The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network

Adesiyan Ayomide; Obioma Osuagwu

doi:doi:10.11648/j.wjap.20210604.11

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The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network

Adesiyan Ayomide, Obioma Osuagwu

Published in World Journal of Applied Physics (Volume 6, Issue 4)

Received: 14 April 2021 Accepted: 30 April 2021 Published: 23 November 2021

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Abstract

This research shows a pedagogic experimental and theoretical study of the motion of a simple pendulum, which considers the propotionality to the variables length (L) and period-time (T) of a simple harmonic motion, is presented. The study has used RELU (RECTIFIED LINEAR UNIT) activation function in deep neural network which is a branch of artificial neural network to examine the correlation between the dependent and independent variables in a simple pendulum experiment, the variables and their values was first generated from an online physics laboratory, the values and their corresponding variables were later separated into two CSV files, after which they were analyzed with the use of linear regression model in PYTHON programming language. It also applies to Physics Direct Method to represent these equations, in addition to the numerical solutions discusses. This research investigates the relationship between Length and Period using neural network models to find out a unique numerical solution by using simulation to see their behavior which shows in last part of this article. The results obtained shows that the linear approach to modeling the relationship between a scalar response and one or more variables with the RELU activation function proves their proportionality, this would be a good reference against which other results obtained from other simple harmonic motion experiments can be compared.

Published in	World Journal of Applied Physics (Volume 6, Issue 4)
DOI	10.11648/j.wjap.20210604.11
Page(s)	55-59
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Simple Pendulum, Artificial Neural Network, Deep Learning Neural Network (DNN), RELU, Python Programming.

References

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[7]	Lin, T.-J., Liang, J.-C., & Tsai, C.-C. (2015). Identifying Taiwanese university students’ physics learning profiles and their role in physics learning self-efficacy. Research in Science Education, 45 (4), 605-624. https://doi.org/10.1007/s11165-014-9440-z.
[8]	Greca, I. M., & de Ataíde, A. R. P. (2017). The Influence of Epistemic Views Epistemic views About the Relationship between Physics and Mathematics in Understanding Physics Concepts and Problem Solving. Key Competences in Physics Teaching and Learning: Springer (pp. 55-64).
[9]	Er. Parveen Kumar, Er. Pooja Sharma, (2014), Neural Networks – A Study., IJEERT, vol 2, issue 2, pp 143-148.
[10]	Krizhevsky, A., Sutskever, I., and Hinton, G, (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pp. 1106–1114.
[11]	Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., (December 2013). Antonoglou, I., Wierstra, D., and Riedmiller, M. Playing Atari with Deep Reinforcement Learning. ArXiv e-prints.
[12]	Yosinski, J., Clune, J., Hidalgo, D., Nguyen, S., Zagal, J. C., and Lipson, H., (August, 2011). Evolving robot gaits in hardware: the hyperneat generative encoding vs. parameter optimization. In Proceedings of the 20th European Conference on Artificial Life, pp. 890–897.
[13]	Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550 (7676): 354.
[14]	Andrychowicz, M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al. (2018). Learning dexterous in-hand manipulation. arXiv preprint arXiv: 1808.00177.
[15]	Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., and Quillen, D. (2018). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 37 (4-5): 421–436.

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APA Style

Adesiyan Ayomide, Obioma Osuagwu. (2021). The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World Journal of Applied Physics, 6(4), 55-59. https://doi.org/10.11648/j.wjap.20210604.11

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ACS Style

Adesiyan Ayomide; Obioma Osuagwu. The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World J. Appl. Phys. 2021, 6(4), 55-59. doi: 10.11648/j.wjap.20210604.11

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AMA Style

Adesiyan Ayomide, Obioma Osuagwu. The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World J Appl Phys. 2021;6(4):55-59. doi: 10.11648/j.wjap.20210604.11

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@article{10.11648/j.wjap.20210604.11,
  author = {Adesiyan Ayomide and Obioma Osuagwu},
  title = {The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network},
  journal = {World Journal of Applied Physics},
  volume = {6},
  number = {4},
  pages = {55-59},
  doi = {10.11648/j.wjap.20210604.11},
  url = {https://doi.org/10.11648/j.wjap.20210604.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wjap.20210604.11},
  abstract = {This research shows a pedagogic experimental and theoretical study of the motion of a simple pendulum, which considers the propotionality to the variables length (L) and period-time (T) of a simple harmonic motion, is presented. The study has used RELU (RECTIFIED LINEAR UNIT) activation function in deep neural network which is a branch of artificial neural network to examine the correlation between the dependent and independent variables in a simple pendulum experiment, the variables and their values was first generated from an online physics laboratory, the values and their corresponding variables were later separated into two CSV files, after which they were analyzed with the use of linear regression model in PYTHON programming language. It also applies to Physics Direct Method to represent these equations, in addition to the numerical solutions discusses. This research investigates the relationship between Length and Period using neural network models to find out a unique numerical solution by using simulation to see their behavior which shows in last part of this article. The results obtained shows that the linear approach to modeling the relationship between a scalar response and one or more variables with the RELU activation function proves their proportionality, this would be a good reference against which other results obtained from other simple harmonic motion experiments can be compared.},
 year = {2021}
}

