Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane foul...
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ISBN:
(纸本)9781509055081
Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particleswarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning,the main components of the factors affecting the flux of MBR membrane were analyzed .The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the ***, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.
Dissolved gas analysis is an effective method for the early detection of incipient fault in power *** improve the capability of interpreting the result of dissolved gas analysis,a technology is proposed in this *** Pa...
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Dissolved gas analysis is an effective method for the early detection of incipient fault in power *** improve the capability of interpreting the result of dissolved gas analysis,a technology is proposed in this *** particleswarmoptimization(PSO) technique is used to integrate with Back Propagation(BP) neural networks,and using particleswarm to optimize the network's weights and biases,the fault of transformers is simulated and *** results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio *** the algorithm based on PSO-BP network model provides a more accurate,safe and reliable result for the fault diagnosis of transformers.
The comprehensive evaluation of electric construction project is one of the main tasks in the early stage of the project. In order to accurately embody the nature, content, and scale of the reserve projects, a quantit...
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The comprehensive evaluation of electric construction project is one of the main tasks in the early stage of the project. In order to accurately embody the nature, content, and scale of the reserve projects, a quantitative evaluation method for reserve projects based on the contribution of power grid is proposed in this paper. An evaluation index system and its quantitative calculation method are established, And the optimal weight of each index is determined by utilizing the combination assigning method and particle swarm optimization algorithm. Then the contribution evaluation model of power grid reserve project is constructed based on the contribution and project investment. The dynamic sorting method for power grid reserve projects is determined by analyzing the coupling relationship and decoupling method in the reserve projects. Finally, the distribution network expansion projects in a certain city are taken as an example, and the results are presented to illustrate the usefulness of proposed method.
In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often lim...
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In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particleswarmoptimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.
The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm,and normalize the d...
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The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm,and normalize the data set to reduce the influence of fluctuation of attribute value for each dimension of sample set on accuracy of clustering ***,it obtains dissimilarity matrix and takes advantage of good global convergence ability of particle swarm optimization algorithm to improve proneness of K-means algorithm to be trapped in local *** effectiveness of the algorithm was verified via ***,although the algorithm herein performs well in part of small low dimensional data set,while how to effectively make cluster analysis on large high dimensional data still needs to be further researched.
In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) set-up is investigated. In the CR overlay mode, primary users (PUs) cooperate with c...
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ISBN:
(纸本)9781479935130
In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) set-up is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.
PSO algorithm is a kind of swarm intelligence optimizationalgorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO alg...
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PSO algorithm is a kind of swarm intelligence optimizationalgorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO algorithm still needs to be *** this paper,a modified PSO algorithm using exponent decline inertia weight is put forward and successfully applied to the parameter identification of the furnace pressure *** modified PSO algorithm combines the nonlinear optimization and genetic algorithm to optimize the inertia weight and acceleration constants of the basic PSO algorithm,and is proved to be effective in parameter identification.
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the ...
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ISBN:
(纸本)9781510813465
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the total cost including the initial investment, fuel costs, net loss costs, environmental damage costs, operation and maintenance costs and power purchase costs. To improve the acceptance ability of the distribution network, and to ensure the economy. Then, using the particle swarm optimization algorithm to solve a typical example, to show that the model and the proposed method is correct and effective.
According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition(EMD), genetic particleswarmoptimization(
ISBN:
(纸本)9781467389808
According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition(EMD), genetic particleswarmoptimization(
A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory ...
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A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory summation function of constraint conditions,to accelerate the convergence rate,a new region changed acceleration mechanism is used,and for shake of improving the local search ability,chaos search technology is *** modified algorithm not only improves the diversity of solution set but also makes the nondominated solutions approach the Pareto set as close as *** last,the algorithm is applied to three classical test functions;the optimization performance of modified algorithm is evaluated and numerical experimental results show the effectiveness of the proposed method.
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