Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

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.

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High-accuracy wire electrical discharge machining using artificial neural networks and optimization techniques

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2017.05.010 ◽

2018 ◽

Vol 49 ◽

pp. 24-38 ◽

Cited By ~ 25

Author(s):

A. Conde ◽

A. Arriandiaga ◽

J.A. Sanchez ◽

E. Portillo ◽

S. Plaza ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Electrical Discharge ◽

Electrical Discharge Machining ◽

High Accuracy ◽

Optimization Techniques ◽

Wire Electrical Discharge Machining ◽

Artificial Neural

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

10.26434/chemrxiv.12250403.v1 ◽

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 ab initio 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 ab initio 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 Li10GeP2S12 (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.

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Discrimination of quarry blasts from earthquakes using artificial neural networks

10.5194/egusphere-egu21-1285 ◽

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

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.

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Artificial neural networks (ANN) in predicting atrophic body gastritis (ABG): evidence for an high accuracy

Digestive and Liver Disease ◽

10.1016/s1590-8658(01)80162-6 ◽

2001 ◽

Vol 33 ◽

pp. A1

Author(s):

Enzo Grossi ◽

Massimo Buscema ◽

Edith Lanher ◽

Marco Intraligi ◽

Giulia Massini ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

High Accuracy ◽

Artificial Neural

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

Fibers ◽

10.3390/fib9120078 ◽

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.

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Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

Materials ◽

10.3390/ma14102570 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2570

Author(s):

Tomasz Trzepieciński ◽

Marcin Szpunar ◽

Ľuboš Kaščák

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Variable Load ◽

Training Methods ◽

Backward Elimination ◽

Drawing Test ◽

Strip Drawing ◽

Rms Error ◽

Artificial Neural ◽

Quasi Newton

This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R2 = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R2 = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

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ARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT

Revista Árvore ◽

10.1590/0100-67622016000300018 ◽

2016 ◽

Vol 40 (3) ◽

pp. 543-549

Author(s):

Antônio José Vinha Zanuncio ◽

Amélia Guimarães Carvalho ◽

Liniker Fernandes da Silva ◽

Angélica de Cássia Oliveira Carneiro ◽

Jorge Luiz Colodette

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Moisture Content ◽

Equilibrium Moisture Content ◽

Basic Density ◽

High Accuracy ◽

Wood Moisture Content ◽

Wood Moisture ◽

The Neural Network ◽

Artificial Neural

ABSTRACT Drying of wood is necessary for its use and moisture control is important during this process. The aim of this study was to use artificial neural networks to evaluate and monitor the wood moisture content during drying. Wood samples of 2 × 2 × 4 cm were taken at 1.3 m above the ground, outside of radial direction, from seven 2-year-old materials and three 7-year-old materials. These samples were saturated and drying was evaluated until the equilibrium moisture content, then, the artificial neural networks were created. The materials with higher initial moisture reached equilibrium moisture content faster due to its higher drying rate. The basic density of all wood materials was inversely proportional at the beginning and directly proportional to the moisture at the end of drying. All artificial neural networks used in this work showed high accuracy to estimate the moisture, however, the neural network based on the basic density and drying days was the best. Therefore, artificial neural networks can be used to control the moisture content of wood during drying.

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Simulation of the Wind Speed at Different Heights Using Artificial Neural Networks

Wind Engineering ◽

10.1260/0309524001495503 ◽

2000 ◽

Vol 24 (2) ◽

pp. 127-136 ◽

Cited By ~ 42

Author(s):

D.A. Bechrakis ◽

P.D. Sparis

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Wind Shear ◽

Surface Friction ◽

High Accuracy ◽

Energy Output ◽

Data Sets ◽

Artificial Neural ◽

Speed Simulation

In this paper, an estimation of the wind speed at different heights with artificial neural networks is presented. It is an alternative way to compute wind shear. This method was tested using pairs of data sets from two measuring stations, installed in different topographic locations. Wind speed simulation is performed with high accuracy. The calculation of the surface friction coefficient from the actual measurements is also compared for wind shear estimation with the typical method in terms of energy output. Results showed that artificial neural networks achieve a better wind speed simulation and wind power estimation at different heights, even in complex terrains.

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