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: The objective of this article is to determine the effect of various radial clearance values over the contact pressure and wear of the hard-on-hard—that is, polycrystalline diamond (PCD)—hip prostheses using finite element concepts for normal walking conditions.
Abstract: Wear of the bearing surface increases the revision rate of artificial hip replacements and is influenced by the radial clearances between the acetabulum cup and the femoral head. The objective of this article is to determine the effect of various radial clearance values over the contact pressure and wear of the hard-on-hard—that is, polycrystalline diamond (PCD)—hip prostheses using finite element concepts for normal walking conditions. The wear of the hip bearing surface is determined by considering the contact pressures obtained from the hip gait instants of normal walking activity and sliding distance determined from the three-dimensional hip gait motions. The radial clearance value of 0.05 mm showed less volumetric wear rate when compared with other radial clearance values. Overall, it is recommended that the low radial clearance between the contacting pair is suitable for PCD-on-PCD hip prostheses.
18 citations
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TL;DR: Second order polynomial equation was developed and its adequacy was analyzed by analysis of variance (ANOVA) and the optimal extraction was found to be as follows; microwave power, extraction time, temperature, pH and mass of the sample.
18 citations
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TL;DR: The DEA method was applied to find the overall efficiency, Technical Efficiency and Scale Efficiency of SOEUs in India and the Most Productive Scale Size (MPSS) is calculated for the scale inefficient utility.
Abstract: In this paper two different DEA models were applied to evaluate the relative efficiency of State Owned Electric Utilities (SOEUs) in India. The DEA method was applied to find the overall efficiency, Technical Efficiency and Scale Efficiency. The Most Productive Scale Size (MPSS) is calculated for the scale inefficient utility. The results and discussions of this paper can be used to assist the authorities to pave the way for the improvement in technical and scale efficiency.
18 citations
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TL;DR: This paper used competitive learning networks and unsupervised data clustering methods to model the differential grading in childhood autistic rating scale CARS-based assessment.
Abstract: The application of different artificial intelligence models in clinical decision support systems has been a research topic which mainly focuses on the diagnosis method. In this paper we describe the application of unsupervised machine learning models in decision supportive tools for predictive grading of autistic disorder. We used competitive learning networks and unsupervised data clustering methods to model the differential grading in childhood autistic rating scale CARS-based assessment. Modelling of conventional score-based assessment using unsupervised learning methods is the novelty in this work. Self-organisation feature map SOM with single input and four output units perform with a predictive ability of 100% during resubstitution testing.
18 citations
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TL;DR: In this paper, the mechanical properties of polyester matrix composites reinforced with coir/SLS fibres were assessed and the fracture toughness of the composites were also studied.
18 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 |