TY  - CONF
AU  - Kostić, Srđan
AU  - Vasović, Nebojša
AU  - Todorović, Kristina
AU  - Samčović, Andreja
PY  - 2016
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2672
AB  - In present paper, authors develop a model for estimation of earth slope stability based on the artificial neural networks. For this purpose, authors engage multi-layer feed-forward network with Levenberg-Marquardt learning algorithm and 14 hidden nodes, using existing experimental data, and the results of traditional limit equilibrium analyzes of 57 different cases according to the predefined experimental plan. The results obtained indicate high level of statistical reliability (R=0.95 and MSE=0.0035 for testing set of scaled values) and similar estimation accuracy as the existing mathematical expression for calculation of slope safety factor.
PB  - IEEE, New York
C3  - 2016 13th Symposium on Neural Networks and Applications, NEUREL 2016
T1  - Application of artificial neural networks for slope stability analysis in geotechnical practice
SP  - 89
EP  - 94
DO  - 10.1109/NEUREL.2016.7800125
ER  - 

@conference{
author = "Kostić, Srđan and Vasović, Nebojša and Todorović, Kristina and Samčović, Andreja",
year = "2016",
abstract = "In present paper, authors develop a model for estimation of earth slope stability based on the artificial neural networks. For this purpose, authors engage multi-layer feed-forward network with Levenberg-Marquardt learning algorithm and 14 hidden nodes, using existing experimental data, and the results of traditional limit equilibrium analyzes of 57 different cases according to the predefined experimental plan. The results obtained indicate high level of statistical reliability (R=0.95 and MSE=0.0035 for testing set of scaled values) and similar estimation accuracy as the existing mathematical expression for calculation of slope safety factor.",
publisher = "IEEE, New York",
journal = "2016 13th Symposium on Neural Networks and Applications, NEUREL 2016",
title = "Application of artificial neural networks for slope stability analysis in geotechnical practice",
pages = "89-94",
doi = "10.1109/NEUREL.2016.7800125"
}

Kostić, S., Vasović, N., Todorović, K.,& Samčović, A.. (2016). Application of artificial neural networks for slope stability analysis in geotechnical practice. in 2016 13th Symposium on Neural Networks and Applications, NEUREL 2016
IEEE, New York., 89-94.
https://doi.org/10.1109/NEUREL.2016.7800125

Kostić S, Vasović N, Todorović K, Samčović A. Application of artificial neural networks for slope stability analysis in geotechnical practice. in 2016 13th Symposium on Neural Networks and Applications, NEUREL 2016. 2016;:89-94.
doi:10.1109/NEUREL.2016.7800125 .

Kostić, Srđan, Vasović, Nebojša, Todorović, Kristina, Samčović, Andreja, "Application of artificial neural networks for slope stability analysis in geotechnical practice" in 2016 13th Symposium on Neural Networks and Applications, NEUREL 2016 (2016):89-94,
https://doi.org/10.1109/NEUREL.2016.7800125 . .

TY  - CONF
AU  - Kostić, Srđan
AU  - Vasović, Nebojša
AU  - Franović, Igor
AU  - Samčović, Andreja
AU  - Todorović, Kristina
PY  - 2015
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2501
AB  - Blast-induced ground motion is analyzed by means of two prediction methods. First conventional approach assumes several types of nonlinear dependence of peak particle velocity on scaled distance from the explosion charge, while the second technique implements a feed-forward three-layer back-propagation neural network with three nodes in input layer (total charge, maximum charge per delay and distance from explosive charge to monitoring point) and only one node in output layer (peak particle velocity). As a result, traditional predictors give acceptable prediction accuracy (r>0.7) when compared with registered values of peak particle velocity. Regarding the forecasting accuracy estimated by neural network, model with nine hidden nodes gives reasonable predictive precision (r>0.9), with much lower standard error in comparison to conventional predictors.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings
T1  - Assessment of Blast Induced Ground Vibrations by Artificial Neural Network
SP  - 55
EP  - 60
DO  - 10.1109/NEUREL.2014.7011458
ER  - 

