The usage of artificial neural network as post processing algorithm in digital holography

Kaya, Gulhan Ustabas; Saraç, Zehra

doi:10.1109/siu.2018.8404668

Cited by 3 publications

(2 citation statements)

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“…One of the practical features of the ANN is that it enables linear or nonlinear modeling of the ANN, thanks to the preferred activation function for the artificial nerve cells that consist of the ANN. Another feature of ANN is that it reveals the hidden relationships in the data structure by using the data related to the problem [44,45].…”

Section: Ann-based Predictive Methodologymentioning

confidence: 99%

Measurement of the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO using the Artificial Neural Network method

Erdogan,

Kocar,

Topal

2023

Scientia Iranica

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Nanofluids are strong candidates as heat carriers due to their excellent thermophysical properties. Among these thermophysical properties, viscosity is critical in heat transfer and pressure loss calculations. In this study, three different water-based nanofluids, Al 2 O 3 , TiO 2 , and ZnO, were prepared with volumetric concentrations ranging from 0.1% to 1%. The dynamic viscosities of these nanofluids were experimentally measured within a temperature range of 20 °C to 50 °C.Artificial neural networks (ANN) were employed to predict the results based on the experimental data. Two different approaches were applied in the implementation of the ANN method. The first approach involved creating three separate ANN models, each dedicated to predicting the viscosities of the three different nanofluids. The second approach used a single generalized ANN to predict the viscosities of all nanofluids. The results were evaluated using the criteria of Rsquared (R 2 ) and root mean square error (RMSE) values. In all models, R 2 values exceeded 99%, while the RMSE values were calculated for the Al 2 O 3 , TiO 2 , and ZnO nanofluid ANN models and the generalized ANN model to be 0.40%, 0.30%, 0.04%, and 0.28% respectively. These results demonstrate that a nanofluid's viscosity can be effectively predicted individually and multiple nanofluids using an ANN model.

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Section: Ann-based Predictive Methodologymentioning

confidence: 99%

Measurement of the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO using the Artificial Neural Network method

Erdogan,

Kocar,

Topal

2023

Scientia Iranica

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“…One of the practical features of ANN is that it allows linear or nonlinear modeling of ANN due to the preferred activation function for artificial nerve cells that make up ANN. It also reveals hidden relationships in the data structure using ANN input data [30].…”

Section: Ann Model Architecture Selectionmentioning

confidence: 99%

Estimating the crashworthiness performances of Collision Boxes Using ANN

Adanur

2023

Preprint

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Studies on the development of energy absorbing systems that minimize vehicle chassis damage in traffic accidents are increasing day by day. Many designs have been made in the studies on crash boxes used to absorb the energy released in the event of an accident. The specific energy absorption/ energy/FP crashworthiness values used to determine the efficiency of the designed crash boxes in this study were estimated using the Artifical Neural Network (ANN). The input layer of the ANN model consists of materials (DP600, DP800 and DP1000), thickness of materials (between 0.8 and 2.2 mm) and initial speed (10m/s, 12m/s and 16 m/s). In the ANN model, 42 different models were created by changing the different training function (training, trainlm and trainrp), transfer function (tansig and logsig) and the number of neurons in the hidden layer (between 9 and 33). R2 and RSME methods were used to evaluate the efficiency of ANN models. The training function was found to be highly accurate (R2: 0.99999 and RSME: 0.314727E-05) when the training function was “Trainlm” and the number of neurons in the hidden layer was 33. The training and testing results of the ANN model show that ANN can be used to estimate the specific energy absorption/energy/PCH value of crash boxes.

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Estimating the crashworthiness performances of crushboxes using artificial neural network

Koçar,

Adanur,

Varol

et al. 2024

Materialwissenschaft Werkst

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Studies on the development of energy absorbing systems that minimize vehicle chassis damage in traffic accidents are increasing day by day. Many designs have been made in the studies on crushboxes used to absorb the energy released in the event of an accident. These design works are quite costly and take a long time. In this study, to design crushboxes faster and more economically was estimated using artificial neural network. The input layer of the artificial neural network model consists of three different materials, thicknesses (between 0.8 and 2.2 mm) and three different initial speeds. In the artificial neural network model, 42 different models were created by changing the different training functions (training, trainlm and trainrp), transfer functions (tansig and logsig) and the number of neurons in the hidden layer (between 9 and 33). R2 and root mean square error (RMSE) methods were used to evaluate the efficiency of artificial neural network models. The training function was found to be highly accurate (R2: 0.99999 and root mean square error: 0.314727E‐05) when the training function was “trainlm” and the number of neurons in the hidden layer was 33. The training and testing results of the artificial neural network model show that artificial neural networks can be used to estimate the specific energy absorption/energy/peak crush force value of crushboxes.

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The usage of artificial neural network as post processing algorithm in digital holography

Cited by 3 publications

References 0 publications

Measurement of the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO using the Artificial Neural Network method

Measurement of the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO using the Artificial Neural Network method

Estimating the crashworthiness performances of Collision Boxes Using ANN

Estimating the crashworthiness performances of crushboxes using artificial neural network

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