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.

ANN based intelligent control of Induction Motor drive with Space Vector Modulated DTC

Abstract: This paper presents the design of Direct Torque Controlled (DTC) Induction Motor drive that incorporates Artificial Neural Network (ANN) based controller. The control algorithms are employed to improve control performance, and to reduce torque and flux ripple. A new control strategy for Space Vector Modulation based DTC (SVM_DTC) using ANN to generate control vector is proposed. The paper investigates application of ANN to Conventional DTC (C_DTC) and SVM_ DTC. Simulation studies show that the intelligent control technique is able to reduce the torque and flux ripples and to improve the performance of the drive especially at low speed. Proposed ANN_SVM_DTC is less complex, requires a single ANN controller for decoupled torque and flux control, and improves the performance.

Design and simulation of Perturb and Observe Maximum Power Point Tracking using MATLAB/Simulink

Abstract: This paper presents design and simulation of Perturb and Observe (PO) algorithm of Maximum Power Point Tracking (MPPT) for Photo voltaic system. The characteristics of a solar cell are dependent on the environmental parameters like sun light and temperature and are non linear in nature. It's power changes non linearly with respect to the voltage and current. Hence there is only one voltage and current at which Maximum Power Point (MPP) exists for certain climate conditions. MPPT serves the purpose of tracking the MPP. The step by step procedure of solar panel modelling, designing of DCDC boost converter and PO MPPT is given. The solar panel and PO MPPT is simulated under different environmental conditions. The simulation results are discussed.

A Novel Multi-feature Multi-classifier Scheme for Unconstrained Handwritten Devanagari Character Recognition

Abstract: This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multistage feature extraction and classification scheme. The initial stages of feature extraction are based upon the structural features and the classification of the characters is done according to their parameters. The final stage of feature extraction employs Radon transform and Euclidean distance transform and applied to two separate feed forward back propagation neural networks. The hybrid classifier at the final stage takes the input from two neural network classifiers and template matching classifier and decides the final output based on maximum voting rule. This multistage feature extraction and classification scheme improves the recognition accuracy over individual classifiers considerably. The recognition rate achieved from the proposed method is 95.40%.

Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods

Abstract: Agriculture is a foremost field within the world, and it’s the backbone in the Republic of India. Agriculture has been in poor condition. The impact of temperature variations and its uncertainty has engendered the bulk of the agricultural crops to be overripe in terms of their manufacturing. A correct forecast of crop expansion is a vital character in crop forecast management. Such forecasts will hold up the federated industries for accomplishing the provision of their occupation. ML is the method of finding new models from giant information sets. Numerous regressive ways like random forest, linear regression, decision tree regression, polynomial regression, and support vector regression will be used for the aim. Area and production are among the meteorological information that’s made by necessary data. This paper figures out the yield recommendation of the crop by the accurate comparison of numerous machine learning ML regressions where the overall percentage improvement over several existing methods is 3.6%.

Detection of fire using image processing techniques with LUV color space

Abstract: Vision based fire detection system have recently gained popularity as compared to traditional fire detection system based on sensors. The popularity and need of video surveillance at residential, Industrial, public and business locations have supported the widespread use of vision based fire detection system. The colour of fire is the basic technique for identification of fire in an image. However, the colour of fire varies from red, orange, yellow to white. Also, there are non-fire objects with fire-like colour. In order to improve the accuracy of fire detection system, colour detection is combined with various other techniques. Edge detection, motion detection, area covered by flames, existence of smoke, growth of fire and background segmentation are some techniques which are combined by various researchers and used to correctly classify the fire images and fire-like non fire images in a video. There are also various thresholds that are used to differentiate fire in any frame. These thresholds need to be adjusted based on the type of area and its brightness level. Also, the difference in the subsequent frames and area covered by the flames supports the existence of fire if it is greater than the threshold. This paper presents the comparative analysis of five recent vision based fire detection system. These fire detection systems are based on flame colour detection combined with other features such as motion and area of frame. The fire detection system based on LUV colour space and hybrid transforms is proposed.