An a posteriori error estimate suitable for finite-volume adaptive computations is presented. The error estimate combines the least-squares method regressions with the residual computation, which provides information ...
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An a posteriori error estimate suitable for finite-volume adaptive computations is presented. The error estimate combines the least-squares method regressions with the residual computation, which provides information from the grid quality and the governing equations for a better local adaptation of the unstructured grid. The decision algorithm uses the information provided by the error estimate and does not require problem-dependent constants;it also uses a grid interface correction step to provide a smoother and a high-quality adaptive grid. The proposed error estimate and the adaptive refinement algorithm are verified against analytic solution for different two-dimensional problems. In addition, calculations of three-dimensional laminar flows with different types of unstructured grids have demonstrated the applicability of the adaptive method.
This paper presents a data filtering based stochastic gradient algorithm for estimating the parameters of multivariable Hammerstein FIR-MA-like systems. By filtering the input and output data, the FIR-MA-like model is...
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This paper presents a data filtering based stochastic gradient algorithm for estimating the parameters of multivariable Hammerstein FIR-MA-like systems. By filtering the input and output data, the FIR-MA-like model is transformed into a controlled autoregressive model. The examples confirm that the proposed algorithm is more accurate and has a better performance than the stochastic gradient algorithm. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and C...
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ISBN:
(纸本)9781509013296
Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20～40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A.
The problem of controlled passage through resonance zone for mechanical systems with several degrees of freedom is studied. Control algorithm design is based on speed-gradient method and estimate for the frequency of ...
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The problem of controlled passage through resonance zone for mechanical systems with several degrees of freedom is studied. Control algorithm design is based on speed-gradient method and estimate for the frequency of the slow motion near resonance (Blekhman frequency). The simulation results for two-rotor flexible vibration units illustrating efficiency of the proposed algorithms and fractal dependence of the passage time on the initial conditions are presented. The novelty of the results is in demonstration of good behavior of the closed loop system if flexibility is taken into account.
Target detection is a key issue in processing hyperspectral images (HSIs). However, current spectral-identification-based algorithms are sensitive to noise during acquisition of the data. In most cases, the denoising ...
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Target detection is a key issue in processing hyperspectral images (HSIs). However, current spectral-identification-based algorithms are sensitive to noise during acquisition of the data. In most cases, the denoising algorithms cannot preserve small targets. In this paper, to overcome this problem, we propose a new algorithm which reduces noise to improve the target detection efficiency of HSI with small targets. First, a three-dimensional wavelet packet transform (3D-WPT) is used to decompose the HSI into several coefficient sets and models each coefficient set as a tensor. Then we exploit a powerful multilinear algebra model named parallel factor analysis (PARAFAC) to filter each tensor. The experiments conducted in both simulated and real-world hyperspectral images demonstrated the performance of the proposed method.
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exp...
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An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.
Community structure plays a key role in analyzing network features and helping people to dig out valuable hidden information. However, how to discover the hidden community structures is one of the biggest challenges i...
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Community structure plays a key role in analyzing network features and helping people to dig out valuable hidden information. However, how to discover the hidden community structures is one of the biggest challenges in social network analysis, especially when the network size swells to a high level. Infomap is a top-class algorithm in nonoverlapping community structure detection. However, it is designed for single processor. When tackling large networks, its limited scalability makes it less effective in fully utilizing server resources. In this paper, based on infomap, we develop a scalable parallel nonoverlapping community detection method, Pinfomr (parallel Infomap with MapReduce), which utilizes the MapReduce framework to solve the two problems. Experiments on artificial networks and real datasets show that our parallel method has satisfying performance and scalability.
This study presents a new approach to detecting and classifying voltage disturbances in electrical distribution systems based on wavelet transform and artificial immune algorithm. This proposal unifies the negative se...
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This study presents a new approach to detecting and classifying voltage disturbances in electrical distribution systems based on wavelet transform and artificial immune algorithm. This proposal unifies the negative selection artificial immune algorithm with the discrete wavelet transform concept. Thus, the measurements obtained in a distribution substation by the supervisory control and data acquisition acquisition system are transformed into the wavelet domain. Afterward, a negative selection artificial immune system realises the diagnosis, identifying and classifying the abnormalities. The principal application of this tool is to aid the system operation during faults as well as to supervise the protection system. To evaluate the performance of the proposed method, two distribution systems were modelled in EMTP software: an 84-bus test system and a 134-bus real system. The results show a good performance, emphasising the precision of the diagnosis.
In this study, a fast gradient-based compressive sensing (FGB-CS) for noise image and video is proposed. Given a noise image or video, the authors first make it sparse by orthogonal transformation, and then reconstruc...
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In this study, a fast gradient-based compressive sensing (FGB-CS) for noise image and video is proposed. Given a noise image or video, the authors first make it sparse by orthogonal transformation, and then reconstruct it by solving a convex optimisation problem with a novel gradient-based method. The main contribution is twofold. Firstly, they deal with the noise signal reconstruction as a convex minimisation problem, and propose a new compressive sensing based on gradient-based method for noise image and video. Secondly, to improve the computational efficiency of gradient-based compressive sensing, they formulate the convex optimisation of noise signal reconstruction under Lipschitz gradient and replace the iteration parameter by the Lipschitz constant. With this strategy, the convergence of our FGB-CS is reduced from O(1/k) to O(1/k(2)). Experimental results indicate that their FGB-CS method is able to achieve better performance than several classical algorithms.
We propose a novel automatic side-scan sonar image enhancement algorithmbased on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human ...
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We propose a novel automatic side-scan sonar image enhancement algorithmbased on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms.
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