Nonlinear energy harvesting device for low frequency human motion application

Energy harvesting from ambient sources had received much attention in the past few years due to worldwide awareness on green technology expands. In vibration based energy harvesting, resonant linear generator are commonly used as the harvesting devices. However, a linear generator induces several li...

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Main Author:	Suhaimi, Khalis
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/16856/1/Nonlinear%20Energy%20Harvesting%20Device%20For%20Low%20Frequency%20Human%20Motion%20Application.pdf http://eprints.utem.edu.my/id/eprint/16856/2/Nonlinear%20energy%20harvesting%20device%20for%20low%20frequency%20human%20motion%20application.pdf
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id	my-utem-ep.16856
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Ramlan, Roszaidi
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Suhaimi, Khalis Nonlinear energy harvesting device for low frequency human motion application
description	Energy harvesting from ambient sources had received much attention in the past few years due to worldwide awareness on green technology expands. In vibration based energy harvesting, resonant linear generator are commonly used as the harvesting devices. However, a linear generator induces several limitations. The power harvested by a linear generator is proportional to the cube of excitation frequency and the power is maximum in a narrow bandwidth only. In this research, human motion vibration was selected as an input excitation and its frequency content is investigated. The frequency of human motion was investigated by placing a vibration recorder on a test subject under 5km/h walking and 9 km/h jogging speed.The investigation shows that the human motion vibration is distributed in the low frequency region. Hence, a device that can operate optimally with low frequency input and has the ability to overcome the narrow bandwidth limitation is designed. A device is designed to overcome the limitations of the linear generators. This device has the combination of the tuning, frequency-up conversion, multimodal and non-linear techniques. The aim is to amplify the input frequency to a higher frequency and at the same time, widen the bandwidth of response. The frequency-up mechanism is made by transforming the translation motion into the rotary motion by using gear ratio to amplify the response to a higher rotational speed. Winding springs are used with twistable enclosure cap to alter the device stiffness. The angles of twist of the enclosure cap are ranging from 180 degree to 900 degree. Two oscillating masses are connected to the device. Each mass can be set with different characteristic to widen the bandwidth. The two masses are also configured with non-linear softening and non-linear hardening properties to further widen the bandwidth. The non-linearities of the system are changed by varying the magnets gap. The non-linear restoring force of the system shows the influences of the linear coefficient and non-linear coefficient. The device is then investigated with two sets of experiments. The quasi-static measurement is to investigate the system stiffness and dynamic measurement is to investigate its response across a frequency range. In the dynamic measurement the device is excited with sinusoidal inputs and real human motion inputs. Overall, the results obtained from the experiment show that device is able to produce frequency amplification. The response also shows that with a properly tuned system, both softening and hardening can produce a flat response which is insensitive to excitation frequency as well as at amplified amplitude.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Suhaimi, Khalis
author_facet	Suhaimi, Khalis
author_sort	Suhaimi, Khalis
title	Nonlinear energy harvesting device for low frequency human motion application
title_short	Nonlinear energy harvesting device for low frequency human motion application
title_full	Nonlinear energy harvesting device for low frequency human motion application
title_fullStr	Nonlinear energy harvesting device for low frequency human motion application
title_full_unstemmed	Nonlinear energy harvesting device for low frequency human motion application
title_sort	nonlinear energy harvesting device for low frequency human motion application
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Mechanical Engineering
