Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Microcontroller Based BLDC Motor Drive for Commercial Applications

Abstract: Brushless DC (BLDC) motors are now a days becoming popular in battery operated vehicles, fuel pumps, medical equipments, printers and in many applications because of its light weight, high operating speed and excellent speed-torque characteristics. However, BLDC motor requires complex and expensive high speed drives and converter circuits to perform electronic commutation and suitable control circuits for implementing control technique. Since the conventional drive circuits are expensive, bulky and more complex, this paper proposes a low cost, compact, high performance BLDC drive system employing solar module with DC–DC converters and Pulse Width Modulation (PWM) control strategy. The proposed drive consists of a solar module, charge controllers, batteries, SEPIC converter and BLDC motor, henceforth developed into the Solar Powered BLDC motor drive and solar powered equipments and three phase inverter containing six MOSFET switches. A microcontroller or DSP will be used to control the overall system. This project explains the study of designing a Solar Powered BLDC Motor Drive which is one of the solutions for the oncoming crisis. The approach of selecting the appropriate components for this application is studied and each of them is simulated.

Design and Analysis of six DOF Robotic Manipulator

Abstract: The robotic manipulators are nowadays used for many applications in the industries. This project involves the design and analysis of a six DOF manipulator for welding, pick and place application. We developed a robot in Solid Works and analysed it motion, load withstanding capacity and path traceability. However, design and analysis of a robot involves modelling of it forward and inverse kinematics. We modelled the forward and inverse kinematics by D-H parameters. The proposed model makes it possible to control the manipulator to achieve any reachable position and orientation in an unstructured environment. The inverse kinematics provided many possible combinations of angles for a single end effector position. A GUI was created in MATLAB for studying the forward and inverse kinematics of the robot. It gave results with precision of 0.2 cm. the load analysis also gave the maximum load it can withstand 200 KN without permanent deformation. The approach presented in this work can also be applicable to solve the kinematics problem of other similar kinds of robot manipulators.

Hybrid Cloud Resource Provisioning (HCRP) Algorithm for Optimal Resource Allocation Using MKFCM and Bat Algorithm

Abstract: The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering Algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

Efficient hybrid multi-level matching with diverse set of features for image retrieval

Abstract: Content-based image retrieval has become popular in the retrieval of images from large image database using reduced human intervention. Researchers are still in need to develop effective systems for dealing many of the wide-scope scientific and medical applications. Past research works have faced a problem on differentiating different images by means of using the single features alone. In this paper, a multi-level matching scheme is introduced for retrieval of image based on a hybrid feature similarity integrating local and global features. Both global- and local-level features included in multi-level scheme are used for image representation. From an image, the color information is extracted globally using a new color, edge directivity descriptor and color-based features. Further, the interest of points from each image is detected using local descriptors called local binary pattern and speeded-up robust features. Using two image databases, the improved retrieval accuracy obtained with the combination of global and local features is analyzed. Experimental outcomes have revealed the effectiveness of proposed system on achieving 91% and 92% precision rates over two datasets compared to other existing methods.