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.

Weighted Hybrid Decision Tree Model for Random Forest Classifier

Abstract: Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

Significant enhancement in quality factor of Zn2TiO4 with Cu-substitution

Abstract: Zinc orthotitanate (Zn1−xCux)2TiO4, 0 ≤ x ≤ 0.20, spinel samples were prepared by solid state reaction and sintering. 1060 °C was the optimal sintering temperature for all the Cu-substituted samples which showed sintered density ≥94%, and maximum unloaded quality factor (Quf). SEM microphotographs revealed fairly uniform grains between 2 and 20 μm depending upon the composition. Microwave measurements at ∼7.1–7.5 GHz on these samples (as dielectric resonators) showed with Cu-substitution a steep increase in Quf value from 2100 GHz (for Zn2TiO4, x = 0) to 15,200 GHz (for x = 0.10), a significant jump of over 7 times, while e ′ r was ∼20 (for x = 0) and ∼18 (x = 0.10). The jump in Quf in Cu-substituted samples was attributed partly to improved sintered density and partly to absence of paramagnetic Ti3+, as revealed by electron spin resonance spectra at 9.1 GHz.

Robust Control of Flexible Joint Robot Manipulator

Abstract: A feedback linearization based control is employed for the rigid link model of a robot having joint flexibility and the effect of joint flexibility is treated as a disturbance. A novel Uncertainty and Disturbance Estimator (UDE) is then used to estimate the disturbance to robustify the feedback linearizing controller. As only motor side variables are used in controller, the number of sensors are reduced. To demonstrate the effectiveness of the design, numerical simulations are carried out by considering parametric uncertainties and joint flexibility and the results are presented.

State and Disturbance Observer-Based Integral Sliding Mode Controlled Boost DC–DC Converters

Abstract: In this brief, a recent result on disturbance observer-based integral sliding mode control of boost dc-dc converters is extended by proposing a state and disturbance observer to estimate inductor current in the presence of an unknown load. The control objective is to track the output voltage under varying load and varying reference voltage. The stability of state and disturbance observer is proved by Kharitonov’s theorem. Extensive simulation and experimentation are carried out to assess the viability of practical implementation of the proposed observer−controller combination.

Smart Poultry Farm Automation and Monitoring System

Abstract: A combination of wireless sensors and GPRS network can be used for controlling and monitoring environmental parameters in a poultry farm. Various environmental parameters like temperature, humidity, ammonia gas have a big role in operations of Poultry. Operator can get updates regarding the internal environmental situation of poultry farm by accessing the data using a web page. A combination of hardware and software is used which will initiate the action automatically to control the environmental parameters according to preset standards, if there are any changes in parameters which exceed the presets system will act automatically and help to take actions to control the environmental parameters. Sensors are used to control temperature, water level, smoke, gas and food dispensing. All these sensors are connected with the raspberry pi which can control and monitor all data. The data is transmitted using GPRS, and detailed record of poultry farm with status of environmental conditions is maintained at a webpage. System thus focuses on maintaining best possible environmental conditions with minimal human effort.