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/16856/1/Nonlinear%20Energy%20Harvesting%20Device%20For%20Low%20Frequency%20Human%20Motion%20Application.pdf http://eprints.utem.edu.my/id/eprint/16856/2/Nonlinear%20energy%20harvesting%20device%20for%20low%20frequency%20human%20motion%20application.pdf
_version_	1747833901484605440
spelling	my-utem-ep.168562022-05-13T11:21:04Z Nonlinear energy harvesting device for low frequency human motion application 2015 Suhaimi, Khalis T Technology (General) TA Engineering (General). Civil engineering (General) Energy harvesting from ambient sources had received much attention in the past few years due to worldwide awareness on green technology expands. In vibration based energy harvesting, resonant linear generator are commonly used as the harvesting devices. However, a linear generator induces several limitations. The power harvested by a linear generator is proportional to the cube of excitation frequency and the power is maximum in a narrow bandwidth only. In this research, human motion vibration was selected as an input excitation and its frequency content is investigated. The frequency of human motion was investigated by placing a vibration recorder on a test subject under 5km/h walking and 9 km/h jogging speed.The investigation shows that the human motion vibration is distributed in the low frequency region. Hence, a device that can operate optimally with low frequency input and has the ability to overcome the narrow bandwidth limitation is designed. A device is designed to overcome the limitations of the linear generators. This device has the combination of the tuning, frequency-up conversion, multimodal and non-linear techniques. The aim is to amplify the input frequency to a higher frequency and at the same time, widen the bandwidth of response. The frequency-up mechanism is made by transforming the translation motion into the rotary motion by using gear ratio to amplify the response to a higher rotational speed. Winding springs are used with twistable enclosure cap to alter the device stiffness. The angles of twist of the enclosure cap are ranging from 180 degree to 900 degree. Two oscillating masses are connected to the device. Each mass can be set with different characteristic to widen the bandwidth. The two masses are also configured with non-linear softening and non-linear hardening properties to further widen the bandwidth. The non-linearities of the system are changed by varying the magnets gap. The non-linear restoring force of the system shows the influences of the linear coefficient and non-linear coefficient. The device is then investigated with two sets of experiments. The quasi-static measurement is to investigate the system stiffness and dynamic measurement is to investigate its response across a frequency range. In the dynamic measurement the device is excited with sinusoidal inputs and real human motion inputs. Overall, the results obtained from the experiment show that device is able to produce frequency amplification. The response also shows that with a properly tuned system, both softening and hardening can produce a flat response which is insensitive to excitation frequency as well as at amplified amplitude. 2015 Thesis http://eprints.utem.edu.my/id/eprint/16856/ http://eprints.utem.edu.my/id/eprint/16856/1/Nonlinear%20Energy%20Harvesting%20Device%20For%20Low%20Frequency%20Human%20Motion%20Application.pdf text en public http://eprints.utem.edu.my/id/eprint/16856/2/Nonlinear%20energy%20harvesting%20device%20for%20low%20frequency%20human%20motion%20application.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96161&query_desc=kw%2Cwrdl%3A%20CDR%2012362 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Mechanical Engineering Ramlan, Roszaidi 1. Abdi, H. and Wiliam, L. J., 2010. Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics. Wiley Online Library. 2. Abdi, H. and Williams, L. J., 2010. Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics. Wiley Online Library. 3. Aparna, R., Bincy, G. and Mathu, T., 2012. Survey on Common Data Mining Classification Techniques. International journal of Wisdom Based Computing, 2(1). 4. Archana, S. and Elangovan, K., 2014. Survey of Classification Techniques in Data Mining. internation Journal of Computer Science and Mobile Application, 2 (2), pp. 65-71. 5. Balakrishnama, S. and Ganapathiraju, A., 2010. Linear Discriminant Analysis - A Brief Toturial. 6. Bellman, R., 1961. Adaptive Control Processes: A Guideed Tour. In: Princeton University Press, P. (ed.). 7. Bensefia, A., Thierry, P. and Laurent, H., 2004. Handwriting Analysis for Writer Verification. Proceedings of the 9th International Workshop on Frontier in Handwriting Recognition. IEEE. 8. Bensefia, A., Thierry, P. and Laurent, H., 2005. A writer identification and verification system. Pattern Recognition Letters, 26 (13), pp. 2080-2092. 