In the present study, an artificial neural network (ANN) together with a heuristic algorithm, called particleswarmoptimization (PSO), was used to set up a methodology for selecting the optimal process parameters for...
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In the present study, an artificial neural network (ANN) together with a heuristic algorithm, called particleswarmoptimization (PSO), was used to set up a methodology for selecting the optimal process parameters for the mu EDM process. The developed methodology is characterized by a double direction functionality responding to different industry needs. Usually, in the industrial scenario, the operators are bound by the project specifications or by the limited availability of time. For this reason, a methodology tested only on a specific workpiece material, that involves limited input parameters or developed for the optimization of a single performance is limiting. The developed 2-steps model leaves operators free to establish which factors to impose for the optimization and allows to define the best solution for the production of a part. The validation of the model shows a good fit between predicted and experimental results.
The aim of this work is to present an adaptive maximum power point tracking (MPPT) approach for photovoltaic (PV) power generation system. Integrating the extension theory as well as the conventional perturb and obser...
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The aim of this work is to present an adaptive maximum power point tracking (MPPT) approach for photovoltaic (PV) power generation system. Integrating the extension theory as well as the conventional perturb and observe method, an maximum power point (MPP) tracker is made able to automatically tune tracking step size by way of the category recognition along a P-V characteristic curve. Accordingly, the transient and steady state performances in tracking process are improved. Furthermore, an optimization approach is proposed on the basis of a particleswarmoptimization (PSO) algorithm for the complexity reduction in the determination of weighting values. At the end of this work, a simulated improvement in the tracking performance is experimentally validated by an MPP tracker with a programmable system-on-chip (PSoC) based controller. (C) 2014 Elsevier Ltd. All rights reserved.
Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial in...
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Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial intelligence (Al) is a new method of solving engineering problems that has the ability to consider all effective parameters simultaneously. Moreover, it has generalization and the ability to learn directly from field data. In this paper, two models were designed using Al and data of 38 wells located in Maroun oil field. Both models were developed by modular neural network, to predict loss circulation in quality and quantity. Then, the particle swarm optimization algorithm was used to minimize loss circulation. The accuracy of two models in predicting loss circulation quantitatively and qualitatively is 0.94 and 0.98 %, respectively.
Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, improved adaptive particleswarm optimiz...
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Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, improved adaptive particleswarmoptimization (IAPSO) is proposed to solve the problem, which involves many conflicting objectives and constraints. The proposed algorithm takes particleswarmoptimization (PSO) as the main evolution method. To overcome the premature convergence of PSO, adjusting dynamically the two sensitive parameters of PSO guides the evolution direction of each particle in the evolution process. In the IAPSO method, an adaptive dynamic parameter control mechanism is applied to determine parameter settings. Moreover, a new strategy is proposed to handle the reservoir output constraint of ROO problem. Finally, the feasibility and effectiveness of the proposed IAPSO algorithm are validated by the Three Gorges Project (TGP) with 42.23 bkW power generation and XiLuoDo Project (XLDP) with 30.10 bkW. Compared with other methods, the IAPSO provides a better operational result with greater effectiveness and robustness, and appears to be better in terms of power generation benefit and convergence performance. Meanwhile, the optimal results could meet output constraint at each interval. (C) 2014 Elsevier B.V. All rights reserved.
Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the cli...
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Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particleswarmalgorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (>= 95%) with only a small number of training samples, which has a good clinical application value.
The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface...
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The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particleswarmoptimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.
In this paper, a communication strategy for hybrid particleswarmoptimization (PSO) with Bat algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particle...
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ISBN:
(纸本)9783319122854
In this paper, a communication strategy for hybrid particleswarmoptimization (PSO) with Bat algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.
In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved parti...
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ISBN:
(纸本)9783037859926
In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved particleswarmalgorithm. The algorithm uses the BIC measure function as a standard Bayesian network, while preserving the optimal particle case, the possibility of a mutation operation is added to decrease the algorithm into a local optimum. Through a typical Asia network, show that the algorithm is feasible, and other related algorithm is better than the experiment, the effectiveness of the algorithm. In this paper, the algorithm is verified from two aspects of theory and experiments, the results show that the algorithm is feasible.
In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suctio...
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ISBN:
(纸本)9783038350095
In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm (ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.
Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm...
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ISBN:
(纸本)9783038350156
Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.
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