scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Norhisham Bakhary ◽  
Khairulzan Yahya ◽  
Chin Nam Ng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Demand ◽  
Percentage Error ◽  
Potential Applications ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

scholarly journals The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Şükrü Özşahin ◽  
Hilal Singer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Densified Wood ◽  
Percentage Error ◽  
Angle Of Incidence ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

scholarly journals Artificial Neural Network for Classifying Injected Materials under Ultrasonography

2021 ◽  
Vol 3 (1) ◽  
pp. 0210101
Author(s):  
Galuh Retno Utari ◽  
Giner Maslebu ◽  
Suryasatriya Trihandaru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Body ◽  
Data Augmentation ◽  
Test Dataset ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Human Eyes

We have constructed an artificial neural network (ANN) architecture to classify four different classes of ultrasonography recorded from a jelly box phantom  that was injected by iron, glass, or plastic marble, or without any injection. This jelly box was made as a phantom of a human body, and the injected materials were the cancers. The small size of the injected materials caused only  little disturbances those could not easily distinguished by human eyes. Therefore, ANN was used for classifying the different kind of the injected materials. The number of original images  taken from ultrasonographs were not so many, therefore we did data augmentation for providing large enough dataset that fed into ANN. The data augmentation was constructed by pixel shifting in horizontal and vertical directions. The procedure proposed here produced 98.2% accuracy for predicting test dataset,  though the result was sensitive to the choice of augmentation area.

scholarly journals Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system

2020 ◽  
Vol 9 (3) ◽  
pp. 480
Author(s):  
Siti Nasuha Zubir ◽  
S. Sarifah Radiah Shariff ◽  
Siti Meriam Zahari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Error Measures ◽  
Train Services ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<span lang="EN-US">Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.</span>

Application of Artificial Neural Network (ANN) for Predicting the Wear Behaviour of Al 2219-SiCp Composite

2016 ◽  
Vol 852 ◽  
pp. 397-401
Author(s):  
Ganesh Radhakrishnan ◽  
Chandrasekaran Kesavan ◽  
Vignesh Ramesh ◽  
Tamilarasan Anandan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wear Behaviour ◽  
Percentage Error ◽  
Good Prediction ◽  
Wear Characteristics ◽  
Feed Forward Back Propagation ◽  
Good Prediction Accuracy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, artificial neural network (ANN) model was developed to predict the wear characteristics of Al 2219-SiCp composite. In the development of predictive model, weight fraction of the reinforcement, sintering temperature, applied normal load on the pin and disc speed were considered as model variables. Full factorial experiments were carried out and observed wear characteristics were taken as input for the model. A feed forward back propagation hierarchical neural network was considered in the study. Out of 81 datasets, 49 sets of data were used for training, 16 sets of data for validation and 16 sets of data for testing. The results exhibit good prediction accuracy of about 85% on average of all wear characteristics and percentage error was within the acceptable limits.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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