9. Breiman, L., 2001. Random Forest. Kluwer Academic Publishers, Machine Learning, 45, pp. 5 - 32. 10. Cha, S. H. and Srihari, S. N., 2000. Multiple Feature Intergration for Writer Verification. Proc. 7th. Workshop on Frontiers in Handwriting Recognition. 11. Cunningham, P., 2007. Dimension Reduction. Dublin: University College Dublin. 12. Deepthy, K. D. and Anita, J., 2012. Survey on Data Mining Teachniques to Enhance Instrusion Detection. International Conference on Computer Communication and Informatics. Coimbatore, India, IEEE. 13. Fernandes, S. and Bala, J., 2013. Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition. Internation Journal of Signal Processing System, 1. 14. Fukunaga, K., 1990. Introduction to Statistical Pattern Recognition. Academic Press Professional. San Diego, CA, essentialUSA. 15. Hall, M., Eibe, F., Geoffrey, H. and Bernhard, P., 2009. The WEKA Data Mining Software: An Update. 10 -18. 16. Harold, H., 1933. Analysis of a Complex of Statistical Variables into Proncipal Components. Journal of Education Psychology. 17. He, Z., Tang, Y. and Youp, X., 2008. Writer Identification using Global Wavelet-based Features. Neurocomputing, 71, pp. 1832-1841. 18. Huber, R. A. and Headrick, A. M., 1999. Handwriting Identication: Facts and Fundamentals. CRC Press. 19. Isaacs, J. C., Foo, S. Y. and Baese, A. M., 2007. Novel Kernels and Kernel PCA for Pattern Recognition. International Symposium on Computational Intelligence in Robotic and Automation. USA, IEEE. 20. Ji, S. and Ye, J., 2009. Linear Dimensionality Reduction for Multi-label Classification. International Joint Conferences on Artificial Intelligence, pp. 1077 - 1082. 21. Jolliffe, I. T., 1986. Principal Component Analysis. New York: Springer. 22. Kaiser, H. F., 1960. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 23. Karl, P., 1901. On lines and planes of closest fit to points in space. Philosophical Magazine. 24. Koren, Y. and Liran, C., 2004. Robust linear dimensionality reduction. IEEE Transactions on Visualization and Computer Graphics. IEEE Computer Society. 25. Kudo, M., 2000. Comparison of Algorithm that Select Features for Pattern Classifiers. Journal of Pattern Recognition, 33, pp. 25-41. 26. Kumar, C. A., 2009. Analysis of unsupervised dimensionality reduction techniques. Computer Sciences and Information System, 6(2), pp. 217-227. 27. Kumar, S. and Chauhan, A., 2014. A Survey on Image Feature Selection Techniques. Internation journal of Computer Science and Information Technology, 5 (5), pp. 6449 - 6452. 28. Li, C., Diao, Y., Ma, H. and Li, Y., 2008. A Statistical PCA Method for Face Recognition. Journal in Intelligent Information Technology Application, pp. 376-380. 29. Li, H., Jiang, T. and Zhang, K., 2006. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Transactions On Neural Networks. IEEE. 30. Maaten, L. J. P. v. d., Postma, E. O. and Herik, H. J. v. d., 2008. Dimensionality Reduction: A Comparative Review. Journal of Machine Learning Research. 31. Markus, I., Clement, A. and Tatjana, K., 2012. Tree Species Classification with Random Forest Using High Spatial Resolution 8-Band World View-2 Satellite Data. Remote Sensing, 4, pp. 22661-22693. 32. Marti, U. and Bunke, H., 2000. Handwritten Sentence Recognition. Proc. of the 15th Int. Conf. on Pattern Recognition. IEEE. 33. Marti, U. and Bunke, H., 2002. The IAM-Database: An English Sentence Database for Off-line Handwriting Recognition. Internation Journal on Document Analysis and Recognition, 5, pp. 39-46. 34. Martinez, A. M. and Kak, A. C., 2001. PCA Versus LDA. IEEE Transaction On Pattern Analysis and Machine Intelligence. IEEE. 35. Masaeli, M., Fung, G. and Jennifer, G., 2010. From Transformation-Based Dimensionality Reduction to Feature Selection. Proc IEEE, 27th International Conference on Machine Learning. Boston, USA, IEEE. 36. Meng, J. and Yang, Y., 2012. Symmetrical Two-Dimensional PCA with Image Measure in Face Recognition. International Journal of Advance Robotic System, 9. 37. Miguel, A. and Perpinan, C., 1997. A review of Dimension Reduction Techniques. Sheffeild: Dept.of Computer Science, University of Sheffield, CS-96-09. 38. Morris, R. and Morris, R. N., 2000. Forensic Handwriting Identification: Fundamental Concepts and Principles. Academic Press. 39. Muda, A. K., 2009. Authorship Invarianceness for Writer Identification Using Invariant Discretization and Modified Immune Classifier. Johor: Universiti Teknologi Malaysia. 