International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Review of the Concept of Perpetual Motion Machines

Author : Kaushalendra Kumar Dubey ¹

Date of Publication :20th July 2017

Abstract: Cases of "Free Energy" age utilizing â€œPerpetual Motion Machines (PMM)â€ are generally limited by the academic network since â€œPerpetual Motion Machinesâ€ are viewed as incomprehensible, as an immediate end product of the â€œLaw of Conservation of Energyâ€. This system contains eight magnets organized at equivalent separations at the edge of a round plate and a ring magnet at the focus of the circle. The primary magnet at the edge of the roundabout plate has its north lined up with pivot of turn. The following magnet put after 1/eighth of the perimeter is turned by an edge of 450 around z-pivot with the end goal that in the wake of travelling half circuit the magnet turns through 1800. The input is ring magnet which has an inward sweep of 31.75 mm and an external range of 50.8 mm. The ring magnet is situated at a point between 300-400 mm from the level upper surface of the plate. The lower face of the ring magnet is North Pole and the upper face goes about as South Pole or the other way around. In either case the substance of bar magnet confronting the ring magnet is with the end goal that there is a shocking power between the two. As the plate turns at first, the ring magnet is situated physically by turning it around it's z-hub at an edge of 300-400 to a pivot parallel to the upper surface of the plate with the end goal that at a moment.

Reference :

1. Julieta Martinez, Michael J Black, and Javier Romero. On human motion prediction using recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
2. Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. A recurrent latent variable model for sequential data. In Advances in neural information processing systems (NeurIPS), pages 2980–2988, 2015.
3. Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In Advances in neural information processing systems (NeurIPS), pages 3738–3746, 2016.
4. Pierre Charbonnier, Laure Blanc-Feraud, Gilles Aubert, and Michel Barlaud. Two deterministic half-quadratic regularization algorithms for computed imaging. In Proceedings of the International Conference on Image Processing, pages 168–172, 1994.
5. Partha Ghosh, Jie Song, Emre Aksan, and Otmar Hilliges. Learning human motion models for long-term predictions. In Proceedings of the International Conference on 3D Vision (3DV), pages 458–466, 2017.
6. Uttam Singh Bist, Manish Kumar, Anupam Baliyan, Vishal Jain, “Decision based Cognitive Learning using Strategic Game Theory”, Indian Journal of Science and Technology, Volume 9, Issue 39, October 2016, page no. 1-7 having ISSN No. 0974-6846
7. Vishal Jain, Gagandeep Singh, Dr. Mayank Singh, “Implementation of Data Mining in Online Shopping System using TANAGRA Tool”, International Journal for Computer Science Engineering (IJCSE), USA, January 2013 page no. 47-58 having ISSN No. 2278-9979.
8. K.Deepika, P.Andrew, R.Santhya, S.Balamurugan, S.Charanyaa, "A Survey on Approaches Developed for Data Anonymization", International Advanced Research Journal in Science, Engineering and Technology Vol 1, Issue 4, December 2014.
9. V.M.Prabhakaran, Prof.S.Balamurugan , S.Charanyaa, “A Strategy for Secured Uploading of Encrypted Microdata in Cloud Environments”, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 3, November 2014