Two models to estimate the density of organic cocrystals

Drying is crucial in the quality preservation of food materials. Physics-based models are effective tools to optimally control the drying process. However, these models require accurate thermo-physical properties; unavailability or uncertainty in the values of these properties increases the possibility of error. Property estimation methods are not standardized, and usually involve the use of many instruments and are time-consuming. In this work, we have developed an experimentally validated deep learning-based artificial neural network model that estimates sensitive input parameters of food materials using temperature and moisture data from a set of simple experiments. This model predicts input parameters with error less than 1%. Further, using input parameters, physics-based model predicts temperature and moisture to within 5% accuracy of experiments. The proposed work when interfaced with food machinery could play a significant role in process optimization in food processing industries.

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Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2013.12.008 ◽

2014 ◽

Vol 31 ◽

pp. 509-519 ◽

Cited By ~ 127

Author(s):

Amit Kumar Yadav ◽

Hasmat Malik ◽

S.S. Chandel

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Prediction Models ◽

Input Parameters ◽

Artificial Neural ◽

Selection Of

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A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Sustainability ◽

10.3390/su12104001 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4001

Author(s):

Sung-Sik Park ◽

Peter D. Ogunjinmi ◽

Seung-Wook Woo ◽

Dong-Eun Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Model Equation ◽

Soil Layer ◽

Unit Weight ◽

Ann Model ◽

Matrix Formulation ◽

Input Parameters ◽

Artificial Neural

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

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A stereological correction method employing an artificial neural network for mineral liberation assessment of ore particles

Minerals Engineering ◽

10.1016/j.mineng.2020.106578 ◽

2020 ◽

Vol 157 ◽

pp. 106578

Author(s):

Takao Ueda ◽

Tatsuya Oki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Correction Method ◽

Mineral Liberation ◽

Artificial Neural

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Influence of Input Parameters on the Performance of an Artificial Neural Network Used to Detect Structural Damage

10.1063/1.3497898 ◽

2010 ◽

Author(s):

Jesus Daniel Villalba ◽

Ivan Dario Gomez ◽

Jose Elias Laier ◽

Theodore E. Simos ◽

George Psihoyios ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Structural Damage ◽

Input Parameters ◽

Artificial Neural

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Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network

Energies ◽

10.3390/en12050790 ◽

2019 ◽

Vol 12 (5) ◽

pp. 790 ◽

Cited By ~ 1

Author(s):

Matej Žnidarec ◽

Zvonimir Klaić ◽

Damir Šljivac ◽

Boris Dumnić

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Quality ◽

Prediction Models ◽

Harmonic Distortion ◽

Pv System ◽

Current Harmonics ◽

Pv Systems ◽

Input Parameters ◽

Artificial Neural

Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

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A comparative study of concrete strength prediction using artificial neural network, multigene programming and model tree

Challenge Journal of Structural Mechanics ◽

10.20528/cjsmec.2019.02.002 ◽

2019 ◽

Vol 5 (2) ◽

pp. 42

Author(s):

Preeti Kulkarni ◽

Shreenivas N. Londhe ◽

Pradnya R. Dixit

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Concrete Strength ◽

Model Performance ◽

Recycled Aggregate Concrete ◽

Recycled Aggregate ◽

Model Tree ◽

Multigene Genetic Programming ◽

Input Parameters ◽

Artificial Neural

In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance.

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Bias Correction Method Based on Artificial Neural Networks for Quantitative Precipitation Forecast

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10356 ◽

2021 ◽

Vol 8 (1) ◽

pp. 38

Author(s):

Adrián Fuentes-Barrios ◽

Maibys Sierra-Lorenzo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Bias Correction ◽

Artificial Neural Network Model ◽

Correction Method ◽

Precipitation Forecast ◽

Quantitative Precipitation Forecast ◽

Artificial Neural

The nowcasting and very short-term prediction system (SisPI, for its acronym in Spanish) is among the tools used by the National Meteorological Service of Cuba for the quantitative precipitation forecast (QPF). SisPI uses the WRF model as the core of its forecasts and one of the challenges to overcome is to improve the precision of the QPF. With this purpose, in this work we present the results of the application of a bias correction method based on artificial neural networks. The method is applied to the highest-resolution domain of SisPI (3 km), and the correction is made from the precipitation estimation of the GPM satellite product. Results shows higher correlation with the artificial neural network model in relation to the values predicted by SisPI (0.76 and 0.34, respectively). The mean square error when applying the artificial neural network model is 3.69, improving the performance of SisPI by 6.78. In general, the bias correction has a good ability to correct the precipitation forecast provided by SisPI, being less evident in cases where precipitation is reported and SisPI is not capable of forecasting it. In cases of overestimation by SisPI (which happens quite frequently), the correction achieves the best results.

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Improving the Mechanical Properties of Wire-Rope Silk Scaffold by Artificial Neural Network in Tendon and Ligament Tissue Engineering

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501501000303 ◽

2015 ◽

Vol 10 (3) ◽

pp. 155892501501000 ◽

Cited By ~ 1

Author(s):

Elham Naghashzargar ◽

Dariush Semnani ◽

Saeed Karbasi

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Back Propagation ◽

Wire Rope ◽

Elongation At Break ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Silk Yarn

Finding an appropriate model to assess and evaluate mechanical properties in tissue engineered scaffolds is a challenging issue. In this research, a structurally based model was applied to analyze the mechanics of engineered tendon and ligament. Major attempts were made to find the optimum mechanical properties of silk wire-rope scaffold by using the back propagation artificial neural network (ANN) method. Different samples of wire-rope scaffolds were fabricated according to Taguchi experimental design. The number of filaments and twist in each layer of the four layered wire-rope silk yarn were considered as the input parameters in the model. The output parameters included the mechanical properties which consisted of UTS, elongation at break, and stiffness. Finally, sensitivity analysis on input data showed that the number of filaments and the number of twists in the fourth layer are less important than other input parameters.

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Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network

Nanotechnology ◽

10.1088/1361-6528/ac3688 ◽

2021 ◽

Author(s):

Chuan-Yong Zhu ◽

Zhi-Yang He ◽

Mu Du ◽

Liang Gong ◽

Xinyu Wang

Keyword(s):

Neural Network ◽

Thermal Conductivity ◽

Artificial Neural Network ◽

Water Content ◽

Effective Thermal Conductivity ◽

Pearson Correlation ◽

Complex Structure ◽

Ann Model ◽

Input Parameters ◽

Artificial Neural

Abstract The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils’ complex structure and components. In the present study, the influences of different parameters, including silt content (m si), sand content (m sa), clay content (m cl), quartz content (m qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters of m si, m sa, m cl, and m qu have a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy and R 2=0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.

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