The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H-infinity control scheme. Instead of considering the conventional network...
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The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H-infinity control scheme. Instead of considering the conventional network transmission delay, the paper introduces the probability distribution based network transmission delay, which is more compatible with real time-simulations. Due to distributed transmission delay, the closed-loop network con-trol system has become distributed delay system. To make use of full information about the membership function, the Lyapunov function candidate is designed as fuzzy-membership-dependent Lyapunov function. A switching concept with respect to the rate of changes in membership functions is introduced to design the optimal control gain matrices for the considered system. The main advantage of the fuzzy-membership-dependent Lyapunov function is that it can ensure the less conservatism of the result without increasing the number of decision variables. To validate the effectiveness of the proposed approach, two kinds are real-time systems are taken into account, one is nonlinear mass-spring-damper model, followed by an industrial example, say, surface mounted permanent mag-net synchronous motor model. The corresponding simulation results show the superiority of the proposed results over the existing works. (C) 2021 Elsevier Inc. All rights reserved.
When criticality calculation is performed with Reactor Monte Carlo (RMC) code for reactor analysis, the fission source iterative method is selected. There are two strategies to sample the fission neutrons in each fiss...
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When criticality calculation is performed with Reactor Monte Carlo (RMC) code for reactor analysis, the fission source iterative method is selected. There are two strategies to sample the fission neutrons in each fissile region: the conventional Proportional Stratified Sampling Method that focuses on the real physical process, and the author-developed Optimal Stratified Sampling Method that focuses on the optimal mathematics treatment. The Optimal Stratified Sampling Method have the effect of global variance reduction and could be easily implemented with the use of optimal source bias coefficients. However, the calculation of optimal source bias coefficients needs the values of variances in advance, which is almost impossible. To meet the requirement, a batch Approximate Method is then put forward. The batch Approximate Method based on the batch algorithm and Shannon Entropy Diagnosis, eliminates the correlation among cycles during the criticality calculation. Combining the Optimal Stratified Sampling Method and the batch Approximate Method, the Optimal Source Bias Method is established and developed in RMC code. The Optimal Source Bias Method was tested with a standard Pressurized Water Reactor assembly. The effect of global variance reduction was found, as well as the flattening of variance distribution. So, the new method is valuable for the high fidelity computation of the RMC code. (C) 2018 Elsevier Ltd. All rights reserved.
In this paper, two families of batch algorithms are constructed in the support vector regression (SVR) framework for blind equalization of multilevel signals. Specifically, the error functions of constant modulus algo...
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ISBN:
(纸本)9781538643624
In this paper, two families of batch algorithms are constructed in the support vector regression (SVR) framework for blind equalization of multilevel signals. Specifically, the error functions of constant modulus algorithm CMA(p, 2) and multimodulus algorithm MMA(p,2) are contained in the penalty term of the SVR. Simulation results show that the proposed MMA(p,2)-based algorithms perform better than the CMA(p,2)based ones, which exhibit lower residual intersymbol interference (ISI) and higher probability of convergence. With respect to conventional dual-mode scheme, the MMA(p,2)-based algorithms show better performance in the case of higher noise or smaller data block, therefore they are robust and more suitable for multilevel signals. In addition, they avoid tedious switching mechanism of dual-mode scheme and overcome phase rotation.
This paper provides a comprehensive empirical performance analysis of various trajectory compression algorithms for offline compression of trajectory data of sea borne objects which include boats, trawlers, ships etc....
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ISBN:
(纸本)9781509063673
This paper provides a comprehensive empirical performance analysis of various trajectory compression algorithms for offline compression of trajectory data of sea borne objects which include boats, trawlers, ships etc. Our empirical study uses data from a surveillance application. The surveillance application receives data from various sensors which include GPS integrated sensors, Radars, AIS, S-AIS etc. The algorithms under consideration include the classical Douglas Peucker algorithm, SQUISH, OLDCAT, SPM. These algorithms have been compared with respect to the capability to preserve the spatio-temporal information and execution time.
This paper describes a batch algorithm for integrating heterogeneous e-Health data using Web Service Model in a low bandwidth environment. The focus is on the problem of higher volume of data stored in relational data...
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ISBN:
(纸本)9781479968992
This paper describes a batch algorithm for integrating heterogeneous e-Health data using Web Service Model in a low bandwidth environment. The focus is on the problem of higher volume of data stored in relational databases and the integration architecture with uncertain schema. In specific the paper presents a batch algorithm for efficient integration and management of heterogeneous health records in an environment with higher network disconnection. The web service application for implementation was developed using Visual Basic .NET. The validation and test cases of the software was done using Demographical Surveillance data sets as well as baseline census data for sample vital registration with verbal autopsy. The result indicates that the proposed algorithm is capable of transferring data in a network with higher disconnection. The analysis of the execution time among four configured relational databases indicates that time taken to transfer data between MySQL engine and SQL server engine is shorter compared to other relation databases under the test. In addition, the architecture and the embedded algorithm may be useful for integrating e-Health records from different research group so that scientists can access them at a single point.
Blind equalization typically achieves parameter optimization through cost minimization using stochastic gradient descent in both batch and adaptive algorithms. In general, stochastic descent algorithms typically requi...
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ISBN:
(纸本)9781424456383
Blind equalization typically achieves parameter optimization through cost minimization using stochastic gradient descent in both batch and adaptive algorithms. In general, stochastic descent algorithms typically require large number of iterations or long data samples to converge. The batch approach is generally based on data reuse (recycling) and refiltering to recompute the cost gradient after each iterative parameter update, thereby causing long processing delays. In this work, we present a novel steepest descent batch algorithm that does not require data recycling. We consider the popular Constant Modulus algorithm and the Minimum Entropy Deconvolution for normalized cumulant maximization. Both algorithms utilize 4-th order cumulants. The proposed steepest descent batch implementation of both algorithms converge rapidly in a few iterations and deliver superior performance without the delay due to data recycling and refiltering(1).
By combining the batch algorithm with the kernel trick, an improved kernel blind source separation (IKBSS) is presented. The lKBSS has not only a better performance but also a less computational complexity compared to...
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By combining the batch algorithm with the kernel trick, an improved kernel blind source separation (IKBSS) is presented. The lKBSS has not only a better performance but also a less computational complexity compared to the original kernel blind source separation (KBSS). (c) 2005 Elsevier B.V. All rights reserved.
Neural Gas (NG) constitutes a very robust clustering algorithm given Euclidean data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-org...
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Neural Gas (NG) constitutes a very robust clustering algorithm given Euclidean data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments. (c) 2006 Elsevier Ltd. All rights reserved.
Karhunen-Loeve transformation (KLT) is a Popular method for dimensional reduction and feature extraction in image analysis. signal processing, automatic control systems, and so on, while the drawback of the KLT is exp...
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ISBN:
(纸本)0780385543
Karhunen-Loeve transformation (KLT) is a Popular method for dimensional reduction and feature extraction in image analysis. signal processing, automatic control systems, and so on, while the drawback of the KLT is expensive computation. In this paper, we propose a novel updating algorithm for KLT, the rank-k updating algorithm, which has advantages especially for image sequences: it is faster than batch algorithm, it can handle the dynamic database, and it does not save the entire database. Furthermore it makes the active learning and recognition possible in Computer vision. Finally we analyze the Computational complexity and error of the algorithm. We also show its application in face analysis. The experimental results demonstrate the efficiency Of Our algorithm.
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