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排序方式: 共有169条查询结果,搜索用时 15 毫秒
1.
The aim of this work is to derive an accurate model of two-dimensional switched control heating system from data generated by a Finite Element solver. The nonintrusive approach should be able to capture both temperature fields, dynamics and the underlying switching control rule. To achieve this goal, the algorithm proposed in this paper will make use of three main ingredients: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and artificial neural networks (ANN). Some numerical results will be presented and compared to the high-fidelity numerical solutions to demonstrate the capability of the method to reproduce the dynamics. 相似文献
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Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation 下载免费PDF全文
This article reports on the implementation of a soft computing technique based on artificial neural networks (ANNs) in analyzing the wear performance of a new class of hybrid composites filled with Linz–Donawitz slag (LDS). LDS is a major solid waste generated in huge quantities during steel making. It comes from slag formers such as burned lime/dolomite and from oxidizing of silica, iron etc. while refining the iron into steel in the LD furnace. In this work, hybrid composites consisting of short glass fiber (SGF) reinforced epoxy filled with different LDS content (0, 7.5, 15 and 22.5 wt%) are prepared by simple hand lay‐up technique. Solid particle erosion trials, as per ASTM G 76 test standards, are conducted on the composite samples following a well‐planned experimental schedule based on Taguchi design of experiments. Significant process parameters predominantly influencing the rate of erosion are identified. The study reveals that the LDS content is the most significant among various factors influencing the wear rate of these composites. Further, a model based on ANN for the prediction of erosion performance of these composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well‐trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers
- Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
- Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
- In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
- Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.
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This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers
- Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.
- Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.
- More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.
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Azlan Mohd Zain Habibollah HaronSultan Noman Qasem Safian Sharif 《Applied Mathematical Modelling》2012,36(4):1477-1492
Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (Ra) must be formulated in the standard mathematical model. To predict the minimum Ra value, the process of modeling is taken in this study. The developed model deals with real experimental data of the Ra in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum Ra value. The results show that regression and ANN models have reduced the minimum Ra value of real experimental data by about 1.57% and 1.05%, respectively. 相似文献
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Huixian Wei Jianguo Ma Zhengwu Wang 《Journal of Dispersion Science and Technology》2013,34(3):319-326
Formulation optimization of emulsifiers for preparing multiple emulsions was performed in respect of stability by using artificial neural network (ANN) technique. Stability of multiple emulsions was expressed by the percentage of reserved emulsion volume of freshly prepared sample after centrifugation. Individual properties of multiple emulsions such as droplet size, δ, viscosity of the primary and the multiple emulsions were also considered. A back‐propagation (BP) network was well trained with experimental data pairs and then used as an interpolating function to estimate the stability of emulsions of different formulations. It is found that using mixtures of Span 80 and Tween 80 with different mass ratio as both lipophilic and hydrophilic emulsifiers, multiple W/O/W emulsions can be prepared and the stability is sensitive to the mixed HLB numbers and concentration of the emulsifiers. By feeding ANN with 39 pairs of experimental data, the ANN is well trained and can predict the influences of several formulation variables to the immediate emulsions stability. The validation examination indicated that the immediate stability of the emulsions predicted by the ANN is in good agreement with measured values. ANN therefore could be a powerful tool for rapid screening emulsifier formulation. However, the long‐term stability of the emulsions is not good, possibly due to the variation of the HLB number of the mixed monolayers by diffusion of emulsifier molecules, but can be greatly improved by using a polymer surfactant Arlacel P135 to replace the lipophilic emulsifier. 相似文献
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M. Mansourpoor S. Osfouri A. A. Izadpanah 《Journal of Dispersion Science and Technology》2019,40(2):161-170
Wax deposition is a frequent problem in oil pipelines and down-stream industries. Correct prediction of wax formation conditions is required to prevent this phenomenon. In this study, wax appearance temperature (WAT) of 12 Iranian oil and condensate samples were measured using viscometry data and differential scanning Calorimetry (DSC) analysis. Also, a new empirical correlation and intelligent artificial neural network (ANN) model were developed to estimate wax disappearance temperature (WDT) of crude oils. Specific gravity, pressure, and molecular weight of oil sample were used as input variables for these models. The ANN model was trained using different hidden neurons and training algorithms. Experimental measurements studies were used for validation of the new correlation. Comparing the results indicated that the ANN model has 0.27% error while most thermodynamic models have an average error of 0.35% to 2.19%. Also, the proposed correlation can predict WDT with good accuracy and minimum input data. Results show that this correlation has a maximum error of 1.16% for 310 published experimental data and 1.19% for 9 Iranian samples. 相似文献
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