scholarly journals Design of frequency selective surface comprising of dipoles using artificial neural network

2020 ◽  
Vol 9 (4) ◽  
pp. 276
Author(s):  
Monojit Rudra ◽  
P Soni Reddy ◽  
Rajatsubhra Chakraborty ◽  
Partha Pratim Sarkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulated Data ◽  
Frequency Selective Surface ◽  
Simulation Software ◽  
Computational Time ◽  
Ann Models ◽  
Artificial Neural ◽  
Frequency Selective ◽  
Artificial Neural Network Ann

<span>This paper depicts the design of Frequency Selective Surface (FSS) comprising of dipoles using Artificial Neural Network (ANN). It has been observed that with the change of the dimensions and periodicity of FSS, the resonating frequency of the FSS changes. This change in resonating frequency has been studied and investigated using simulation software. The simulated data were used to train the proposed ANN models. The trained ANN models are found to predict the FSS characteristics precisely with negligible error. Compared to traditional EM simulation softwares (like ANSOFT Designer), the proposed technique using ANN models is found to significantly reduce the FSS design complexity and computational time. The FSS simulations were made using ANSOFT Designer v2 software and the neural network was designed using MATLAB software.</span>

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

A Prediction Model of Deflection of Fixing Thin Parts

2012 ◽  
Vol 217-219 ◽  
pp. 1526-1529
Author(s):  
Yu Mei Liu ◽  
Wen Ping Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Simulated Data ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

scholarly journals Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Pei-Fang (Jennifer) Tsai ◽  
Po-Chia Chen ◽  
Yen-You Chen ◽  
Hao-Yuan Song ◽  
Hsiu-Mei Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coronary Atherosclerosis ◽  
Inpatient Care ◽  
Length Of Hospital Stay ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Christian Hospital

For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

2014 ◽  
Vol 902 ◽  
pp. 431-436 ◽  
Author(s):  
A. Shahpanah ◽  
S. Poursafary ◽  
S. Shariatmadari ◽  
A. Gholamkhasi ◽  
S.M. Zahraee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Waiting Time ◽  
Container Terminal ◽  
Hybrid Approach ◽  
Simulation Software ◽  
Optimum Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

scholarly journals A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Agus Saptoro ◽  
Moses O. Tadé ◽  
Hari Vuthaluru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Data Representation ◽  
Data Splitting ◽  
Neural Network Models ◽  
Data Division ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.

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