An adaptive optimization water-marking algorithm based on Genetic algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model fo...
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ISBN:
(纸本)9780819469533
An adaptive optimization water-marking algorithm based on Genetic algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital water-marking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.
A fundamental problem in water quality modeling is adequately representing the changing state of aquatic ecosystems as accurately as possible, but with appropriate mathematical relationships without creating a highly ...
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A fundamental problem in water quality modeling is adequately representing the changing state of aquatic ecosystems as accurately as possible, but with appropriate mathematical relationships without creating a highly complex and overly parameterized model. A model more complex than necessary will require more input and results in unaffordable calibration times. In this work we propose and test a calibration strategy for a one-dimensional dynamic physical ecological model (DYRESM-CAEDYM) to reproduce the seasonal changes in the functional composition of the phytoplankton community existing in El Gergal reservoir (Seville, Spain). The community is described as a succession of functional groups with different response to environmental conditions. First, we performed a sensitivity analysis to identify the parameters to include in the calibration process, and then applied a global optimization algorithm to fit the model for each algal group in a sequential fashion. Finally we simulated all the functional groups adopting parameter values established during the group-by-group calibrations. Our results show that the performance of this approach is strictly related with: (1) the level of system description (i.e. the model structure and the number of functional groups simulated);(2) the level of information included in the calibration process (i.e. the observations);and (3) the non-linear interactions among functional groups. Functional segmentation of the model should be minimized even though groups with different environmental requirements must be discriminated. Although magnitudes of biomass peaks were not always estimated correctly, the calibrated model was able to predict peak sequence and timing of dominant phytoplankton groups. Thus our study showed that: (1) model structure and nature of observations adopted have to be in agreement with the level of organization in the system;(2) integration of automatic calibration strategies is a useful approach in complex determini
Bilevel decision addresses the problem in which two levels of decision makers each tries to optimize their individual objectives under certain constraints, and to act and react in an uncooperative and sequential manne...
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Bilevel decision addresses the problem in which two levels of decision makers each tries to optimize their individual objectives under certain constraints, and to act and react in an uncooperative and sequential manner. Given the difficulty of formulating a bilevel decision problem by mathematical functions, a rule sets-based bilevel decision (RSBLD) model was proposed. This article presents an algorithm to solve a RSBLD problem. A case-based example is given to illustrate the functions of the proposed algorithm. Finally, a set of experiments is analyzed to further show the functions and the effectiveness of the proposed algorithm.
A methodology is developed for determining boundaries of spectral zones for pebble bed reactors A spectral zone is defined as a region made up of a number of nodes whose characteristics are collectively similar and th...
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A methodology is developed for determining boundaries of spectral zones for pebble bed reactors A spectral zone is defined as a region made up of a number of nodes whose characteristics are collectively similar and that are assigned the same few-group diffusion constants The spectral zones are selected in such a manner that the difference (error) between the reference transport solution and the diffusion code solution takes a minimum value This is achieved by choosing spectral zones through optimally minimizing this error The objective function for the optimization algorithm is the total reaction rate error which is defined as the sum of the leakage absorption and fission reaction rates errors in each zone The selection of these spectral zones is such that the core calculation results based on diffusion theory are within an acceptable tolerance as compared to a proper transport reference solution Through this work a consistent approach for identifying spectral zones that yield more accurate diffusion results is introduced (C) 2010 Elsevier Ltd All rights reserved
This paper discusses the problems of the delay-dependent robust stability and stabilization for a class of linear time-delay uncertain systems with saturating actuators. Some new delay-dependent stability criteria are...
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This paper discusses the problems of the delay-dependent robust stability and stabilization for a class of linear time-delay uncertain systems with saturating actuators. Some new delay-dependent stability criteria are derived by taking the relationships between the terms in the Leibniz Newton formula into account. The stability conditions are formulated as linear matrix inequalities that can be easily solved by various convex optimization algorithms or computing software. Moreover, the stability criteria are extended to the design of a stabilizing state feedback controller. Numerical examples demonstrate that these criteria are effective and are an improvement on previous ones.