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TY  - JOUR
T1  - The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network
AU  - Adesiyan Ayomide
AU  - Obioma Osuagwu
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.wjap.20210604.11
DO  - 10.11648/j.wjap.20210604.11
T2  - World Journal of Applied Physics
JF  - World Journal of Applied Physics
JO  - World Journal of Applied Physics
SP  - 55
EP  - 59
PB  - Science Publishing Group
SN  - 2637-6008
UR  - https://doi.org/10.11648/j.wjap.20210604.11
AB  - This research shows a pedagogic experimental and theoretical study of the motion of a simple pendulum, which considers the propotionality to the variables length (L) and period-time (T) of a simple harmonic motion, is presented. The study has used RELU (RECTIFIED LINEAR UNIT) activation function in deep neural network which is a branch of artificial neural network to examine the correlation between the dependent and independent variables in a simple pendulum experiment, the variables and their values was first generated from an online physics laboratory, the values and their corresponding variables were later separated into two CSV files, after which they were analyzed with the use of linear regression model in PYTHON programming language. It also applies to Physics Direct Method to represent these equations, in addition to the numerical solutions discusses. This research investigates the relationship between Length and Period using neural network models to find out a unique numerical solution by using simulation to see their behavior which shows in last part of this article. The results obtained shows that the linear approach to modeling the relationship between a scalar response and one or more variables with the RELU activation function proves their proportionality, this would be a good reference against which other results obtained from other simple harmonic motion experiments can be compared.
VL  - 6
IS  - 4
ER  -

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Author Information

Adesiyan Ayomide

Department of Engineering/IT, City 105.1 FM, Lagos, Nigeria
Obioma Osuagwu

Department of Engineering/IT, City 105.1 FM, Lagos, Nigeria

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Plain Text BibTeX RIS

APA Style

Adesiyan Ayomide, Obioma Osuagwu. (2021). The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World Journal of Applied Physics, 6(4), 55-59. https://doi.org/10.11648/j.wjap.20210604.11

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ACS Style

Adesiyan Ayomide; Obioma Osuagwu. The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World J. Appl. Phys. 2021, 6(4), 55-59. doi: 10.11648/j.wjap.20210604.11

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AMA Style

Adesiyan Ayomide, Obioma Osuagwu. The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network. World J Appl Phys. 2021;6(4):55-59. doi: 10.11648/j.wjap.20210604.11

Copy | Download

@article{10.11648/j.wjap.20210604.11,
  author = {Adesiyan Ayomide and Obioma Osuagwu},
  title = {The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network},
  journal = {World Journal of Applied Physics},
  volume = {6},
  number = {4},
  pages = {55-59},
  doi = {10.11648/j.wjap.20210604.11},
  url = {https://doi.org/10.11648/j.wjap.20210604.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wjap.20210604.11},
  abstract = {This research shows a pedagogic experimental and theoretical study of the motion of a simple pendulum, which considers the propotionality to the variables length (L) and period-time (T) of a simple harmonic motion, is presented. The study has used RELU (RECTIFIED LINEAR UNIT) activation function in deep neural network which is a branch of artificial neural network to examine the correlation between the dependent and independent variables in a simple pendulum experiment, the variables and their values was first generated from an online physics laboratory, the values and their corresponding variables were later separated into two CSV files, after which they were analyzed with the use of linear regression model in PYTHON programming language. It also applies to Physics Direct Method to represent these equations, in addition to the numerical solutions discusses. This research investigates the relationship between Length and Period using neural network models to find out a unique numerical solution by using simulation to see their behavior which shows in last part of this article. The results obtained shows that the linear approach to modeling the relationship between a scalar response and one or more variables with the RELU activation function proves their proportionality, this would be a good reference against which other results obtained from other simple harmonic motion experiments can be compared.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - The Verification of Propotionality Between Time Period and Length of a Simple Pendulum Experiment Using Deep Neural Network
AU  - Adesiyan Ayomide
AU  - Obioma Osuagwu
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.wjap.20210604.11
DO  - 10.11648/j.wjap.20210604.11
T2  - World Journal of Applied Physics
JF  - World Journal of Applied Physics
JO  - World Journal of Applied Physics
SP  - 55
EP  - 59
PB  - Science Publishing Group
SN  - 2637-6008
UR  - https://doi.org/10.11648/j.wjap.20210604.11
AB  - This research shows a pedagogic experimental and theoretical study of the motion of a simple pendulum, which considers the propotionality to the variables length (L) and period-time (T) of a simple harmonic motion, is presented. The study has used RELU (RECTIFIED LINEAR UNIT) activation function in deep neural network which is a branch of artificial neural network to examine the correlation between the dependent and independent variables in a simple pendulum experiment, the variables and their values was first generated from an online physics laboratory, the values and their corresponding variables were later separated into two CSV files, after which they were analyzed with the use of linear regression model in PYTHON programming language. It also applies to Physics Direct Method to represent these equations, in addition to the numerical solutions discusses. This research investigates the relationship between Length and Period using neural network models to find out a unique numerical solution by using simulation to see their behavior which shows in last part of this article. The results obtained shows that the linear approach to modeling the relationship between a scalar response and one or more variables with the RELU activation function proves their proportionality, this would be a good reference against which other results obtained from other simple harmonic motion experiments can be compared.
VL  - 6
IS  - 4
ER  -

Copy | Download