Kongu Engineering College

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

L 1 norm based pedestrian detection using video analytics technique

Abstract: Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improvement in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extraction, and classification. In spite of giving entire information into feature extraction, the system gives only a useful information (foreground image) by twin background model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For feature extraction, histogram of orientation gradient (HOG) L1 normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized.

Comparative analysis of line-start permanent magnet synchronous motor and squirrel cage induction motor under customary power quality indices

Abstract: Super premium efficiency (IE4) line-start permanent magnet synchronous motor (LSPMSM) is one of the good alternatives to the squirrel cage induction motor (SCIM), especially for a line-start application. Motor performance parameters like efficiency, input power consumption, power factor, starting behavior and torque ripple are affected primarily because of the customary power quality indices. The problems originate due to furnaces, power electronic converters, nonlinear loads, digital controllers, modern electrical equipments and lively load variations. Hence, the performance analysis of LSPMSM and SCIM is essential to understand the motor behavior. Voltage harmonic distortion, voltage unbalance factor and long-duration voltage variation are considered in this study. Customary power quality indices (CPQI) are collected using power and energy analyzer in a sugar industry at various measured points. In the present study, 2.2 kW LSPMSM is designed and prototyped for constant load operation. The designed motor and SCIM are used for analyzing the performance using finite element analysis under the influence of measured CPQI and the effects on its individual performance parameters.

Antipredator Adaptation Shuffled Frog Leap Algorithm to Improve Network Life Time in Wireless Sensor Network

Abstract: A Wireless Sensor Network (WSN) is an interdisciplinary discipline of sensing, electronics and wireless communication employed widely in environmental monitoring and surveillance applications. The sensor nodes are generally tiny and made of irreplaceable power source and limited capacity for computing, communication, and storage. The power constraint necessitates that the WSN routing protocols conserve energy as critical factor to maximize the network lifetime. Cluster-based approaches are popularly used for its energy efficiency where some nodes designated as Cluster Heads (CHs) organize WSNs for data aggregation and energy saving. The CH is responsible for gathering data from the cluster nodes and conveying it to the base station due to which higher energy drain occurs at CH leading to uneven network degradation. Thus, the selection of CH is critical for improving the WSN performance and lifetime. In this paper, a hybrid Shuffled Frog Leaping Algorithm (AASFLA) with antipredator capabilities to avoid the local minima is proposed. Results show avoidance of suboptimal solution compared to SFLA and particle swarm optimization.

Extraction, multi-response analysis, and optimization of biologically active phenolic compounds from the pulp of Indian jamun fruit

Abstract: An aqueous extraction technique was applied to optimize the extraction process variables of temperature (40°C–60°C), time (20–100 min), and the solid-to-liquid ratio (1:10–1:15) using a Box-Behnken experimental design and response surface methodology. Maximum extraction yields of total phenolics and flavonoids, and anti-oxidant activities were obtained from Indian jamun fruit. Effects of the extraction temperature and the solid-toliquid ratio were found to be significant (p<0.05) for all responses. Second order polynomial models were developed from experimental data to predict the effects of the independent variables on the responses. Optimum extraction conditions (temperature of 54°C, time of 50 min, and a solid-to-liquid ratio of 1:8.5) for the maximum extraction yield of total phenolics (1,332.36 mg GAE/100 g) and flavonoids (110.94 mg QE/100 g), and the anti-oxidant activity (233.55mg AAE/100 g) were achieved. Experimental values matched well with predicted values obtained under optimum conditions.