Araştırma Makalesi
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Yıl 2020, Cilt: 3 Sayı: 2, 72 - 76, 06.12.2020

Esme Işık

Öz

Kaynakça

  • [1] T. Suda, M. Moore, T. Nakano, R. Egashira, and A. Enomoto, “Exploratory Research on Molecular Communication between Nanomachines,” Nat. Comput., vol. 25, pp. 1–30, 2005.
  • [2] G. W. Hanson, “Fundamental transmitting properties of carbon nanotube antennas,” IEEE Antennas Propag. Soc. AP-S Int. Symp., vol. 3 B, no. 11, pp. 247–250, 2005, doi: 10.1109/APS.2005.1552484.
  • [3] S. E. Guidoni and C. M. Aldao, “On diffusion, drift and the Einstein relation,” Eur. J. Phys., vol. 23, no. 4, pp. 395–402, 2002, doi: 10.1088/0143-0807/23/4/302.
  • [4] G. Peskir, “On the diffusion coefficient: The Einstein relation and beyond,” Stoch. Model., vol. 19, no. 3, pp. 383–405, 2003, doi: 10.1081/STM-120023566.
  • [5] M. Fuchs and K. Kroy, “Statistical mechanics derivation of hydrodynamic boundary conditions: The diffusion equation,” J. Phys. Condens. Matter, vol. 14, no. 40 SPEC., pp. 9223–9235, 2002, doi: 10.1088/0953-8984/14/40/313.
  • [6] S. Gisladottir, T. Loftsson, and E. Stefansson, “Diffusion characteristics of vitreous humour and saline solution follow the Stokes Einstein equation,” Graefe’s Arch. Clin. Exp. Ophthalmol., vol. 247, no. 12, pp. 1677–1684, 2009, doi: 10.1007/s00417-009-1141-3.
  • [7] B. Charbonneau, P. Charbonneau, and G. Szamel, “A microscopic model of the Stokes-Einstein relation in arbitrary dimension,” J. Chem. Phys., vol. 148, no. 22, 2018, doi: 10.1063/1.5029464.
  • [8] H. Walter and J. Vreeburg, Fluid Sciences and Materials Science in Space - a European Perspective, vol. 50. 1989.
  • [9] A. Breki and M. Nosonovsky, “Einstein’s Viscosity Equation for Nanolubricated Friction,” Langmuir, vol. 34, no. 43, pp. 12968–12973, 2018, doi: 10.1021/acs.langmuir.8b02861.
  • [10] I. Isik, H. B. Yilmaz, and M. E. Tagluk, “A Preliminary Investigation of Receiver Models in Molecular Communication via Diffusion,” 2017.
  • [11] W. Guo et al., “Molecular communications: Channel model and physical layer techniques,” IEEE Wirel. Commun., vol. 23, no. 4, pp. 120–127, 2016, doi: 10.1109/MWC.2016.7553035.
  • [12] A. Akkaya, H. B. Yilmaz, C. B. Chae, and T. Tugcu, “Effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver,” IEEE Commun. Lett., vol. 19, no. 2, pp. 155–158, 2015, doi: 10.1109/LCOMM.2014.2375214.
  • [13] M. E. Isik, I., Yilmaz, H. B., Demirkol, I., & Tagluk, “Effect of receiver shape and volume on the Alzheimer disease for molecular communication via diffusion,” IET nanobiotechnology, vol. 14, no. 7, pp. 602–608, 2020.
  • [14] M. A. Gluck and G. H. Bower, “From Conditioning to Category Learning: An Adaptive Network Model,” J. Exp. Psychol. Gen., vol. 117, no. 3, pp. 227–247, 1988, doi: 10.1037/0096-3445.117.3.227.
  • [15] J. Grainger, J. K. O’regan, A. M. Jacobs, and J. Segui, “On the role of competing word units in visual word recognition: The neighborhood frequency effect,” Percept. Psychophys., vol. 45, no. 3, pp. 189–195, 1989, doi: 10.3758/BF03210696.
  • [16] M. Y. Rafiq, G. Bugmann, and D. J. Easterbrook, “Neural network design for engineering applications,” Comput. Struct., vol. 79, no. 17, pp. 1541–1552, 2001, doi: 10.1016/S0045-7949(01)00039-6.
  • [17] N. Farsad and A. Goldsmith, “Neural Network Detectors for Sequence Detection in Communication Systems,” pp. 1–15.
  • [18] S. Balasubramaniam et al., “Development of Artificial Neuronal Networks for Molecular Communication,” Nano Commun. Netw., vol. 2, no. 2, 2011, doi: 10.1016/j.nancom.2011.05.004.
  • [19] E. Işik, H. Toktamiş, and İ. Işik, “Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model,” Luminescence, no. November 2019, pp. 1–8, 2020, doi: 10.1002/bio.3788.
  • [20] M. E. Tagluk and I. Isik, “Communication in nano devices: Electronic based biophysical model of a neuron,” Nano Commun. Netw., vol. 19, pp. 134–147, 2019, doi: 10.1016/J.NANCOM.2019.01.006.

