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.

Investigation on the energy and exergy efficiencies of a domestic refrigerator retrofitted with water-cooled condensers of shell-and-coil and brazed-plate heat exchangers

Abstract: Domestic refrigerator is one of the major energy-consuming appliances, and the enhancement of its energy efficiency plays a vital role in implementing the energy conservation policies and green building concepts in residential sector. The major strategies used to improve the performance of domestic refrigerators are the replacement of existing components, use of alternative refrigerants and the reduction in condensing pressure. Therefore, in this work, the conventional air-cooled condenser has been replaced with water-cooled condensers such as shell-and-coil and brazed-plate heat exchangers to maintain a low compression ratio and condensing pressure. The performance of a domestic refrigeration system retrofitted with water-cooled condensers has been studied using experimental methods. The result showed that the system with water-cooled condensers reduces the pull-down time and the per day energy consumption by 70% and 3.5%, respectively. Moreover, the proposed system can improve the COP and exergy efficiency by 6.4% and 4.9%, respectively. Compared to shell-and-coil heat exchanger, the system with brazed-plate heat exchanger can reduce irreversibility and TEWI by 3.9% and 3.7%, respectively. In this study, the system with brazed-plate heat exchanger showed better performance than the shell-and-coil heat exchanger for all operating conditions.

Optimization of treatment efficiency of UV/H2O2 process on simulated textile industry wastewater

Abstract: The present study involves optimization of UV/H2O2 process on a simulated dye bath effluent by varying initial H2O2 concentration, pollution load, and pH. The process was optimized by four factors and three levels of Box–Behnken design coupled with response surface methodology. During the experiment, color removal and degradation studies were also performed to ensure the treatment efficiency. The results obtained show that the color removal efficiency can be achieved within a short time due to the degradation of the structure which is more susceptible to oxidation. Then slower and gradual degradation of the simple and conjugated aromatic compounds takes place. In the first 5 min of the irradiation of the dyes, the pH value decreased from 11 to 9.5 and for another pH value decreased from 9 to 7.01. The drop in the pH value is mainly due to the formation of organic as well as inorganic acid as a degradation product. Under the optimum operating conditions such as pollution load of 64%, initial concen...

Computer aided detection and classification of mammogram using self-adaptive resource allocation network classifier

Abstract: This study presents a computer aided system for automatic detection and classification of breast cancer in mammogram images. First the suspicious region or the Region of Interest is identified and extracted using Intuitionistic Fuzzy C-Means Clustering technique. Next multilevel Discrete Wavelet Transformation is applied to the extracted Region of Interest. After applying Discrete Wavelet Transformation, histogram features, Gray Level Concurrence wavelet features, and wavelet energy features are extracted from each Region of Interest of the image. Before classification, Principal Component Analysis is applied on the extracted features to reduce the feature dimension. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification. The proposed system is verified with 295 mammograms in the Mammographic Image Analysis Society Database. The result shows that the proposed algorithm produces better results.

A Cluster Based Resource Allocation Architecture for mobile grid environments

Abstract: The mobility issue in grid environments has established new challenges to the research communities particularly in the areas of scheduling, adaptation, security and mobility. Especially, the resource allocation becomes more challenging when mobility is considered in grid environment. Hence it is necessary to consider the mobility of users along with the resource availability while scheduling the resources for the execution of jobs. In this paper, we propose to design a Cluster Based Resource Allocation Architecture (CBRA) which includes resource monitoring and scheduling operations for mobile grid. In this architecture, the Mobile Grid is divided into clusters. Each cluster has one cluster head (CH). A master server (MS) controls each local clusters and has frequent updates of all the CH information. Each CH has a monitoring agent (MA) which will periodically predict the mobility of the cluster nodes and monitor the resource availability and update their values. When the MS forwards the job request of a user to the ideal CH, the CH schedules the jobs based on the predicted time for resource availability and sufficiency of the resources. By simulation results, we show that our proposed architecture achieves good success ratio and throughput with the reduced delay and energy consumption.

High Utility Infrequent Itemset Mining Using a Customized Ant Colony Algorithm

Abstract: Itemset mining is a popular extension to the frequent pattern mining problem in data mining. Finding infrequent patterns, however, has gained its importance due to proven utility in the areas of web mining, bioinformatics and others. High utility mining refines the problem focus to identifying business-relevant transaction patterns that take purchase quantities and monetary considerations into account, like unit price and cost, typically to identify patterns of profit potential. High utility infrequent itemset mining unveils rare cases of highly profitable itemsets. This paper proposes a customized Ant colony algorithm for the efficient discovery of high utility infrequent itemsets. The mining performance of proposed algorithm is analyzed on four real time datasets namely chess, food mart, mushroom and retail.