@conference{
author = "Kostić, Srđan and Vasović, Nebojša and Franović, Igor and Samčović, Andreja and Todorović, Kristina",
year = "2015",
abstract = "Blast-induced ground motion is analyzed by means of two prediction methods. First conventional approach assumes several types of nonlinear dependence of peak particle velocity on scaled distance from the explosion charge, while the second technique implements a feed-forward three-layer back-propagation neural network with three nodes in input layer (total charge, maximum charge per delay and distance from explosive charge to monitoring point) and only one node in output layer (peak particle velocity). As a result, traditional predictors give acceptable prediction accuracy (r>0.7) when compared with registered values of peak particle velocity. Regarding the forecasting accuracy estimated by neural network, model with nine hidden nodes gives reasonable predictive precision (r>0.9), with much lower standard error in comparison to conventional predictors.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings",
title = "Assessment of Blast Induced Ground Vibrations by Artificial Neural Network",
pages = "55-60",
doi = "10.1109/NEUREL.2014.7011458"
}

Kostić, S., Vasović, N., Franović, I., Samčović, A.,& Todorović, K.. (2015). Assessment of Blast Induced Ground Vibrations by Artificial Neural Network. in 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings
Institute of Electrical and Electronics Engineers Inc.., 55-60.
https://doi.org/10.1109/NEUREL.2014.7011458

Kostić S, Vasović N, Franović I, Samčović A, Todorović K. Assessment of Blast Induced Ground Vibrations by Artificial Neural Network. in 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings. 2015;:55-60.
doi:10.1109/NEUREL.2014.7011458 .

Kostić, Srđan, Vasović, Nebojša, Franović, Igor, Samčović, Andreja, Todorović, Kristina, "Assessment of Blast Induced Ground Vibrations by Artificial Neural Network" in 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings (2015):55-60,
https://doi.org/10.1109/NEUREL.2014.7011458 . .

TY  - CONF
AU  - Vasović, Nebojša
AU  - Burić, Nikola
AU  - Grozdanović, Ines
AU  - Todorović, Kristina
AU  - Samčović, Andreja
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1666
AB  - Influence of small time-delays in coupling between noisy excitable systems on the coherence resonance and self-induced stochastic resonance is studied. Parameters of delayed coupled deterministic excitable units are chosen such that the system has only one attractor, namely the stationary state, for any value of the coupling and the time-lag. Addition of white noise induces qualitatively different types of coherent oscillations, and we analyzed the influence of coupling time-delay on the properties of these coherent oscillations. The main conclusion is that time-lag tau >= 1, but still smaller than the refractory period, and sufficiently strong coupling drastically change signal-to-noise ratio in the quantitative and qualitative way. An interval of noise values implies quite large signal to noise ratio and different types of noise induced coherence are greatly enhanced. We also observed coincident spiking for small noise intensity and time-lag proportional to the inter-spike interval of the coherent spike trains. On the other hand, time-lags tau  lt  1 and/or weak coupling induce negligible changes in the properties of the stochastic coherence.
PB  - IEEE, New York
C3  - 11th Symposium on Neural Network Applications in Electrical Engineering,NEUREL 2012 - Proceedings
T1  - Coherent Oscillations in Minimal Neural Network of Excitable Systems Induced by Noise and Influenced by Time Delay
DO  - 10.1109/NEUREL.2012.6419957
ER  - 

@conference{
author = "Vasović, Nebojša and Burić, Nikola and Grozdanović, Ines and Todorović, Kristina and Samčović, Andreja",
year = "2012",
abstract = "Influence of small time-delays in coupling between noisy excitable systems on the coherence resonance and self-induced stochastic resonance is studied. Parameters of delayed coupled deterministic excitable units are chosen such that the system has only one attractor, namely the stationary state, for any value of the coupling and the time-lag. Addition of white noise induces qualitatively different types of coherent oscillations, and we analyzed the influence of coupling time-delay on the properties of these coherent oscillations. The main conclusion is that time-lag tau >= 1, but still smaller than the refractory period, and sufficiently strong coupling drastically change signal-to-noise ratio in the quantitative and qualitative way. An interval of noise values implies quite large signal to noise ratio and different types of noise induced coherence are greatly enhanced. We also observed coincident spiking for small noise intensity and time-lag proportional to the inter-spike interval of the coherent spike trains. On the other hand, time-lags tau  lt  1 and/or weak coupling induce negligible changes in the properties of the stochastic coherence.",
publisher = "IEEE, New York",
journal = "11th Symposium on Neural Network Applications in Electrical Engineering,NEUREL 2012 - Proceedings",
title = "Coherent Oscillations in Minimal Neural Network of Excitable Systems Induced by Noise and Influenced by Time Delay",
doi = "10.1109/NEUREL.2012.6419957"
}

