter for each quantised CQI level so as to maximise the average sum rate of the system. The author's numerical results give the number of bits required to sufficiently represent the CQI to achieve almost the maximum sum rate attained using full knowledge of the CQI.
This paper presents a multiphysical modeling of a two-dimensional (2-D) reversible tubular solid oxide cell. The developed model can represent both a solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOF...
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This paper presents a multiphysical modeling of a two-dimensional (2-D) reversible tubular solid oxide cell. The developed model can represent both a solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOFC) operations. By taking into account of the electrochemical, fluidic, and thermal physical phenomena, the presented model can accurately describe the multiphysical effects inside a cell for both fuel cell and electrolysis cell operation under entire working range of cell current and temperature. In addition, an iterative solver is proposed which is used to solve the 2-D distribution of physical quantities along the tubular cell. The proposed model is suitable for embedded applications, such as real-time simulation or online diagnostic control. The reversible solid oxide cell model is then validated experimentally in both SOEC and SOFC configurations under different species partial pressures, operating temperatures, and current densities conditions.
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