Analyzing of the Viscosity by Using Artificial Neural Networks

Yıl 2020, Cilt: 3 Sayı: 2, 72 - 76, 06.12.2020

Esme Işık

Öz

The nano networks with many thousands of devices which are working cooperatively to complete challenging tasks, ultra-responsive to changes in the environment, and self-replicating devices are called nano devices (nano machine). It is thought that the studies in this field will contribute greatly to the developments in the field of nano technology. Many models have been proposed to provide nano or macro-scale systems such as molecular communication, interstitial or inter-neural communication. In these systems, information is carried by the molecules in the diffusion medium and the viscosity of the medium is an important parameter. In this study, some of the system parameters, the viscosity and distance (d) between transmitter and receiver are examined detailed by using Artificial Neural Network (ANN) algorithm in Matlab. Viscosity and d are simulated and predicted by using ANN and they also compared with results of the proposed system model.

Anahtar Kelimeler

Nano-scale systems Viscosity Artificial neural network

Kaynakça

  • [1] T. Suda, M. Moore, T. Nakano, R. Egashira, and A. Enomoto, “Exploratory Research on Molecular Communication between Nanomachines,” Nat. Comput., vol. 25, pp. 1–30, 2005.
  • [2] G. W. Hanson, “Fundamental transmitting properties of carbon nanotube antennas,” IEEE Antennas Propag. Soc. AP-S Int. Symp., vol. 3 B, no. 11, pp. 247–250, 2005, doi: 10.1109/APS.2005.1552484.
  • [3] S. E. Guidoni and C. M. Aldao, “On diffusion, drift and the Einstein relation,” Eur. J. Phys., vol. 23, no. 4, pp. 395–402, 2002, doi: 10.1088/0143-0807/23/4/302.
  • [4] G. Peskir, “On the diffusion coefficient: The Einstein relation and beyond,” Stoch. Model., vol. 19, no. 3, pp. 383–405, 2003, doi: 10.1081/STM-120023566.
  • [5] M. Fuchs and K. Kroy, “Statistical mechanics derivation of hydrodynamic boundary conditions: The diffusion equation,” J. Phys. Condens. Matter, vol. 14, no. 40 SPEC., pp. 9223–9235, 2002, doi: 10.1088/0953-8984/14/40/313.
  • [6] S. Gisladottir, T. Loftsson, and E. Stefansson, “Diffusion characteristics of vitreous humour and saline solution follow the Stokes Einstein equation,” Graefe’s Arch. Clin. Exp. Ophthalmol., vol. 247, no. 12, pp. 1677–1684, 2009, doi: 10.1007/s00417-009-1141-3.
  • [7] B. Charbonneau, P. Charbonneau, and G. Szamel, “A microscopic model of the Stokes-Einstein relation in arbitrary dimension,” J. Chem. Phys., vol. 148, no. 22, 2018, doi: 10.1063/1.5029464.
  • [8] H. Walter and J. Vreeburg, Fluid Sciences and Materials Science in Space - a European Perspective, vol. 50. 1989.
  • [9] A. Breki and M. Nosonovsky, “Einstein’s Viscosity Equation for Nanolubricated Friction,” Langmuir, vol. 34, no. 43, pp. 12968–12973, 2018, doi: 10.1021/acs.langmuir.8b02861.
  • [10] I. Isik, H. B. Yilmaz, and M. E. Tagluk, “A Preliminary Investigation of Receiver Models in Molecular Communication via Diffusion,” 2017.
  • [11] W. Guo et al., “Molecular communications: Channel model and physical layer techniques,” IEEE Wirel. Commun., vol. 23, no. 4, pp. 120–127, 2016, doi: 10.1109/MWC.2016.7553035.
  • [12] A. Akkaya, H. B. Yilmaz, C. B. Chae, and T. Tugcu, “Effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver,” IEEE Commun. Lett., vol. 19, no. 2, pp. 155–158, 2015, doi: 10.1109/LCOMM.2014.2375214.
  • [13] M. E. Isik, I., Yilmaz, H. B., Demirkol, I., & Tagluk, “Effect of receiver shape and volume on the Alzheimer disease for molecular communication via diffusion,” IET nanobiotechnology, vol. 14, no. 7, pp. 602–608, 2020.
  • [14] M. A. Gluck and G. H. Bower, “From Conditioning to Category Learning: An Adaptive Network Model,” J. Exp. Psychol. Gen., vol. 117, no. 3, pp. 227–247, 1988, doi: 10.1037/0096-3445.117.3.227.
  • [15] J. Grainger, J. K. O’regan, A. M. Jacobs, and J. Segui, “On the role of competing word units in visual word recognition: The neighborhood frequency effect,” Percept. Psychophys., vol. 45, no. 3, pp. 189–195, 1989, doi: 10.3758/BF03210696.
  • [16] M. Y. Rafiq, G. Bugmann, and D. J. Easterbrook, “Neural network design for engineering applications,” Comput. Struct., vol. 79, no. 17, pp. 1541–1552, 2001, doi: 10.1016/S0045-7949(01)00039-6.
  • [17] N. Farsad and A. Goldsmith, “Neural Network Detectors for Sequence Detection in Communication Systems,” pp. 1–15.
  • [18] S. Balasubramaniam et al., “Development of Artificial Neuronal Networks for Molecular Communication,” Nano Commun. Netw., vol. 2, no. 2, 2011, doi: 10.1016/j.nancom.2011.05.004.
  • [19] E. Işik, H. Toktamiş, and İ. Işik, “Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model,” Luminescence, no. November 2019, pp. 1–8, 2020, doi: 10.1002/bio.3788.
  • [20] M. E. Tagluk and I. Isik, “Communication in nano devices: Electronic based biophysical model of a neuron,” Nano Commun. Netw., vol. 19, pp. 134–147, 2019, doi: 10.1016/J.NANCOM.2019.01.006.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Esme Işık 0000-0002-6179-5746

