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Patil, Aditya
- Simulation of Magneto-Rheological Brake for Automotive Application
Abstract Views :141 |
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Authors
Affiliations
1 Department of Automotive Engineering, School of Mechanical Engineering, VIT University, Vellore-632014, Tamil Nadu, IN
1 Department of Automotive Engineering, School of Mechanical Engineering, VIT University, Vellore-632014, Tamil Nadu, IN
Source
International Journal of Engineering Research, Vol 5, No 6 (2016), Pagination: 462-466Abstract
In this paper Magneto-Rheological Brake model has been proposed that can provide the braking torque required for stopping a two-wheeler vehicle under consideration. Firstly the braking torque required to stop the vehicle is calculated analytically. Then appropriate design parameters have been selected and a model has been developed in MATLAB/SIMULINK software to achieve the same braking torque. The braking torque has been calculated at vehicle speed of 70 km/hr. and for stopping time of 2(sec), 3(sec), 4(sec) and 5(sec) respectively. The maximum braking torque at stopping time of 5 seconds is 28.3485 Nm, at the rear wheel. The proposed MR model generates the required braking torque. The MR fluid used is Lord Corporation’s MRF-132DG. The use of Magneto-rheological fluids in the braking application has been restricted, as the torque produced is not as much as required for braking, due to limited shear stress the MR fluid can sustain. This paper implicates that MR brake torque capacity can be increased by proper design considerations. Here we have increased the number of discs for the same purpose.Keywords
MR Fluids, Braking Torque, MATLAB/Simulink, Number of Discs.- Advance Integrated System for Controlling Devices Using Brain Waves
Abstract Views :340 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Engineering, Dr. D. Y. Patil School of Engineering and Technology, Lohegaon, Pune, IN
1 Department of Computer Engineering, Dr. D. Y. Patil School of Engineering and Technology, Lohegaon, Pune, IN
Source
Automation and Autonomous Systems, Vol 11, No 1 (2019), Pagination: 17-22Abstract
The proposed system is fully functional product with practice of BCI (Brain Controlled Interface). The ambitious goal of the system is to help controlling the muscular action for handicapped people. The BCI is simply a Breakthrough from conventional channels of interaction which are muscles and thoughts, which results in communication and control between the human brain and physical devices, with the use of different kind of waves emitted by brain called as brain wave. A proper control over these waves can pursue a controlling channel which will help these device to work correctly. The brain wave patterns will be categorized and evaluated accordingly to generate a unique set of pattern which will provide functionality to each action of the system. All the electrical pulses will be sensed by the EEG sensor and then it will convert this raw format into packets and communicate with Bluetooth for transmission. The brain wave raw data will be extracted and processed using Arduino Microcontroller. The interfaces are created on MatLab that will change the module according to client’s needs.Keywords
BCI (Brain Controlled Interface), BrainWave, Arduino Microcontroller, EEG Sensor.References
- Shivappa, V. K. K., Luu, B., Solis, M., & George, K. (2018). “Home automation system using brain computer interface paradigm based on auditory selection attention”. 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
- Ms Nanditha, Smt. Christy Persya, “EEG-Based Brain Controlled Robo and Home Appliances”, International Journal of Engineering Trends and Technology (IJETT) – May 2017.
- R. ChandanaPriya, K. Aparna, “Mind Wave Sensor Controlled Wheel Chair”, International Journal of Advance Engineering and Research Development, September 2017.
- Latif, M. Y., Naeem, L., Hafeez, T., Raheel, Saeed, S. M. U., Awais, M.Anwar, S. M. (2017). “Brain computer interface based robotic arm control”. 2017 International Smart Cities Conference (ISC2).
- Matsuzawa, K., & Ishii, C. (2016). “Control of an electric wheelchair with a brain-computer interface headset”. 2016 International Conference on Advanced Mechatronic Systems (ICAMechS).
- Ghodake, A. A., & Shelke, S. D. (2016). “Brain controlled home automation system”. 2016 10th International Conference on Intelligent Systems and Control (ISCO).
- Liu, C., Zhang, X., Li, R., & Ma, W. (2015). “Design on portable brain control system and its application”. 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).
- Prashant, P., Joshi, A., & Gandhi, V. (2015). “Brain computer interface: A review.” 2015 5th Nirma University International Conference on Engineering (NUiCONE).
- M. Akila, K. Sathiya Sekar, A. Suresh, “Smart Brain-Controlled Wheelchair and Devices Based On Eeg In Low Cost For Disabled Person”, International Journal of Computers Communication Networks and Circuit Systems (IJCCN), April 2015.
- Wei Lee, Humaira Nisar,Aamir Malik,Kim Ho Yeap. (2013). “A brain computer interface for smart home control”. 2013 IEEE International Symposium on Consumer Electronics (ISCE).
- R Prathibha; L Swetha; K R Shobha. (2017). “Brain computer interface: Design and development of a smart robotic gripper for a prosthesis environment”. 2017 International Conference on Networks & Advances in Computational Technologies (NetACT).
- Dany Bright, Amrita Nair, Devashish Salvekar, Swati Bhisikar.(2016). “EEG-based brain controlled prosthetic arm”. 2016 Conference on Advances in Signal Processing (CASP)
- G. Wu, Z. Xie and X. Wang, “Development of a mind-controlled Android racing game using a brain computer interface (BCI)”,2014 4th IEEE International Conference on Information Science and Technology
- M. Y. Latif et al., “Brain computer interface based robotic arm control”, 2017 International Smart Cities Conference (ISC2).
- K. Matsuzawa and C. Ishii, “Control of an electric wheelchair with a brain-computer interface headset”, 2016 International Conference on Advanced Mechatronic Systems (ICAMechS).
- N. Shinde and K. George, “Brain-controlled driving aid for electric wheelchairs,” 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
- Rosemary Mampilly, Nicy Jos, Neema Rose, “Brain Computer Interface for Paralyzed People”, International Journal for Research in Applied Science & EngineeringTechnology (IJRASET), March 2017.
- T. Carlson, R. Leeb, R. Chavarriaga and J. del R. Millan, “The birth of the brain-controlled wheelchair,” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems