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.

Data Aggregation in Wireless Sensor Network Using Shuffled Frog Algorithm

Abstract: Wireless Sensor Networks (WSNs) is made of numerous autonomous sensors forming a wireless network and cooperating with one another to transmit sensed data to a base station. With the advent of biomedical sensors, healthcare application for monitoring of vital body signs of patients is developing rapidly wherein all sensors cooperatively send data to the central server. The network routing protocols aims to reduce energy consumption and prolonging network life. Clustering is an important method to prolong network life in WSNs. It involves sensor nodes grouping into clusters and selecting Cluster Heads (CHs). Cluster Heads aggregate data its group and forward accumulated data to base station resulting in a higher energy spend. A big WSN challenge is selecting suitable CHs as they dissipate more energy compared to regular nodes in the network. A popular clustering protocol, LEACH offsets this by probabilistically rotating CHs role among nodes. Nevertheless, network performance may not be optimal if the CHs are not selected appropriately. This paper presents a shuffled frog meta-heuristic algorithm for CHs selection. The proposed method chooses CH based on energy remaining in the nodes. Simulation results shows the proposed technique to outperform LEACH and Genetic Algorithm based methods in terms of Quality of Service.

Combustion characteristics of briquette fuels from sorghum panicle-pearl millets using cassava starch binder.

Abstract: The utilization of agricultural wastes is an attractive and viable option to reduce the environmental pollution and reverse the over-exploitation of fossil fuels. Now-a-days, the usage of fossil fuels has increased manifold causing twin serious problems such as depletion of limited source of fossil fuels and increase in environmental pollution with major consequences. In this study, briquettes were produced using sorghum panicles (SP) and pearl millet (PM) with different ratios (100:0, 10:90, 20:80, 30:70, 40:60, 50:50, 60:40, 70:30, 80:20, 90:10 and 0:100) using cassava starch as a binder with a planned compacting pressure level (200 kN) by exploring hydraulic compression method. The proximate parameters such as water content, level of fixed carbon, ash and volatile matter were determined using American Society for Testing and Materials (ASTM) standard procedures. The elemental analyses (SEM/EDAX) which include carbon (C), oxygen (O), potassium (K), calcium (Ca), chlorine (Cl), sulphur (S), phosphorus (P), aluminium (Al), silicon (Si), magnesium (Mg), cobalt (Co), iron (Fe), zinc (Zn) and sodium (K) were determined in all the briquette samples. Weight loss and optimum heating values of the samples were measured by adopting differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) respectively. In addition to that, the density and compressive strength of all the produced briquettes were determined. In comparison with pongamia–tamarind shell, sorghum panicle–pearl millet briquettes have better fuel properties. The pongamia–tamarind shell fuel has nitrogen and hydrogen whereas sorghum panicle–pearl millet has no identities on both nitrogen and hydrogen content. The occurrence of nitrogen absence is due to non-availability of NOx emissions during combustion. By the cause of more fixed carbon composition, there exists lack in hydrogen content. The sorghum panicle–pearl millet briquettes have better calorific value than pongamia–tamarind shells, and they produce better heating values. Hence, the prepared biomass briquettes are potentially good fuels that derived from agro wastes. Likewise, the determined parameters are compared with the other biomass briquettes.

Heuristically repopulated Bayesian ant colony optimization for treating missing values in large databases

Abstract: The incomplete datasets with missing values are unsuitable for making strategic decisions since they lead to biased results. This problem is even worse when the dataset is large and collected from many heterogeneous sources. The paper deals with missing scenarios which were not dealt together earlier. The proposed Dual Repopulated Bayesian Ant Colony Optimization (DPBACO) handles both ignorable and non-ignorable missing values in heterogeneous attributes of large datasets The DPBACO integrates Bayesian principles with Ant Colony Optimization technique since both are simple and efficient to implement. After pheromone updation, repopulation of the solution pool is done by dividing the population into two based on their fitness values and generating new offsprings by performing crossover operation. The DPBACO algorithm is implemented on six large mixed-attribute datasets for imputing both kinds of missing values. The empirical and statistical results show that DPBACO performs better than other existing methods at variable missing rates ranging from 5% to 50%.

An integrated approach for performance enhancement in automobile repair shops

Abstract: Performance management has been a topic of interest for academicians and practitioners. This paper aims at integrating fuzzy logic, data envelopment analysis (DEA) and fuzzy failure mode and effects analysis (fuzzy FMEA) for enhancing the performance in automobile (car) repair shops. A questionnaire with linguistic terms has been used to collect the data related to customer's perceived service quality and the perceptions are quantified using triangular fuzzy numbers. The weights for the service quality parameters considered are gained through analytical hierarchy process (AHP). Fuzzy perceived quality score is then calculated by combining the fuzzy numbers of criteria with the corresponding weights. Then the service performance is measured using DEA by considering both quantitative and qualitative measures. The results from DEA model provide the relative efficiency measure and efficient input/output targets for each repair shop. For further improvement of the service process, fuzzy FMEA is used to prioritise the potential failure modes. Based on the calculated fuzzy risk priority numbers (FRPN) remedial actions can be initiated.

An integrated closed-loop model for service performance management

Abstract: With service industries sprouting at an incredible rate, measuring and improving service performance becomes an essential strategy for success and survival in today's competitive situation. In this paper, a new closed-loop model has been proposed to measure and improve the service performance using cost, time and service quality as dimensions. The relative service performance has been measured by the Extended Brown?Gibson (EBG) model by taking cost, time and service quality dimensions into consideration. The service quality factors have been evaluated using the Analytical Hierarchy Process (AHP). Quality Function Deployment (QFD) has been used to redesign the existing services when performance measure falls below the satisfactory level. The developed model quantifies service performance and ensures the service process improvement. A case study using data from car repair shops illustrates the usability of the model. The case study shows that the model can be used to gain service process understanding and to identify significant factors for redesigning.