Vasović, N., Burić, N., Grozdanović, I., Todorović, K.,& Samčović, A.. (2012). Coherent Oscillations in Minimal Neural Network of Excitable Systems Induced by Noise and Influenced by Time Delay. in 11th Symposium on Neural Network Applications in Electrical Engineering,NEUREL 2012 - Proceedings
IEEE, New York..
https://doi.org/10.1109/NEUREL.2012.6419957

Vasović N, Burić N, Grozdanović I, Todorović K, Samčović A. Coherent Oscillations in Minimal Neural Network of Excitable Systems Induced by Noise and Influenced by Time Delay. in 11th Symposium on Neural Network Applications in Electrical Engineering,NEUREL 2012 - Proceedings. 2012;.
doi:10.1109/NEUREL.2012.6419957 .

Vasović, Nebojša, Burić, Nikola, Grozdanović, Ines, Todorović, Kristina, Samčović, Andreja, "Coherent Oscillations in Minimal Neural Network of Excitable Systems Induced by Noise and Influenced by Time Delay" in 11th Symposium on Neural Network Applications in Electrical Engineering,NEUREL 2012 - Proceedings (2012),
https://doi.org/10.1109/NEUREL.2012.6419957 . .

TY  - CONF
AU  - Burić, Nikola
AU  - Todorović, Kristina
AU  - Vasović, Nebojša
AU  - Samcović, Andreja
PY  - 2008
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1044
AB  - Synchronization patterns in chains of N bi-directionally delayed coupled systems with delayed feedback are studied in this paper. Each system is hyper-chaotic when decoupled from the chain. It is shown that chains with odd or even number of cites N display different spatial patterns of stable exact synchronization. When N is odd the only stable pattern of exact synchronization is among all of the units. When N is even, next to the nearest neighbors could become exactly synchronized, with the dynamics of the nearest neighbors related in a more complicated way. Sufficiently strong coupling leads to the nearest neighbor synchronization also for even N. No other patterns have been observed.
PB  - IEEE, New York
C3  - 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings
T1  - Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells
SP  - 51
DO  - 10.1109/NEUREL.2008.4685559
ER  - 

@conference{
author = "Burić, Nikola and Todorović, Kristina and Vasović, Nebojša and Samcović, Andreja",
year = "2008",
abstract = "Synchronization patterns in chains of N bi-directionally delayed coupled systems with delayed feedback are studied in this paper. Each system is hyper-chaotic when decoupled from the chain. It is shown that chains with odd or even number of cites N display different spatial patterns of stable exact synchronization. When N is odd the only stable pattern of exact synchronization is among all of the units. When N is even, next to the nearest neighbors could become exactly synchronized, with the dynamics of the nearest neighbors related in a more complicated way. Sufficiently strong coupling leads to the nearest neighbor synchronization also for even N. No other patterns have been observed.",
publisher = "IEEE, New York",
journal = "9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings",
title = "Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells",
pages = "51",
doi = "10.1109/NEUREL.2008.4685559"
}

Burić, N., Todorović, K., Vasović, N.,& Samcović, A.. (2008). Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells. in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings
IEEE, New York., 51.
https://doi.org/10.1109/NEUREL.2008.4685559

Burić N, Todorović K, Vasović N, Samcović A. Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells. in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings. 2008;:51.
doi:10.1109/NEUREL.2008.4685559 .

Burić, Nikola, Todorović, Kristina, Vasović, Nebojša, Samcović, Andreja, "Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells" in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings (2008):51,
https://doi.org/10.1109/NEUREL.2008.4685559 . .