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.

Burr formation and its treatments—a review

Abstract: Burrs, being one of the most undesired obstructions generated during machining, affects work piece quality negatively in many aspects. Although deburring removes burrs, this extra process is time consuming, costly and might affect dimensional accuracy. This study investigates mechanisms, effects and variations on burr formation in most common machining processes such as drilling, milling, turning and grinding based on the information available in literature. The problems related to burrs as well as ways and methods to remove burr and control or minimize burr formation has critically discussed. Burrs can be minimised by selecting proper tool geometry, tool materials, coolant, machining parameters, work piece material, process planning and tool path design. As there is no method that can eliminate burr formation, thus deburring is essential to eliminate burrs after machining. Manual tools, abrasive blasting, abrasive flow, magnetic abrasive finishing, centrifugal barrel finishing, thermal melting and electrochemical effect are most commonly used for deburring depending on material, size and precision of parts.

Comparison of Longley-Rice, ITU-R P.1546 and Hata-Davidson propagation models for DVB-T coverage prediction

Abstract: This paper compares precision field-strength measurements taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer with simulation results derived from the ITM coverage prediction model (Irregular Terrain Model), also known as NTIA-ITS Longley-Rice model, in conjunction with the 3-arc-second SRTM (Satellite Radar Topography Mission) geographical data, the propagation predictions of ITU-R Recommendation P.1546 and those of the empirical Hata-Davidson model using HAAT. ITU-R P.1546 and Hata-Davidson models exhibit higher errors at longer distances and therefore necessary corrections should be introduced in the models in order to increase propagation prediction accuracy. Especially, measurements results show that ITU-R P.1546, on average, underestimates the field strength at distances longer than 50 km. The Longley-Rice model using the terrain digital elevations is more accurate, as expected, and its results are closer to the measurement data.

Treatment of egg processing industry effluent using chitosan as an adsorbent

Abstract: The objective of this present study is to investigate the efficiency of chitosan as an adsorbent to treat egg processing industry wastewater. Parameters affecting the effluent treatment process such as pH, chitosan dosage, settling time and initial chemical oxygen demand (COD) concentration on the reduction percentage of turbidity, COD and biochemical oxygen demand (BOD) were studied. Optimum condition was found to be pH of 4, chitosan dosage of 1.1 g L-1 and settling time of 40 min respectively. The maximum reduction percentage of turbidity, COD and BOD were found to be 94 %, 88 % and 83 % respectively. The effective adsorption process was confirmed by FT-IR spectra analysis. The experimental data was analyzed by different isotherm and kinetic models. Langmuir isotherm type I model was satisfactorily described the adsorption mechanism and the rate of COD reduction followed the pseudo-first-order kinetic model. A four factor, three levels Box-Behnken response surface design was employed to develop second order polynomial mathematical models from the experimental data.

Feature selection using Artificial Bee Colony for cardiovascular disease classification

Abstract: Machine learning techniques are widely used in medical decision support systems. Medical diagnosis helps to obtain different features representing the different variations of the disease. With the help of different diagnostic procedures, it is likely to have relevant, irrelevant and redundant features to represent a disease. Redundant features contribute to the wrong classification of the disease. Therefore, removing the redundant features reduces the size of the data and computation complexity. Identifying a good feature subset for effective classification is a non-trivial task. This requires an exhaustive search over the sample space of the dataset. The main objective of this paper is to use a metaheuristic algorithm to determine the optimal feature subset with improved classification accuracy in cardiovascular disease diagnosis. Swarm intelligence based Artificial Bee Colony (ABC) algorithm is used to find the best features in the disease identification. To evaluate the fitness of ABC, Support Vector Machine (SVM) classification is used. The performance of the proposed algorithm is validated against the Cleveland Heart disease dataset taken from the UCI machine learning repository. The experimental results show that, ABC-SVM performs better than Feature selection with reverse ranking. The results also show that, the proposed method obtained good classification accuracy with only seven features.