scholarly journals A NEW HYBRID ANN MODEL FOR EVALUATING THE EFFICIENCY OF Π-TYPE FLOATING BREAKWATER

2017 ◽  
pp. 25 ◽  
Author(s):  
Hadi Ghasemi ◽  
Morteza Kolahdoozan ◽  
Enrique Pena ◽  
Javier Ferreras ◽  
Andrés Figuero
Keyword(s):  
Experimental Data ◽  
Wave Energy ◽  
Model Performance ◽  
Accurate Estimate ◽  
Ann Model ◽  
Proposed Model ◽  
Wave Basin ◽  
Short Period ◽  
Ann Models ◽  
Artificial Neural Network Ann

Floating breakwaters (FBs) have been specially regarded in recent years as a means to protect small harbors and marine structures against affection of short period waves. FBs have different types in terms of geometric shapes; one of the most common of which is the π-type FB. Generally, FBs are designed to reduce wave energy. The parameter used to evaluate the efficiency of FBs in the wave energy reduction is the wave transmission coefficient (Kt). Thus, accurate estimate of Kt is an important aspect in FBs design. In the present study, new hybrid artificial neural network (ANN) models are developed for predicting Kt of π-type FBs. Actually, a new algorithm that combines particle swarm optimization (PSO) and Levenberg-Marquardt (LM) is used for learning ANN models. These models are developed by the use of experimental data sets obtained from the Kt of π-type FBs using a wave basin of the University of a Coruña, Spain. A proposed model performance was evaluated and results show that this model can be successfully applied for the prediction of the Kt. Also, results of proposed model show that the efficiency of this model is improved in compare with the introduced formulas cited in the literature. After assuring the acceptability of the prediction results, this model as one of an efficient tool was used for extending the experimental data and selection of the optimal design of π-type FBs in terms of the geometric characteristics.

scholarly journals Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

2021 ◽  
Vol 11 (20) ◽  
pp. 9760
Author(s):  
Zhongkai Huang ◽  
Dongmei Zhang ◽  
Dongming Zhang
Keyword(s):  
Pressure Coefficient ◽  
Earth Pressure ◽  
Numerical Data ◽  
Absolute Error ◽  
Ann Model ◽  
Soil Stiffness ◽  
Wall Deflection ◽  
Proposed Model ◽  
Ann Models ◽  
Artificial Neural Network Ann

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

scholarly journals An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)

Materials ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1282 ◽  
Author(s):  
Zhongman Cai ◽  
Hongchao Ji ◽  
Weichi Pei ◽  
Xuefeng Tang ◽  
Long Xin ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Dynamic Recrystallization ◽  
Microstructure Evolution ◽  
Processing Map ◽  
Resistant Steel ◽  
Ann Model ◽  
Heat Resistant Steel ◽  
Artificial Neural Network Ann

Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( ε ˙ ). For the control of the microstructure, the control of the ε ˙ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

2022 ◽  
pp. 1287-1300
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini
Keyword(s):  
Neural Network ◽  
Crop Production ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Ann Model ◽  
Demand Prediction ◽  
Computational Performance ◽  
Societal Needs ◽  
Ann Models ◽  
Artificial Neural Network Ann

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

2020 ◽  
Vol 12 (4) ◽  
pp. 35-47
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini
Keyword(s):  
Neural Network ◽  
Crop Production ◽  
Demand Forecasting ◽  
Ann Model ◽  
Demand Prediction ◽  
Computational Performance ◽  
Societal Needs ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Agricultural Food

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

scholarly journals Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1303
Author(s):  
Wei-Ting Hung ◽  
Cheng-Hsuan (Sarah) Lu ◽  
Stefano Alessandrini ◽  
Rajesh Kumar ◽  
Chin-An Lin
Keyword(s):  
Machine Learning ◽  
New York ◽  
New York State ◽  
Vertical Mixing ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Joint Analysis ◽  
Ann Model ◽  
York State ◽  
Ann Models

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

2014 ◽  
Vol 1017 ◽  
pp. 166-171
Author(s):  
Bin Zhao ◽  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Surface Roughness ◽  
Cast Iron ◽  
Three Dimensional ◽  
Ann Model ◽  
Linear Speed ◽  
Functional Parameters ◽  
Grinding Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Dimensional Surface

Three-dimensional surface roughness parameters are widely applied to characterize frictional and lubricating properties, corrosion resistance, fatigue strength of surfaces. Among them, the functional parameters of surface roughness, such as Sbi, Sci, and Svi, are used to evaluate bearing and fluid retention properties of surfaces. In this study, the effects of grinding parameters, including wheel linear speed (Vs), workpiece linear speed (Vw), grinding depth (ap), longitudinal feed rate (fa), and dressing rate (F), on functional parameters were studied in grinding of cast iron. An artificial neural network (ANN) model was developed for predicting the functional parameters of three-dimensional surface roughness. The inputs of the ANN models were grinding parameters (Vs, Vw, ap, fa, F), and the output parameters of the models were functional parameters of surface roughness (Sbi, Sci, Svi). With small errors (e.g MSE = 0.09%, 0.61%, and 0.0014%. ), the ANN-based models are considered sufficiently accurate to predict functional parameters of surface roughness in grinding of cast iron.

scholarly journals Neural Network Characterization of Reflectarray Antennas

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Angelo Freni ◽  
Marco Mussetta ◽  
Paola Pirinoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Procedure ◽  
Antenna Design ◽  
Ann Model ◽  
Design And Optimization ◽  
Full Wave ◽  
Proposed Model ◽  
Artificial Neural Network Ann

An efficient artificial neural network (ANN) approach for the modeling of reflectarray elementary components is introduced to improve the numerical efficiency of the different phases of the antenna design and optimization procedure, without loss in accuracy. The comparison between the results of the analysis of the entire reflectarray designed using the simplified ANN model or adopting a full-wave characterization of the unit cell finally proves the effectiveness of the proposed model.

Neural Network MIMO Model for Production of Isopropyl Myristate in a Semibatch Reactive Distillation Column

2013 ◽  
Vol 284-287 ◽  
pp. 403-408
Author(s):  
Nur Alwani Ali Bashah ◽  
Mohd Roslee Othman ◽  
Norashid Aziz
Keyword(s):  
Neural Network ◽  
Reactive Distillation ◽  
Batch Reactor ◽  
Isopropyl Myristate ◽  
Ann Model ◽  
Validation Data ◽  
Testing Data ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Mimo Model

Batch reactive distillation is an integrated unit of batch reactor and distillation. It provides benefits of having higher conversion and yield by continuous removal of side product. The aim of this paper is to develop an artificial neural network (ANN) based model for production of isopropyl myristate in an industrial scaled semibatch reactive distillation. Two cases of the MIMO model were developed. Case 1 does not consider historical data as inputs while case 2 does. The trained ANN for both cases was validated with independent validation data and the best architecture was optimized. Case 1 resulted to 8 inputs, 14 hidden nodes and 2 outputs [8-14-2] ANN while Case 2 resulted to [12-13-2] ANN. The results show that both ANN models have ability to predict the unknown validation and testing data very well. However, the [8-14-2] ANN model produce higher accuracy than [12-13-2] ANN model with MSE of 0.00094 and 0.0013, respectively.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

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