Yayımlanma Tarihi 6 Aralık 2020
Gönderilme Tarihi 8 Kasım 2020
Kabul Tarihi 14 Kasım 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 2

Kaynak Göster

APA Işık, E. (2020). Analyzing of the Viscosity by Using Artificial Neural Networks. Journal of Physical Chemistry and Functional Materials, 3(2), 72-76.
AMA Işık E. Analyzing of the Viscosity by Using Artificial Neural Networks. Journal of Physical Chemistry and Functional Materials. Aralık 2020;3(2):72-76.
Chicago Işık, Esme. “Analyzing of the Viscosity by Using Artificial Neural Networks”. Journal of Physical Chemistry and Functional Materials 3, sy. 2 (Aralık 2020): 72-76.
EndNote Işık E (01 Aralık 2020) Analyzing of the Viscosity by Using Artificial Neural Networks. Journal of Physical Chemistry and Functional Materials 3 2 72–76.
IEEE E. Işık, “Analyzing of the Viscosity by Using Artificial Neural Networks”, Journal of Physical Chemistry and Functional Materials, c. 3, sy. 2, ss. 72–76, 2020.
ISNAD Işık, Esme. “Analyzing of the Viscosity by Using Artificial Neural Networks”. Journal of Physical Chemistry and Functional Materials 3/2 (Aralık 2020), 72-76.
JAMA Işık E. Analyzing of the Viscosity by Using Artificial Neural Networks. Journal of Physical Chemistry and Functional Materials. 2020;3:72–76.
MLA Işık, Esme. “Analyzing of the Viscosity by Using Artificial Neural Networks”. Journal of Physical Chemistry and Functional Materials, c. 3, sy. 2, 2020, ss. 72-76.
Vancouver Işık E. Analyzing of the Viscosity by Using Artificial Neural Networks. Journal of Physical Chemistry and Functional Materials. 2020;3(2):72-6.
 Kapak Resmi İndir