Search results

1

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

100%

Chudzik S., Minkina W.

Metrology and Measurement Systems

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2011

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issue 2

2

Prediction of the unit cell edge length of cubic A22+BB′O6 perovskites by multiple linear regression and artificial neural networks

100%

Dimitrovska S., Aleksovska S., Kuzmanovski I.

Open Chemistry

|

2005

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vol. 3

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issue 1

198-215

EN

The unit cell edge length, a, of a set of complex cubic perovskites having the general formula A22+BB′O6 is predicted using two methodologies: multiple linear regression and artificial neural neworks. The unit cell edge length is expressed as a function of six independent variables: the effective ionic radii of the constituents (A, B and B′), the electronegativities of B and B′, and the oxidation state of B. In this analysis, 147 perovskites of the A22+BB′O6 type, having the cubic structure and belonging to the Fm3m space group, are included. They are divided in two sets; 98 compounds are used in the calibration set and 49 are used in the test set. Both models give consistent results and could be successfully use to predict the lattice cell parameter of new members of this series.

3

Application of artificial neural nerworks in oxidation kinetic analysis of nanocomposites

100%

Straszko J., Biedunkiewicz A., Strzelczak A.

Polish Journal of Chemical Technology

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2008

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vol. 10

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issue 3

21-28

EN

This study concerns the application of artificial neural networks in oxidation kinetic analysis of ceramic nanocomposites. The oxidation of the Ti-Si-C ceramic nanocomposite in dry air was studied. The size of the nanoparticles was determined by scanning electron microscopy (SEM). The gaseous oxidation products were analysed by mass spectroscopy (MS) while the solid oxidation products by X-ray diffraction (XRD). The kinetic analysis of the oxidation was based on the Coats-Redfern equation. The kinetic models were identified for the consecutive stages and then the A and E parameters of the Arrhenius equations were evaluated. Artificial neural networks were used at each step of the kinetic calculations.

4

THE USE OF ARTIFICIAL NEURAL NETWORKS IN SUPPORTING THE ANNUAL TRAINING IN 400 METER HURDLES

100%

Iskra J., Przednowek K., Wiktorowicz K., Krzeszowski T.

Central European Journal of Sport Sciences and Medicine

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2017

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vol. 17

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issue 1

15-24

EN

This paper presents an evaluation of the annual cycle for 400 m hurdles using artificial neural networks. The analysis included 21 Polish national team hurdlers. In planning the annual cycle, 27 variables were used, where 5 variables describe the competitor and 22 variables represent the training loads. In the presented solution, the task of generating training loads for the assumed result were considered. The neural models were evaluated by cross-validation method. The smallest error was obtained for the radial basis function network with nine neurons in the hidden layer. The performed analysis shows that at each phase of training the structure of training loads is different.

5

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

100%

Wit A., Czaplicki A.

Human Movement

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2008

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vol. 9

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issue 2

93-102

EN

The aim of this study is to review the achievements of the mathematical modeling of muscles force contribution during walking. In order to determine the contributions of individual muscles to the net force or net muscle torque at a given joint, the external forces acting on lower extremity joints during gait ought to be identified. The solution of this problem, called in biomechanics the inverse dynamics, is now regarded as a classical method of movement modelling. In the hypothesis put forward in this paper, it is considered if the artificial neural network method could be applied to the muscle contraction prediction during gait analysis in normal and disabled subjects. Artificial neural network (ANN) is an artificial intelligence method used in mathematical modelling and its applications in diverse areas, especially in biology and medicine, are steadily progressing. The achievements and possibilities of ANN in biomechanics were presented previously by others authors. For example, Liu and Lockhart [13] attempted at creating a network capable of reproducing muscle forces during gait from EMG signals recorded in working muscles. The objective of our study was to make use of the experience gained in the construction of ANN and to apply advanced mathematical procedures to identical experimental conditions of gaint analysis.

6

The Influence of Changes in Active Binder Content on the Control System of the Moulding Sand Quality

100%

Dobosz S. M., Jakubski J., Major-Gabryś K.

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issue 4

7

Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

100%

Coral R., Flesch C. A., Penz C. A., Borges M. R.

