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Inpil Kang
Researcher at Pukyong National University
Publications - 38
Citations - 1839
Inpil Kang is an academic researcher from Pukyong National University. The author has contributed to research in topics: Carbon nanotube & Piezoresistive effect. The author has an hindex of 10, co-authored 38 publications receiving 1691 citations. Previous affiliations of Inpil Kang include University of Cincinnati & Konkuk University.
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A carbon nanotube strain sensor for structural health monitoring
TL;DR: In this paper, a biomimetic artificial neuron was developed by extending the length of the sensor, which is a long continuous strain sensor that has a low cost, is simple to install and is lightweight.
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Introduction to carbon nanotube and nanofiber smart materials
Inpil Kang,Yun Yeo Heung,Jay Kim,Jong Won Lee,Ramanand Gollapudi,Srinivas Subramaniam,Suhasini Narasimhadevara,Douglas Hurd,Goutham R. Kirikera,Vesselin Shanov,Mark J. Schulz,Donglu Shi,Jim Boerio,Shankar Mall,Marina Ruggles-Wren +14 more
TL;DR: The potential use of carbon nanotubes and nanofibers as smart composite materials is discussed in this article, where four applications under development are briefly discussed and a vision is presented for using nanoscale smart materials to synthesize intelligent electronic structures with prescribed elastic and electrical properties for a wide range of new applications.
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Preparation of piezoresistive nano smart hybrid material based on graphene
TL;DR: In this paper, a smart hybrid material called Graphene based nano composites which have piezoresistivity for strain sensor has been presented to address the need for new smart hybrid materials, and the strain response of graphene/epoxy composites showed fairly symmetrical and reversible behavior.
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Preparation and properties of ethylene propylene diene rubber/multi walled carbon nanotube composites for strain sensitive materials
TL;DR: In this article, the composites were prepared with a constant amount of organo-clay and various amounts of multi walled carbon nanotubes (MWCNTs) from 5 to 50 ¼%.
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Structural health monitoring using continuous sensors and neural network analysis
TL;DR: In this paper, a method for damage detection in a plate structure is presented based on strain waves that are generated by foreign object impact on the structure, or by damage that is propagating in the structure.