Determination of the Impact Location and Damage Characterization Based on Guided Waves

2016 ◽  
Vol 713 ◽  
pp. 10-13 ◽  
Author(s):  
A. de Luca ◽  
Zahra Sharif Khodaei ◽  
Francesco Caputo
Keyword(s):  
Neural Network ◽  
Guided Waves ◽  
Training Phase ◽  
Damage Mode ◽  
Impact Location ◽  
Damage Characterization ◽  
Artificial Neural Network Ann ◽  
The Impact ◽  
Damage Criteria

The aim of this paper is to understand the effects of the damage criteria modelling on the training phase (performed by means of Finite Element simulations) of an artificial neural network (ANN) enabled to locate impacts onto a CFRP laminate. The developed FE models have been also used to investigate the intra-laminar damage mode, which, among different ones, has the most effects on the residual strength of the panel.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Application of artificial neural network to predict properties of diesel-biodiesel blends

1970 ◽  
Vol 6 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Jatinder Kumar ◽  
Ajay Bansal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Statistical Technique ◽  
Training Algorithms ◽  
Biodiesel Blends ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The experimental determination of various properties of diesel-biodiesel mixtures is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of diesel-biodiesel blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the flash point, fire point, density and viscosity of diesel and biodiesel mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of dieselbiodiesel mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures of diesel and biodiesel. Key words: Biodiesel; Artificial Neural Network; Principle of least squares; Diesel; Linear Regression. DOI: 10.3126/kuset.v6i2.4017Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.98-103

scholarly journals SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

2021 ◽  
Author(s):  
Harish Chandra ◽  
Xianwei Meng ◽  
Arman Margaryan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sources ◽  
Modeling Framework ◽  
Novel Approach ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Model Versus ◽  
Artificial Neural Network Ann ◽  
The Impact

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.

scholarly journals Comparison of partial least squares and artificial neural network chemometric techniques in determination of sulfamethoxazole and trimethoprim in pharmaceutical suspension by ATR–FTIR spectrometry

2011 ◽  
Vol 26 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Khanmohammadi ◽  
N. Dallali ◽  
A. Bagheri Garmarudi ◽  
M. Zarnegar ◽  
K. Ghasemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Parameters ◽  
Modified Method ◽  
Ftir Spectrometry ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Partial Least Square (PLS) and Artificial Neural Network (ANN) techniques were compared during development of an analytical method for quantitative determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in Co-Trimoxazole®suspension. The procedure was based on Attenuated Total Reflectance Fourier Transform Infrared (ATR–FTIR) spectrometry. The 800–2500 cm−1spectral region was selected for quantitative analysis.R2and relative error of prediction (REP) in PLS technique were (0.989, 2.128) and (0.986, 1.381) for SMX and TMP, respectively. These statistical parameters were improved using the ANN models considering the complexity of the sample and the speediness and simplicity of the method.R2and RMSEC in modified method were (0.997, 1.064) and (0.997, 0.634) for SMX and TMP, respectively.

scholarly journals Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

2020 ◽  
Vol 25 (2) ◽  
pp. 145-152
Author(s):  
Yan Kuchin ◽  
Ravil Mukhamediev ◽  
Kirill Yakunin ◽  
Janis Grundspenkis ◽  
Adilkhan Symagulov
Keyword(s):  
Neural Network ◽  
Automatic Classification ◽  
Training Data ◽  
Classification Problems ◽  
Expert Opinions ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

AbstractMachine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influence on the classification. This paper presents a quantitative assessment of the impact of expert opinions on the classification process. In other words, we have prepared the data, identified the experts and performed a series of experiments with and without taking into account the fact that the expert identifier is supplied to the input of the automatic classifier during training and testing. Feedforward artificial neural network (ANN) has been used as a classifier. The results of the experiments show that the “knowledge” of the ANN of which expert interpreted the data improves the quality of the automatic classification in terms of accuracy (by 5 %) and recall (by 20 %). However, due to the fact that the input parameters of the model may depend on each other, the SHapley Additive exPlanations (SHAP) method has been used to further assess the impact of expert identifier. SHAP has allowed assessing the degree of parameter influence. It has revealed that the expert ID is at least two times more influential than any of the other input parameters of the neural network. This circumstance imposes significant restrictions on the application of ANNs to solve the task of lithological classification at the uranium deposits.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Predicting golf ball launch characteristics using iron clubhead presentation variables and the influence of mishits

2021 ◽  
pp. 175433712098785
Author(s):  
Tom W Corke ◽  
Nils F Betzler ◽  
Eric S Wallace ◽  
Steve R Otto
Keyword(s):  
Linear Regression Analysis ◽  
Leading Edge ◽  
Specific Knowledge ◽  
Ball Speed ◽  
Impact Location ◽  
Ball Contact ◽  
The Impact ◽  
Launch Angle ◽  
Vertical Impact

In golf, it is well understood that the interaction between the clubhead and ball determines the initial ball launch conditions. Specific knowledge regarding these relationships for iron clubs, particularly when clubhead and turf interactions are considered, would be both novel and of benefit to practitioners. Linear regression analysis was used to determine relationships between selected clubhead presentation and shot outcome variables for a sample of 1127 ‘5-iron’ shots hit from natural turf by 96 golfers. As expected, clubhead speed was the most significant predictor of ball speed, with obliquity of impact and eccentricity of the impact location making smaller, yet statistically significant contributions. Marginally ‘fat’ strikes, whereby the leading edge of the clubhead was only slightly beneath the ground at impact, appeared to have a lesser effect than expected in terms of ball speed. Effective loft was found to be the strongest predictor of vertical launch angle, whilst clubhead speed and spin loft had the greatest influence in the model for predicting total spin; inclusion of ‘thin’ strikes (i.e. those struck with the leading edge) appeared to create a non-linear element to these models and consequently overestimated the influence of vertical impact location in both cases. These findings suggest that determination of impact location, particularly for instances whereby ball contact is not wholly on the club face, is critical in research concerning irons. Overall, this study makes an original contribution to the understanding of 5-iron shot outcomes based on clubhead presentation characteristics.

scholarly journals Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

2020 ◽  
pp. 46-54
Author(s):  
Pradeep Mishra ◽  
Chellai Fatih ◽  
Deepa Rawat ◽  
Saswati Sahu ◽  
Sagar Anand Pandey ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Fuzzy Time Series ◽  
Short Term ◽  
Indian Health ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

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