Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters; then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of ∼0.21 and ∼0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.

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A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽

10.3390/s20072058 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2058 ◽

Cited By ~ 2

Author(s):

Salaheldin Elkatatny ◽

Ahmed Al-AbdulJabbar ◽

Khaled Abdelgawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mutual Effect ◽

Percentage Error ◽

Drilling Process ◽

Ann Model ◽

Data Set ◽

Individual Parameter ◽

Rate Of Penetration ◽

Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

Dhaka University Journal of Science ◽

10.3329/dujs.v68i2.54612 ◽

2020 ◽

Vol 68 (2) ◽

pp. 143-147

Author(s):

Abira Sultana ◽

Murshida Khanam

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Series Data ◽

Arima Model ◽

Rice Production ◽

Series Data ◽

Ann Model ◽

Neural Network Models ◽

Artificial Neural ◽

Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

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Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production

Processes ◽

10.3390/pr9081444 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1444

Author(s):

Saeed Na’amnh ◽

Muath Bani Salim ◽

István Husti ◽

Miklós Daróczi

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Artificial Neural Network ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Classical Method ◽

Fuzzy Model ◽

Ann Model ◽

Inference System ◽

Artificial Neural

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.

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

BALTIC FORESTRY ◽

10.46490/bf422 ◽

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.

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Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Journal of Food Quality ◽

10.1155/2021/7739732 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Priyanka Dhurve ◽

Ayon Tarafdar ◽

Vinkel Kumar Arora

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fluidized Bed ◽

Drying Kinetics ◽

Ann Model ◽

Neural Network Models ◽

Drying Behavior ◽

Pumpkin Seeds ◽

Artificial Neural ◽

Semiempirical Models

Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.

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Prediction of rubber vulcanization using an artificial neural network

Hemijska industrija ◽

10.2298/hemind210511026l ◽

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.

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Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Water ◽

10.3390/w11081594 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1594 ◽

Cited By ~ 1

Author(s):

Piotr Ofman ◽

Joanna Struk-Sokołowska

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Batch Reactor ◽

Nitrogen Concentration ◽

Nitrogen Form ◽

Ann Model ◽

Nitrogen Forms ◽

Neural Network Models ◽

Ann Models ◽

Artificial Neural

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.

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Daily Peak Load Forecast Using Artificial Neural Network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2256-2263 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2256

Author(s):

Ramesh Kumar V ◽

Pradipkumar Dixit

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Learning Algorithm ◽

Peak Load ◽

Load Forecasting ◽

Percentage Error ◽

Ann Model ◽

Short Term ◽

Short Term Load Forecasting ◽

Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

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Operational Dst index prediction model based on combination of artificial neural network and empirical model

Journal of Space Weather and Space Climate ◽

10.1051/swsc/2021021 ◽

2021 ◽

Author(s):

Wooyeon Park ◽

Jaejin Lee ◽

Kyung-Chan Kim ◽

JongKil Lee ◽

Keunchan Park ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Space Weather ◽

Large Scale ◽

Geomagnetic Storms ◽

Ann Model ◽

Neural Network Models ◽

Dst Index ◽

Artificial Neural

<p class="Abstract" style="margin: 6pt 0cm 0.0001pt; font-size: 12pt; font-family: 굴림, sans-serif; color: rgb(0, 0, 0); text-align: justify; text-indent: 36pt;"><span lang="EN-US" style="font-family: "Times New Roman", serif;">In this paper, an operational Dst index prediction model is developed by combining empirical and artificial neural network models. Artificial neural network algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, ACE and DSCOVR mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to Artificial Neural Network (ANN) model only. This model could be used practically for space weather operation by extending prediction time to 24 hours and updating the model output every hour.<o:p></o:p></span></p>

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Artificial neural network (ANN) approach for modeling of selected biogenic compounds in a mixture of treated municipal and dairy wastewater

E3S Web of Conferences ◽

10.1051/e3sconf/201910000077 ◽

2019 ◽

Vol 100 ◽

pp. 00077

Author(s):

Joanna Struk-Sokołowska ◽

Piotr Ofman ◽

Sevgi Demirel

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wastewater Treatment ◽

Wastewater Treatment Plant ◽

Extreme Values ◽

Treatment Plant ◽

Dairy Wastewater ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

This paper presents artificial neural network (ANN) model of wastewater treatment plant, which was used for average monthly concentrations of N-NH4+, N-NO3-, N-NO2-, total Kiejdahl nitrogen (TKN), PO43- and SO42- approximation. ANN model was developed for wastewater treatment plant located in Bystre, Poland which treats municipal wastewater with a share of dairy wastewater. The object was chosen because of the unique location, in the Great Mazury Lakes area and the need for its special environmental protection. Input layer of developed ANN model consisted of BOD, COD, concentrations of total nitrogen and total phosphorus, total organic carbon, sulphates, wastewater temperature and pH., The developed model reflected extreme values observed during study period. Average error percentage with which output variables were approximated equalled to 35.35%; 8.99%; 21.23%; 5.08%; 10.99%; 3.02% respectively for N-NH4+, N-NO3-, N-NO2-, TKN, PO43- and SO42-.

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