Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance re...
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Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.
Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algo...
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Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field. (C) 2019 Elsevier B.V. All rights reserved.
Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized b...
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Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized by mind evolutionary algorithm, network optimized by genetic algorithm, network optimized by particle swarm algorithm and network optimized by adaptive weight particle swarm algorithm are used to forecast the energy consumption. The optimal values of the learning rate and hidden layer node number are choosen. The characteristics of various kinds of optimization algorithm are compared. The neural network optimized by adaptive weight particle swarm algorithm is proved to be the most accurate in predicting energy consumption.
In this paper, the Oppositional Whale optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of A...
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In this paper, the Oppositional Whale optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.
In order to accurately obtain the wax deposition rate model, according to the kinetic principle of wax deposition, several factors affecting the wax deposition rate were selected, and by a optimization software of Fir...
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In order to accurately obtain the wax deposition rate model, according to the kinetic principle of wax deposition, several factors affecting the wax deposition rate were selected, and by a optimization software of First optimization(1stOpt), The parameters of two typical wax deposition rate models are solved respectively based on optimization algorithm combined by Levenberg-Marquardt (L-M) algorithm and global optimization and the calculated data were compared. The results show that: compared with the model parameters obtained by least squares method;the model parameters obtained by this optimization algorithm can describe the variation of wax deposition rate more accurately. The maximum error is reduced from 30% to 10%, and the average error is reduced from 10.3% to 2.42%;Alike, the mathematical model obtained by this optimization algorithm is also better than that solved by L-M algorithm alone. The maximum error is reduced from 13.62% to 11%, and the average error is reduced from 6.46% to 4.77%. To a certain extent, this optimization algorithm avoids the premature phenomenon caused by using Levenberg-Marquardt alone. In addition, the use of the optimization algorithm does not require suitable initial values, prior knowledge and programming, easy to use, and has important use value.
The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is propo...
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The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is proposed that is an optimization algorithm for the auxiliary anode system of a grounding grid based on improved simulated annealing. The mathematical model of the auxiliary anode system is inferred from the mathematical model of cathodic protection. On that basis, the parameters of the finite element model are optimized with the improved simulated annealing algorithm, thereby the auxiliary anode system of a grounding grid with optimized parameters is structured. Then the algorithm is proven as valid through experiments. The precision of the optimized parameters is improved by about 1.55% with respect to the Variable Metric Method and the Genetic algorithm, so it can provide a basis for parameter design in the auxiliary anode system of a grounding grid.
Accurate R-peak detection is very important for arrhythmia diagnosis. Our previous effective R detection algorithm consisted of three strategies: band-pass filter, adaptive definition of interesting block and dynamic ...
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ISBN:
(纸本)9781538691847
Accurate R-peak detection is very important for arrhythmia diagnosis. Our previous effective R detection algorithm consisted of three strategies: band-pass filter, adaptive definition of interesting block and dynamic threshold. Then, it adopted the optimization algorithm to replace the knowledge-based theory and found out the suitable parameters (El, F2, N, WI, W2, beta and mu) in R detection algorithm quickly and obtained the high performance of detecting R peaks (99.77%). In order to improve the performance of the previous study, this study proposes to add the median filter in the algorithm to correct baseline wander components of electrocardiography (ECG) signals. It is necessary to defined two parameters (Ti and T2) in median filter. Therefore, this study adopts particle swarm optimization (PSO) to find the suitable parameters (Ti, T2, F1, F2, N, W1, W2, beta and mu) in the proposed method. The proposed method is applied to MIT-BIH arrhythmia database. The results show that PSO can find out the suitable parameters in R detection algorithm and have a higher accuracy (99.95%) than one of the previous study.
This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of...
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This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems,the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.
Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer's experience, an automatic aerodynamic optimization design platfo...
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Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer's experience, an automatic aerodynamic optimization design platform for automotive shape was built based on mesh deformation technology, surrogate model and optimization algorithm in this paper. A parameterized model of an automotive was established. Latin hypercube method was adopted to select sample points. The drag coefficients corresponding to sample points were calculated by CFD simulation, whereby the influence of each parameter on drag coefficient was obtained. By comparing the calculation time, optimization effect and optimization accuracy of 9 combinations of surrogate models and optimization algorithms, the combination of RBF model and NLPQL algorithm was selected as the optimal one which is the most appropriate for the aerodynamic optimization design for automotive shape.
In this paper, a novel Gravitational Artificial Bee Colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strateg...
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ISBN:
(纸本)9781538682463
In this paper, a novel Gravitational Artificial Bee Colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strategy was introduced into the artificial bee colony algorithm, and a gravitational bee colony was established. The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation, which makes the optimization process more effectively and efficiently. Based on GABC algorithm, an intelligent kernel clustering model was established, in which the clustering center and kernel parameters were combined to be the optimal variable, while the clustering index was used as the objective function. GABC was utilized to find the optimal result of the clustering model. The standard testing functions were used to test the proposed algorithm, and GABC showed high accuracy and convergence speed. Then the testing data and fault samples were utilized to test the performance of GABC based clustering model, and its superiority on effectiveness and efficiency was demonstrated.
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