scholarly journals Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs)

Fibers ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 78
Author(s):  
Chiara Turco ◽  
Marco Francesco Funari ◽  
Elisabete Teixeira ◽  
Ricardo Mateus
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Coefficients ◽  
Natural Fibre ◽  
High Accuracy ◽  
Numerical Investigations ◽  
Compressed Earth Blocks ◽  
Earth Blocks ◽  
R Value ◽  
Artificial Neural

The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the available literature. Data relating to 332 specimens (Database 1) were used for the prediction of the compressive strength (ANN1), and, due to the lack of some information, those relating to 130 specimens (Database 2) were used for the prediction of the tensile strength (ANN2). The developed tools showed high accuracy, i.e., correlation coefficients (R-value) equal to 0.97 for ANN1 and 0.91 for ANN2. Such promising results prompt their applicability for the design and orientation of experimental campaigns and support numerical investigations.

scholarly journals Characterization of a Mobile Waste-Robot: a Heuristic Method to Path Planning using Artificial Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3902-3910
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Planning ◽  
Mobile Robotics ◽  
Heuristic Method ◽  
Correlation Coefficients ◽  
Path Tracking ◽  
Ann Model ◽  
Artificial Neural ◽  
Tracking Time

In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

Winter surface air temperature prediction over Japan using artificial neural networks

2021 ◽  
Author(s):  
J. V. Ratnam ◽  
Masami Nonaka ◽  
Swadhin K. Behera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Anomaly Correlation ◽  
The North ◽  
Skill Scores ◽  
Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

scholarly journals Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

2020 ◽  
Author(s):  
Karun Kumar Rao ◽  
Yan Yao ◽  
Lars Grabow
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Artificial Neural Networks ◽  
Solid State ◽  
Ab Initio ◽  
Density Functional ◽  
Lithium Ion ◽  
Md Simulations ◽  
High Accuracy ◽  
Artificial Neural

There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on <i>ab initio</i> molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations, to eliminate the need for costly <i>ab initio </i>force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> (LGPS) when benchmarking the ANN-MD results with DFT-MD. To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy.

scholarly journals Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5542
Author(s):  
Alejandro Grande-Fidalgo ◽  
Javier Calpe ◽  
Mónica Redón ◽  
Carlos Millán-Navarro ◽  
Emilio Soria-Olivas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reconstruction Algorithm ◽  
High Accuracy ◽  
Recording Device ◽  
Lead System ◽  
Ambulatory Ecg ◽  
Rms Error ◽  
Artificial Neural ◽  
Electrocardiogram Ecg

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

Discrimination of quarry blasts from earthquakes using artificial neural networks

2021 ◽  
Author(s):  
Deniz Ertuncay ◽  
Andrea De Lorenzo ◽  
Giovanni Costa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Characteristics ◽  
High Accuracy ◽  
Anthropogenic Sources ◽  
North East ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Seismic Networks ◽  
Intensive Work

&lt;p&gt;Seismic networks record vibrations that are captured by their stations. These vibrations can be coming from various sources, such as tectonic tremors, quarry blasts and anthropogenic sources. Separation of earthquakes from other sources may require human intervention and it can be a labor-intensive work. In case of lack of such a separation, seismic hazard may be miscalculated. Our goal is to discriminate earthquakes from quarry blasts by using artificial neural networks. To do that, we used two different databases for earthquake signals and quarry blasts. Neither of them have data from our study of interest, which is North-East of Italy. We used three channel signals from the stations as an input for the neural networks. Signals are used as both time series and their spectral characteristics and are fed to the neural networks with this information. We then separated earthquakes from quarry blasts in North-East Italy by using our algorithm. We conclude that our algorithm is able to discriminate earthquakes from quarry blasts with high accuracy and the database can be used in different regions with different earthquake and quarry blast sources in a large variety of distances.&lt;/p&gt;

Evaluation Compressive Strength of Cement-Limestone-Slag Ternary Blended Concrete Using Artificial Neural Networks (ANN) and Gene Expression Programming (GEP)

2020 ◽  
Vol 837 ◽  
pp. 119-124
Author(s):  
Xiao Yong Wang
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gene Expression Programming ◽  
Concrete Strength ◽  
Correlation Coefficients ◽  
Strength Development ◽  
Ann Model ◽  
Artificial Neural ◽  
Ternary Blended Concrete

Limestone and slag blended concrete is an innovative concrete which belongs to the family of limestone calcined clay cement (LC3) concrete. Strength is an important property of structural concrete. This study shows artificial neural networks (ANN) and gene expression programming (GEP) models for predicting strength development of limestone and slag blended concrete. ANN model consists of an input layer, a hidden layer, and output layer. GEP model consists of the sum of three expression trees. The input parameters of ANN and GEP models are mixtures and ages. The output parameter is a strength. The correlation coefficients of ANN and GEP model are 0.99 and 0.98, respectively. Both ANN and GEP model can produce prediction results of the strength of ternary blended concrete reliably.

Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks

2019 ◽  
Vol 14 (4) ◽  
Author(s):  
Ana Carolina Moreno Pássaro ◽  
Tainá Maia Mozetic ◽  
Jones Erni Schmitz ◽  
Ivanildo José da Silva ◽  
Tiago Dias Martins ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adsorption Capacity ◽  
Protein Interactions ◽  
Nonlinear Models ◽  
Correlation Coefficients ◽  
Sodium Phosphate Buffer ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Hidden Neurons

Abstract This work aimed to evaluate the interaction of human IgG in non-conventional adsorbents based on chitosan and alginate in the absence and presence of Reactive Green, Reactive Blue and Cibacron Blue immobilized as ligands. The adsorption was evaluated at 277, 288, 298 and 310 K using sodium phosphate buffer, pH 7.6, at 25 mmol L−1. The highest adsorption capacity was observed in the experiments performed with no immobilized dye, although all showed adsorption capacity higher than 120 mg g−1. Data modeling was done using Langmuir, Langmuir-Freundlich and Temkin classical nonlinear models, and artificial neural networks (ANN) for comparison. According to the parameters obtained, a possible adsorption in multilayers was observed due to protein-adsorbent and protein-protein interactions, concluding that IgG adsorption process is favorable and spontaneous. Using an ANN structure with 3 hidden neurons (single hidden layer), the MSE (RMSE) for training, test and validation were 13.698 (3.701), 11.206 (3.347) and 7.632 (2.763), respectively, achieving correlation coefficients of 0.999 in all steps. ANN modeling proved to be effective in predicting the adsorption isotherms in addition to overcoming the difficulties caused by experimental errors and/or arising from adsorption phenomenology.

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