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Institution

College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.


Papers
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Proceedings ArticleDOI
01 Dec 2016
TL;DR: In this paper, a brushless DC motor is compared on two different inverter control strategies, i.e., Sinusoidal and square wave, individually applied to the motor and characteristics are studied.
Abstract: The aim of this paper is to compare performances of brushless DC (BLDC) motor on two different inverter control strategies. Sinusoidal and square wave are individually applied to the motor and characteristics are studied. A prototype inverter fed BLDC motor drive is designed and developed. DSP dsPIC30F3011 along with MPLAB IDE software is used for generation of firing pulses for each type of inverter control strategy. The drive is tested first on square wave inverter fed supply and then on state vector modulation (SVM) inverter fed supply. The proposed control strategies are simulated in MATLAB/Simulink and simulation results are validated with hardware. Experimental and simulated results are also compared for performances of complete BLDC motor drive.

11 citations

Book ChapterDOI
01 Jan 2020
TL;DR: This system puts forth the home automation technique for smart plant watering system that uses Node ESP8266 as the microcontroller interfacing unit and a smartphone empowers the user to be updated with their current garden status using IoT from any part of the world.
Abstract: In this modern era of fast-moving technology, we can do things which we could never do before and to do these tasks there is a necessity to build a platform to perform these tasks. The proposed system puts forth the home automation technique for smart plant watering system. A smartphone empowers the user to be updated with their current garden status using IoT from any part of the world. The system uses Node ESP8266 as the microcontroller interfacing unit.

11 citations

Proceedings ArticleDOI
01 Jan 2016
TL;DR: The velocity equations of the each wheel of the drive required to navigate the path are explained and the robot moves to the destination point along the specific path with desired velocity.
Abstract: This paper concentrates on navigation of four wheel omni drive robot system with velocity control. The system has two feedback systems, viz. rotary encoders and laser distance sensors. The following paper assumes that these paths can be defined mathematically in terms of curves like straight line, circle, ellipse or any other mathematically defined curve or as a part of these curves. Two feedback systems enable the robot to navigate the specific path accurately and reliably. This paper explains the velocity equations of the each wheel of the drive required to navigate the path. The laser distance sensor system assists the robot to maintain its trajectory by providing real time position of the robot. The closed loop system of PID controller and rotary encoder provides the required accurate velocity to each wheel which is calculated according to the mathematical equations. As a result the robot moves to the destination point along the specific path with desired velocity.

11 citations

Journal ArticleDOI
TL;DR: Second order sliding mode control is proposed for liquid level control in a quadruple tank system that has internal cross coupling in its four tank setup, and realizes in a nonlinear model.

11 citations

Journal ArticleDOI
TL;DR: A stacked ensemble meta-learning approach for customized convolutional neural network is proposed for Marathi handwritten numeral recognition and it overpowers the average ensemble because the weighted and maximum contribution of each pipeline is taken in this approach.
Abstract: Pattern Recognition is the method of mapping the inputs to their respective target classes based on features of data. In this paper a stacked ensemble meta-learning approach for customized convolutional neural network is proposed for Marathi handwritten numeral recognition. Stacked ensemble merges the pre-trained base pipe lines to create a multi-head meta-learning classifier that outputs the final target labels. It overpowers the average ensemble because the weighted and maximum contribution of each pipeline is taken in this approach. The stacked ensemble meta-learning classifier proves to be efficient because the base pipelines, which are already acquainted with output desirable results, are concatenated, instead of averaging, to achieve maximum efficiency. Performance evaluation and analysis have been done on Marathi handwritten numeral dataset, and the experiment results are better than the existing proposed systems.

11 citations


Authors

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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202227
2021491
2020323
2019325
2018373
2017334