This paper deals with the optimization of aircraft noise-abatement departure procedures. A multicriteria optimization strategy is presented, where the fairness of the optimal trajectories is assessed vis-a-vis the dif...
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This paper deals with the optimization of aircraft noise-abatement departure procedures. A multicriteria optimization strategy is presented, where the fairness of the optimal trajectories is assessed vis-a-vis the different noise-sensitive locations around the airport of study. This equitable optimization is formulated as the minimization of the maximum noise-annoyance deviation regarding all considered locations. This strategy is complemented with an iterative lexicographic optimization algorithm which, in turn, guarantees the Pareto efficiency condition of the final solution. Aircraft operating costs are also considered by neglecting the marginal benefits of noise reduction below a certain threshold value. An application example is shown (as an illustrative case) based on a departure of runway 02 at Girona airport in Catalonia, Spain. The results show the feasibility of this technique, which is intended to be used by procedure designers or airport authorities.
Due to data sparseness and attribute redundancy in high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. To effectively address this issue, this paper presents a new opti...
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Due to data sparseness and attribute redundancy in high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. To effectively address this issue, this paper presents a new optimization algorithm for clustering high-dimensional categorical data, which is an extension of the k-modes clustering algorithm. In the proposed algorithm, a novel weighting technique for categorical data is developed to calculate two weights for each attribute (or dimension) in each cluster and use the weight values to identify the subsets of important attributes that categorize different clusters. The convergence of the algorithm under an optimization framework is proved. The performance and scalability of the algorithm is evaluated experimentally on both synthetic and real data sets. The experimental studies show that the proposed algorithm is effective in clustering categorical data sets and also scalable to large data sets owning to its linear time complexity with respect to the number of data objects, attributes or clusters. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
Geometry optimization of ethane clusters (C2H6)(n) in the range of n <= 25 is carried out with a Morse potential. A heuristic method based on perturbations of geometries is used to locate global minima of the clust...
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Geometry optimization of ethane clusters (C2H6)(n) in the range of n <= 25 is carried out with a Morse potential. A heuristic method based on perturbations of geometries is used to locate global minima of the clusters. The following perturbations are carried out: (1) the molecule or group with the highest energy is moved to the interior of a cluster, (2) it is moved to stable positions on the surface of a cluster, and (3) orientations of one and two molecules are randomly modified. The geometry obtained after each perturbation is optimized by a quasi-Newton method. The global minimum of the dimer is consistent with that previously reported. The putative global minima of the clusters with 3 <= n <= 25 are first proposed and their building-up principle is discussed. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 32: 1345-1352, 2011
A minimum transmission power loss calculation method is proposed, and the specific load configuration is obtained to improve energy transmission capability of the aircraft electric power system. The method is divided ...
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A minimum transmission power loss calculation method is proposed, and the specific load configuration is obtained to improve energy transmission capability of the aircraft electric power system. The method is divided into three steps. First, a node table-style format of the aircraft electric power system is proposed according to its characteristics. Second, the transmission power loss equation is modeled by introducing the power flow calculation method. Third, the system transmission power losses with different load types are studied. Using particle swarm optimization algorithm, the minimum transmission power losses with different system load types are obtained. With the optimum load configuration, the transmission power loss is reduced and energy transmission capacity of the aircraft electric power system will be improved effectively.
The optimization algorithms of multiparameter controllers, control performance and stability of systems with PID and PIDD2D3 controllers have been investigated. The methodology suggested by Yu.P. Nikolaev for PID cont...
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The optimization algorithms of multiparameter controllers, control performance and stability of systems with PID and PIDD2D3 controllers have been investigated. The methodology suggested by Yu.P. Nikolaev for PID controllers has been used. The modification for multiparameter PIDD2D3 controller has been performed. The comparative analysis of stability of systems with these controllers has been performed at considerable time delay.
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