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Yuvaraj, D.
- Design and Simulation of Thermal Power Plant Using PLC and SCADA
Authors
Source
Programmable Device Circuits and Systems, Vol 8, No 8 (2016), Pagination: 228-232Abstract
This project is enhanced by constant monitoring using SCADA screen which is connected to the PLC by means of communication cable. In order to automate a power plant and minimize human intervention, there is a need to develop a SCADA (Supervisory Control and Data Acquisition) system that monitors the plant and helps reduce the errors caused by humans. SCADA system is used in monitoring the power plant parameter likes temperature, pressure, flow, level sensors are used to monitor the parameter and the sensed signals are processed by PLC and monitor with SCADA the signals are compared with the reference parameter and the respective valves of the parameter are adjusted with the monitoring and logic control system.
Keywords
Pressure, Level, Flow, Temperature Control.
- Android Based Robotic Arm Control
Authors
Source
Digital Signal Processing, Vol 8, No 6 (2016), Pagination: 177-180Abstract
The work is designed to develop a pick and place robotic arm vehicle with a soft catching gripper that is designed to avoid extra pressure on the suspected object (Like Bombs) for safety reasons. The robotic vehicle is android application controlled for remote operation. At the transmitting end using android application device, commands are sent to the receiver to control the movement of the robot either to move forward, backward and left or right etc. At the receiving end four motors are interfaced to the microcontroller where two of them are used for arm and gripper movement of the robot while the other two are for the body movement of the vehicle. The main advantage of this robot is its soft catching arm that is designed to avoid extra pressure on the suspected object for safety reasons. The android application device transmitter acts as a remote control that has the advantage of adequate range, while the receiver end. Bluetooth device is connected to the microcontroller to drive DC motors via motor driver IC for necessary operation. Remote operation is achieved by any smart-phone/Tablet etc., with Android OS; upon a GUI (Graphical User Interface) based touch screen operation.Keywords
Pick and Place Robot, Soft Catching Arm, Atmega328, Android, Blue Control.- MEMS Based Robot
Authors
1 Department of ICE, Tamilnadu College of Engineering, IN
Source
Fuzzy Systems, Vol 9, No 6 (2017), Pagination: 114-116Abstract
Robotics plays vital role in industrial automation, automobile, communication, bio-medical, weather fore casting and much more areas. Robotics are manmade machine to do continuous operation without rest. Old generation robots are programmed for single continuous work. New generation robots are very intelligent, which can perform various works simultaneously. We would like to develop a real working model robot to handle various functions which are essential nowadays world. We would like to develop the robot using state of art embedded technology to reduce the complexity in design and to achieve maximized output with minimized input. The robot in our mind is expected to perform various works like moving forward, moving reverse, tuning left and right, with super intelligence like detecting the obstacle, finding bombs on its route, path finding, Image capturing, voice detection and recording mechanical conventional recorders etc…Only 35 single world instructions to learn PIC 16F877A controllers is a powerful tool for automation and the chip is most compatible for our project. 16F877A will be coupled to the computer using proper interpreter protocols. The chip is available in normal 40 PIN DIP package. A real movable mechanical model also will be developed to ensure our reality demonstration. Mechanical model will not like a toy, expected to look like real moving and industrial model.
References
- “Robotics” by K.S.FU, R.C. Gonzalez, C.S.G. Lee, McGraw Hill Publications.
- “Industrial Robotics” by Groover, Weiss, Nagel, Odrey, McGraw Hill Publications.
- “Robotic Engineering” by Richard D. Klafter, Thomas A. Chmielewski, Michael Negin, PHI Pvt Ltd.
- Analysis on Performance Comparison of Virtual Grid-Base Dynamic Route Adjustment in Wireless Detector Networks
Authors
1 Department of Computer Science and Engineering, SriGuru Institute of Technology, IN
2 Department of Computer Science and Engineering, Rathinam Technical campus, IN
3 Department of Computer Science and Engineering, Cihan University - Duhok, IQ
Source
ICTACT Journal on Communication Technology, Vol 11, No 1 (2020), Pagination: 2138-2142Abstract
A virtual Grid-based dynamic routes adjustment scheme (virtual grid routing) for wireless sink-based wireless sensor networks is a recent introduction. Each mobile node in the network is capable of sensing, processing and communicating. In the present scenario, sensor networks are used in a variety of applications such as military, commercial, industrial, etc. which require constant monitoring and detection of specific event. The approach of efficient data delivery using communication of distance priority is used, avoiding the technique of previous schemes. Our method aims to reduce the routes reconstruction cost of sensor nodes while maintaining the most favorable routes to the mobile sink's recent location. It will improve the lifetime and reduce the cost consumption. This method highlights many routed schemes. A few such novel routing schemes are Virtual Grid based Dynamic Route Adjustment (VGDRA), Multiple Enhanced Specified-deployed Sub-sinks (MESS), Virtual Circle Combined Straight Routing (VCCSR), Hexagonal cell-based Data Dissemination (HexDD), Hierarchical Cluster-based Data Dissemination (HCDD), Backbone-based Virtual Infrastructure (BVI), Line-Based Data Dissemination (LBDD), Rail Road, Quadtree-based Data Dissemination (QDD), and Two-Tier Data Dissemination (TTDD). But, each scheme has its own advantages and disadvantages.Keywords
VGDRA, MESS, VCCSR, HCDD, BVI, LBDD, QDD, TTDD, WDN, Grid Routing, Data Mining.References
- J. Wang, J.Cao, S. Ji and J.H. Park, “Energy-Efficient Cluster-based Dynamic Routes Adjustment Approach for Wireless Sensor Networks with Mobile Sinks”, Journal of Supercomputing, Vol. 73, No. 7, pp. 3277-3290, 2017.
