Institution
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.
Topics: Cluster analysis, Control theory, Response surface methodology, Wireless sensor network, Ultimate tensile strength
Papers published on a yearly basis
Papers
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TL;DR: In this article, thermal management plays a vital role in electronic system design, and the temperature of the electronic components should not exceed manufacturer-specified temperature levels in order to meet the requirements of the users.
Abstract: In the current scenario, thermal management plays a vital role in electronic system design. The temperature of the electronic components should not exceed manufacturer-specified temperature levels ...
6 citations
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TL;DR: Modifying the design of the traditionally employed wooden squeegee and evaluating its effect on muscle activity during HSP process reduces muscle activity of sternocleidomastoid (SCM), upper trapezius (bilateral) and infraspinatus ( bilateral) muscles during H SP process thereby reduces the risk of neck and shoulder injuries.
Abstract: Hand screen printing process (HSP) is a highly labour intensive work involving repetitive and awkward motion without proper ergonomic tools. The main purpose of this study is to modify the design of the traditionally employed wooden squeegee and evaluate its effect on muscle activity during HSP process. Twenty hand screen printing industry workers participated in the study and printed the design on 30-metre length cloth using both traditional and modified squeegee. Surface electromyography (SEMG) is used to record the muscle activity of sternocleidomastoid (SCM), upper trapezius (bilateral) and infraspinatus (bilateral) muscles during HSP process. One way ANOVA and chi square test is performed using SPSS version 21.0 and the result showed that the data obtained using both traditional and modified squeegee are statistically significant. Introducing modified squeegee in HSP process reduces muscle activity of sternocleidomastoid (SCM), upper trapezius (bilateral) and infraspinatus (bilateral) muscles during HSP process thereby reduces the risk of neck and shoulder injuries.
6 citations
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01 Mar 2019TL;DR: To improve exploration capability of SSA, mutation method is developed to find the optimal value for kernel parameter and penalty parameter values and the preliminary result indicates Mutated SSA with SVM increases classification accuracy than simple SSAWith SVM.
Abstract: Support Vector Machine (SVM) is typically a supervised learning algorithm that carefully examines input and identifies distinct patterns. The function of SVM classifier relies on adjusting or controlling of kernel and penalty parameter values. Nature Inspired Algorithm helps to solve the natural problems and has been attracting considerable attention due to their better performance. Salp Swarm Algorithm (SSA) is a Nature Inspired Algorithm (NIA) which is used to control the finest SVM parameters value. To improve exploration capability of SSA, mutation method is developed to find the optimal value for kernel parameter and penalty parameter. The preliminary result indicates Mutated SSA with SVM increases classification accuracy than simple SSA with SVM.
6 citations
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TL;DR: In this paper, shot peening treatment is used to enhance the wear resistance ability of the composites of AL7075 hybrid metal matrix composites and the results reveal that the shot-peening treatment increases micro-hardness and surface roughness up to 90
6 citations
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TL;DR: The main objective of the proposed system is to optimize the primary performance criterions such as area and power to be most adaptable for low power applications with superior performance in register designs.
6 citations
Authors
Showing all 2001 results
Name | H-index | Papers | Citations |
---|---|---|---|
Thalappil Pradeep | 76 | 581 | 24664 |
Kumarasamy Thangaraj | 47 | 361 | 11869 |
Pagavathigounder Balasubramaniam | 46 | 268 | 6935 |
J. Prakash Maran | 34 | 56 | 3636 |
S. Saravanan | 30 | 209 | 3308 |
Rathanasamy Rajasekar | 23 | 86 | 2142 |
V. Sivakumar | 23 | 93 | 2265 |
K. Thirugnanasambandham | 21 | 31 | 1759 |
Subramaniam Shankar | 20 | 104 | 1510 |
P. Sivakumar | 19 | 132 | 1464 |
N. Sivarajasekar | 18 | 60 | 1025 |
S. Selvakumar | 18 | 68 | 1155 |
Zaharias D. Zaharis | 17 | 128 | 1179 |
P. Balasubramanie | 16 | 27 | 469 |
P. N. Palanisamy | 16 | 47 | 754 |