Metrology and Measurement Systems

|

2015

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vol. 22

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issue 1

8

Coronary Artery Diagnosis Aided by Neural Network

88%

Stefko K.

Polish Journal of Medical Physics And Engineering

|

2007

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vol. 13

|

issue 3

149-155

EN

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.

9

Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

88%

Garbaa H., Romanowski A., Jackowska-Strumiłło L., Grudzień K.

Archives of Electrical Engineering

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2016

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vol. 65

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issue 4

10

Automatic Fault Classification for Journal Bearings Using ANN and DNN

88%

Narendiranath Babu T., Aravind A., Rakesh A., Jahzan M., Rama Prabha D., Ramalinga Viswanathan M.

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issue 4

11

Prediction of quality parameters of a dry air separation product using machine learning methods

88%

Żogała A., Rzychoń M.

Gospodarka Surowcami Mineralnymi

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2019

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vol. 35

|

issue 2

12

Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

88%

Rakesh A., Aravind A., Narendiranath B. T., Jahzan M., Rama P. D.

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issue 2

13

A Monte Carlo-Based Method for Assessing the Measurement Uncertainty in the Training and Use of Artificial Neural Networks

88%

Coral R., Flesch C. A., Penz C. A., Roisenberg M., Pacheco A. L. S.

Metrology and Measurement Systems

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2016

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vol. 23

|

issue 2

14

Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features

75%

Grochowski M., Mikołajczyk A., Kwasigroch A.

Metrology and Measurement Systems

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2019

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vol. 26

|

issue 1

15

Limited selectivity of amperometric gas sensors operating in multicomponent gas mixtures and methods of selectivity improvement

75%

Dmitrzak M., Jasinski P., Jasinski G., Dmitrzak M., Jasinski P., Jasinski G.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2020

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issue 6

16

Prediction of Properties of Microwave- Hardened Sandmixes Containing Water- Glass with Use of Neural Networks

75%

Stachowicz M., Pałyga Ł., Granat K.

Archives of Foundry Engineering

|

2015

|

issue 2

17

Evaluation of the unit weight of organic soils from a CPTM using an Artificial Neural Networks

75%

Straż G., Borowiec A., Straż G., Borowiec A.

Archives of Civil Engineering

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2021

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vol. 67

|

issue 3

18

Prediction of asphalt creep compliance using artificial neural networks

75%

Zofka A., Yut I.

Archives of Civil Engineering

|

2012

|

issue 2

19

Assessing domestic factors determining water consumption in a semi-arid area (Sedrata City) using artificial neural networks and principal component analysis

75%

Zeroual M., Hani A., Boustila A., Zeroual M., Hani A., Boustila A.

Journal of Water and Land Development

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2021

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issue 49

20

ANN-based modeling of fly ash compaction curve

71%

Zabielska-Adamska K., Sulewska M. J., Zabielska-Adamska K., Sulewska M. J.

Archives of Civil Engineering

|

2012

|

issue 1

Refine search results

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

Prediction of the unit cell edge length of cubic A22+BB′O6 perovskites by multiple linear regression and artificial neural networks

Application of artificial neural nerworks in oxidation kinetic analysis of nanocomposites

THE USE OF ARTIFICIAL NEURAL NETWORKS IN SUPPORTING THE ANNUAL TRAINING IN 400 METER HURDLES

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

The Influence of Changes in Active Binder Content on the Control System of the Moulding Sand Quality

Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

Coronary Artery Diagnosis Aided by Neural Network

Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

Automatic Fault Classification for Journal Bearings Using ANN and DNN

Prediction of quality parameters of a dry air separation product using machine learning methods

Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

A Monte Carlo-Based Method for Assessing the Measurement Uncertainty in the Training and Use of Artificial Neural Networks

Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features

Limited selectivity of amperometric gas sensors operating in multicomponent gas mixtures and methods of selectivity improvement

Prediction of Properties of Microwave- Hardened Sandmixes Containing Water- Glass with Use of Neural Networks

Evaluation of the unit weight of organic soils from a CPTM using an Artificial Neural Networks

Prediction of asphalt creep compliance using artificial neural networks

Assessing domestic factors determining water consumption in a semi-arid area (Sedrata City) using artificial neural networks and principal component analysis

ANN-based modeling of fly ash compaction curve