College of Engineering, Pune

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

IoT Based Health Monitoring System Using Raspberry Pi

Abstract: Health has become one of the global challenges for humanity. Cardiac diseases, Lung failures and heart related diseases are increasing at a rapid rate. Monitoring health of elderly people at home or patients at hospitals is necessary but it requires constant observation of Practitioners and Doctors. Information Technology (IT) and its growing applications are performing major role in making human life easier. Internet of Things (IoT) is transforming healthcare and the role of IT in healthcare. IoT consists of physical devices, such as sensors and monitoring devices for patients (glucose, blood pressure, heart rate & activity monitoring, etc) to connect to the internet and transforms information from the physical world into the digital world. The proposed system, with the help of IoT's such features, will help to keep the necessary details and reports of a patient organized and available to all actors in the system. IoT devices like low power sensors will be used to collect data from patients and it will be displayed using LCD and stored on any personal computer and also on the cloud so that any actor in the system can refer to it.

Vibration control of bridge subjected to multi-axle vehicle using multiple tuned mass friction dampers

Abstract: The effectiveness of tuned mass friction damper (TMFD) in reducing undesirable resonant response of the bridge subjected to multi-axle vehicular load is investigated. A Taiwan high-speed railway (THSR) bridge subjected to Japanese SKS (Salkesa) train load is considered. The bridge is idealized as a simply supported Euler–Bernoulli beam with uniform properties throughout the length of the bridge, and the train’s vehicular load is modeled as a series of moving forces. Simplified model of vehicle, bridge and TMFD system has been considered to derive coupled differential equations of motion which is solved numerically using the Newmark’s linear acceleration method. The critical train velocities at which the bridge undergoes resonant vibration are investigated. Response of the bridge is studied for three different arrangements of TMFD systems, namely, TMFD attached at mid-span of the bridge, multiple tuned mass friction dampers (MTMFD) system concentrated at mid-span of the bridge and MTMFD system with distributed TMFD units along the length of the bridge. The optimum parameters of each TMFD system are found out. It has been demonstrated that an optimized MTMFD system concentrated at mid-span of the bridge is more effective than an optimized TMFD at the same place with the same total mass and an optimized MTMFD system having TMFD units distributed along the length of the bridge. However, the distributed MTMFD system is more effective than an optimized TMFD system, provided that TMFD units of MTMFD system are distributed within certain limiting interval and the frequency of TMFD units is appropriately distributed.

Fractional order sliding mode control for single link flexible manipulator

Abstract: A fractional order sliding mode control (FOSMC) scheme for control of a single link flexible manipulator is proposed in this paper. Fractional order sliding mode controller is designed and its performance is compared with the traditional sliding mode controller. Here, `fractional' refers to the inclusion of fractional derivatives in the differential equation governing the dynamics of the switching surface. The control law of the proposed FOSMC scheme is designed using Lyapunov stability analysis. Simulation results demonstrate that the proposed FOSMC not only achieves better control performance with smaller chattering than that with integer order sliding mode control, but also is robust to external load disturbance and parameter variations.

Handwritten Devanagari Character Classification using Deep Learning.

Abstract: Since past few years, deep neural networks, because of their outstanding performance, are getting highly used in computer vision and machine learning tasks such as regression, segmentation, classification, detection, pattern recognition etc. Recognition of handwritten Devanagari characters is challenging task, but Deep learning can be effectively used as a solution for various such problems. Person to person variations in writing style makes handwritten character recognition one of the most difficult tasks. In this Experiment, we successfully tried to classify handwritten Devanagari characters using transfer learning mechanism with the help of Alexnet. Alexnet, a convolutional neural network, is trained over a dataset of around 16870 samples of 22 consonants of Devanagari script which shows impressive results. The transfer learning helps to learn faster and better even if the data samples are less as compared with the training a CNN from scratch.

Disturbance observer based speed control of PMSM using fractional order PI controller

Abstract: In this paper, fractional order PI ( FOPI ) control is developed for speed control of permanent magnet synchronous motor ( PMSM ). Designing the parameters for FOPI controller is a challenging task, especially for nonlinear systems like PMSM. All three PI controllers in the conventional vector controlled speed drive are replaced by FOPI controllers. Design of these FOPI controllers is based on the locally linearized model of PMSM around an operating point. This operating point changes with the load torque. The novelty of the work reported here is in use of Non Linear Disturbance Observer ( NLDO ) to estimate load torque to obtain this new operating point. All three FOPI controllers are then designed adaptively using this new operating point. The scheme is tested on simulation using MATLAB / SIMULINK and results are presented.