scholarly journals CALIBRATION OF BONDING MODEL PARAMETERS FOR COATED FERTILIZERS BASED ON PSO-BP NEURAL NETWORK

2021 ◽  
pp. 255-264
Author(s):  
Xin Du ◽  
Cailing Liu ◽  
Meng Jiang ◽  
Hao Yuan ◽  
Lei Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Correlation Coefficients ◽  
Model Parameters ◽  
Uniaxial Compression Test ◽  
Steepest Ascent ◽  
Process Testing ◽  
Element Simulation ◽  
Simulation Parameters ◽  
Coated Fertilizer ◽  
Dem Model

In this paper, the ultimate crushing displacement and ultimate crushing load of the coated fertilizer granules were obtained by uniaxial compression test as 0.450 mm and 58.668 N, respectively. Then the DEM model of the encapsulated fertilizer was established, and the Plackett-Burman and Steepest ascent tests were taken to determine the factors that had significant effects on the results and their ranges of values, respectively. Finally, the PSO_BP neural network was trained using full-factor test data, and the correlation coefficients of training process, validation process, testing process and overall performance were obtained as 0.98057, 0.95781, 0.96724 and 0.97459, respectively, indicating that the trained PSO_BP neural network fits well and can predict the ultimate crushing displacement and ultimate crushing load. The ultimate crushing displacement Y1 and ultimate crushing load Y2 are 0.450 mm and 58.703 N, with a minimum relative error of 0.06% from the actual value. This study can provide new methods and ideas for the calibration of discrete element simulation parameters.

Parameter Determination of Discrete Element Model of Conditioned Soil by Genetic Neural Network

2010 ◽  
Vol 150-151 ◽  
pp. 27-31
Author(s):  
Li Wu ◽  
Tian Min Guan ◽  
Fu Zheng Qu ◽  
Shou Ju Li
Keyword(s):  
Neural Network ◽  
Triaxial Test ◽  
Element Model ◽  
Discrete Element ◽  
Triaxial Tests ◽  
Model Parameters ◽  
Discrete Element Model ◽  
Discrete Element Simulation ◽  
Element Simulation ◽  
Genetic Neural Network

The inversion method combining the genetic neural network and the discrete element simulation of triaxial tests is firstly described for determining the discrete element model parameters of the conditioned soil. The purpose is to make the error of the simulation curves and the laboratory curves of the triaxial test minimum. The solve approach is the parameters identification based on the genetic neural network. The network training sample is provided by the discrete element simulation. The input sample is the simulation curves of triaxial test, and the output sample is the model parameters. The laboratory triaxial test curves of the conditioned soil are used to determine its model parameters. The simulation curves calculated with the inversed parameters match the laboratory curves well, which illustrate that the discrete element model can accurately predict the deformation characteristics and flow patterns of conditioned soils.

scholarly journals Intelligent Parameter Inversion of Fractional-Order Model Based on BP Neural Network

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 4) ◽  
Author(s):  
Renbo Gao ◽  
Fei Wu ◽  
Cunbao Li ◽  
Jie Chen ◽  
ChenXin Ji
Keyword(s):  
Neural Network ◽  
Numerical Model ◽  
Bp Neural Network ◽  
Inversion Method ◽  
Engineering Practice ◽  
Model Parameters ◽  
Uniaxial Compression Test ◽  
Test Scheme ◽  
Salt Rock ◽  
Parameter Inversion

Abstract To explore creep parameters and creep characteristics of salt rock, an Ansys numerical model of salt rock sample was established by using fractional creep constitutive model of salt rock, and an orthogonal test scheme was designed based on uniaxial compression test of salt rock samples. A large number of training data were obtained by combining the numerical model with the experimental scheme, and the model parameters were inverted by using the BP neural network. The model parameters are used for forwarding calculation, and the results are in good agreement with the measured strain data. This shows that the model parameter inversion method proposed in this paper can obtain reasonable parameter values and then accurately predict the creep behaviour of salt rock, which provides a good technical basis for related engineering practice and scientific research in the future.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

10.2196/16981 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e16981
Author(s):  
Yang Xiang ◽  
Hangyu Ji ◽  
Yujia Zhou ◽  
Fang Li ◽  
Jingcheng Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Asthma Exacerbation ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Model Parameters ◽  
Supporting Evidence ◽  
Contributing Factors ◽  
Clinical Variables ◽  
Personalized Risk

Background Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. Objective This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. Methods We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. Results The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. Conclusions The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

scholarly journals ОПТИМІЗАЦІЯ ПРОГНОЗУВАННЯ РОЗВИТКУ АРТЕРІАЛЬНОЇ ГІПЕРТЕНЗІЇ НА ОСНОВІ КОМПЛЕКСНОГО ЗАСТОСУВАННЯ ІНФОРМАЦІЙНИХ МЕТОДИК ІЗ РОЗРОБКОЮ ДИФЕРЕНЦІАЛЬНО-ДІАГНОСТИЧНИХ КРИТЕРІЇВ ПРИ НАДАННІ ПЕРВИННОЇ МЕДИЧНОЇ ДОПОМОГИ

2016 ◽  
Author(s):  
V. P. Martsenyuk ◽  
P. R. Selskyy ◽  
B. P. Selskyy
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Primary Health Care ◽  
Roc Analysis ◽  
Correlation Coefficients ◽  
Network Clustering ◽  
Care Level ◽  
Primary Health ◽  
Initial Survey

The paper describes the optimization of the prediction of disease at the primary health care level with a complex phased application of information techniques. The approach is based on analysis of the average values of indicators, correlation coefficients, using multi-parameter neural network clustering, ROC-analysis and decision tree.The data of 63 patients with arterial hypertension obtained at teaching and practical centers of primary health care were used for the analysis. It has been established that neural network clasterization can effectively and objectively allocate patients into the appropriate categories according to the level of average indices of patient examination results. Determination of the sensitivity and specificity of hemodynamic parameters, including blood pressure, and repeated during the initial survey was conducted using ROC-analysis.The diagnostic criteria of decision-making were developed to optimize the prediction of disease at the primary level in order to adjust examination procedures and treatment based on the analysis of indicators of patient examination with a complex gradual application of information procedures.

Aircraft Health Diagnosis Method Based on ARMA Model and Probabilistic Neural Network

2011 ◽  
Vol 225-226 ◽  
pp. 527-530 ◽  
Author(s):  
Jian Guo Cui ◽  
Bo Han Song ◽  
Shi Liang Dong ◽  
Hai Gang Liu ◽  
Qing Zhao
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Arma Model ◽  
Information Criterion ◽  
Model Parameters ◽  
Health State ◽  
Feature Vectors ◽  
Emission Signal ◽  
Diagnosis Method ◽  
Health Diagnosis

In order to diagnose the health state of Aircraft effectively, a new method based on ARMA Model and probabilistic neural network(PNN) is proposed in this paper. First, an ARMA model is built using the original acoustic emission signal of aircraft crucial components, then use the autoregressive approximation theory to estimate model parameters, and order of the model is calculated according to Akaike Information Criterion(AIC). Use the autoregressive parameters to build feature vectors, then the probabilistic neural network is used to carry out the recognition of these feature vectors, and the health state of aircraft crucial components is effectively diagnosed. After the application on certain type of real aircraft, this method is proved to be capable of detecting the fatigue crack on crucial structural components. And we can conclude that the method is an effective way to carry out aircraft health diagnosis.

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