- J.S. Pan, L. Kong, T.W. Sung, P.W. Tsai and V. Snasel, “A Clustering Scheme for Wireless Sensor Networks based on Genetic Algorithm and Dominating Set”, Journal of Internet Technology, Vol. 19, No. 4, pp. 1111-1118, 2018.
- W. Qi, W. Liu, X. Liu, A. Liu, T. Wang, N.N. Xiong and Z. Cai, “Minimizing Delay and Transmission Times with Long Lifetime in Code Dissemination Scheme for High Loss Ratio and Low Duty Cycle Wireless Sensor Networks”, Sensors, Vol. 18, No. 10, pp. 3516-3523, 2018.
- S. Chen, C. Zhao and M. Wu, “Compressive Network Coding for Wireless Sensor Networks: Spatio-Temporal Coding and Optimization Design”, Computer Networks, Vol. 108, No. 1, pp. 345-356, 2016.
- X. Deng, Z. Tang, L.T. Yang, M. Lin and B. Wang, “Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 5, pp. 2220-2229, 2017.
- Y. Liu, K. Ota, K. Zhang, M. Ma and N. Xiong, “QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 8273-8291, 2018.
- T.L. Duc, D.T. Le, V.V. Zalyubovski and D.S. Kim, “Towards Broadcast Redundancy Minimization in Duty‐Cycled Wireless Sensor Networks”, International Journal of Communication Systems, Vol. 30, No. 6, pp. 3108-3117, 2017.
- J. Tan, W. Liu, T. Wang, S. Zhang, A. Liu, M. Xie and M. Zhao, “An Efficient Information Maximization based Adaptive Congestion Control Scheme in Wireless Sensor Network”, IEEE Access, Vol. 7, pp. 64878-64896, 2019.
- L. Kong, J.S. Pan and V. Snasel, “An Energy-Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.
- E.P.K. Gilbert, B. Kaliaperumal, E.B. Rajsingh and M. Lydia, “Trust based Data Prediction, Aggregation and Reconstruction using Compressed Sensing for Clustered Wireless Sensor Networks”, Computers and Electrical Engineering, Vol. 72, pp. 894-909, 2018.
- F. Ma, X. Liu, A. Liu, M. Zhao, C. Huang and T. Wang, “A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks”, Wireless Communications and Mobile Computing, Vol. 32, No. 2, pp. 1-16, 2018.
- K. Muthukumaran, K. Chitra and C. Selvakumar, “An Energy Efficient Clustering Scheme using Multilevel Routing for Wireless Sensor Network”, Computers and Electrical Engineering, Vol. 69, pp. 642-652, 2018.
- A. Agrawal, V. Singh, S. Jain and R.K. Gupta, “GCRP: Grid-Cycle Routing Protocol for Wireless Sensor Network with Mobile Sink”, AEU-International Journal of Electronics and Communications, Vol. 94, No. 2, pp. 1-11, 2018.
- W. Chen and I.J. Wassell, “Cost-Aware Activity Scheduling for Compressive Sleeping Wireless Sensor Networks”, IEEE Transactions on Signal Processing, Vol. 64, No. 9, pp. 2314-2323, 2016.
- J.S. Pan, L. Kong and P.W. Tsail, “α-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks”, Journal of Internet Technology, Vol. 19, No. 6, pp. 1717-1726, 2018.
- S.M. Amini, A. Karimi and M. Esnaashari, “Energy-Efficient Data Dissemination Algorithm based on Virtual Hexagonal Cell-Based Infrastructure and Multi-Mobile Sink for Wireless Sensor Networks”, Journal of Supercomputing, Vol. 76, No. 1, pp. 150-173, 2020.