40. Muda, A. K., Mariyam, S. and Maslina, D., 2008. Invariants Discretization for Individuality Representation in Handwritten Authorship. 2nd Internation Workshop on Computational Forensic. Washington: DC : Springer-Verlag. 41. Mukundan, R., 1998. Moment Functions in in Image Analysis Theory and Application. Singapore : World Scientific Publishing Co.Pte.Ltd. 42. Pankaj, D. S. and Wilscy, M., 2013. Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network. Conference in Advances in Computing and Infor-mation Technology. Springer. 43. Partridge, M. and Jabri, M., 2000. Robust Principal Component Analysis. Proceedings of the 2000 IEEE Signal Processing Society Workshop. Sydney: IEEE. 44. Pechenizkiy, M. and Puuronen, S., 2004. PCA-based feature transformation for classification: issues in medical diagnostics. Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems. IEEE. 45. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J. and Worek, W., 2005. Overview of the Face Recognition Grand Challenge. Conference on Computer vision and pattern recognition. IEEE. 46. Plamondon, R. and Lorette, G., 1989. Automatic Signiture Verification and Writer Identification - The State of the Art. 47. Prasad, S. and Sapre, A., 2010. Handwriting Analysis Based on Segmentation Method for Prediction of Human Personality Using Support Vector Machine. International Journal of Computer Application, 8. 48. Ranshul, C., Prabhdeep, S. and Rajiv, M., 2014. A Survey On Data Mining Techniques. International Journal of Advance Research in Computer and Communication Engineering, 3 (1). 49. Sasan, K., Shahidan, M. A., Azizah, A. M. and Mazdak, Z., 2013. An Overviewf of Principal Component Analysis. Journal of Signal and Information Processing, Scientific Research, 4, pp. 173-175. 50. Schlapbach, A. and Bunke, H., 2007. A Writer Identification and Verification System Using HMM Based Recognizer. Pattern Analysis and Applications, pp. 33-34. Springer. 51. Schlapbach, A. and Bunke, H., 2008. Off-lineWriter Identification and Verification Using Gaussian Mixture Models. In: S.Marinai, H. F. (ed.) Machine Learning in Document Analysis and Recognition, pp. 409-428. Springer. 52. Schomaker, L., 2008. Writer identification and verification. 53. Shah, J. H., Sharif, M., Raza, M. and Azeem, A., 2013. A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques. International Arab Journal of Information Technology, 10 (6). 54. Srihari, S. N., 2002. Individuality of Handwriting. Journal of Forensic Sciences, pp. 856-872. 55. Srihari, S. N., Huang, C., Srinivasan, H. and Shah, V. A., 2006. Biometric and Forensic Aspect of Digital Document Processing. Digital Document Processing. Springer. 56. Srihari, S. N. and Shi, Z., 2004. Forensic handwritten document retrieval system. Proc. of the First International Workshop on Document Image Analysis for Libraries. Washington, DC, USA, IEEE Computer Society. 57. Wang, X. and Ding, X., 2004. An effective writer verification algorithm using negative samples. Ninth International Workshop on Frontiers in Handwriting Recognition. . IEEE. 58. Yinan, S. and Wang, Y., 2003. United moment invariants for shape discrimination. Proceedings on IEEE International Conference Robotics, Intelligent Systems and Signal Processing., IEEE. 59. Zhang, B. and Srihari, S. N., 2003a. Analysis of handwriting individuality using word features. Proceedings. Seventh International Conference on Document Analysis and Recognition. IEEE. 60. Zhang, B., Srihari, S. N. and Lee, S., 2003b. Individuality of Handwritten Characters. Proc.7th.Conference on Document Analysis and Recognition. 61. Zhao, N., Washington, M. and Xiuwen, L., 2011. A Hybrid PCA-LDA Model for Dimension Reduction. International Joint Conference on Neural Network. San Jose, Calofornia, USA, IEEE. 62. Zhu, M., 2004. A Simple Technique for Automatically Selecting the Number of Principal Components via the Used of Profile Likelihood. Working Paper. Department of Statistic & Actuarial Science, University of Waterloo Canada. 63. Zhu, M. and Ghodsi, A., 2006. Automatic Dimensionality Selection from the Scree Plot via the Use of Profile Likelihood. Computational Statistics & Data Analysis, 51, pp. 918 - 930. 64. Zimmermann, M. and Bunke, H., 2000. Automatic Segmentation of the IAM Off-line Database for Handwritten English Text. Proc. of the 16th Int. Conf. on Pattern Recognition. 65. Zois, E. N. and Anastassopoulos, V., 2001. Fusion of Correlated Decisions for Writer Verification. Journal of Pattern Recognition, 34, pp. 47-61.

Nonlinear energy harvesting device for low frequency human motion application

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