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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.
Papers
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Journal ArticleDOI
Piezoresistive Characteristics of Nanocarbon Composite Strain Sensor by Its Longitudinal Pattern Design
Sung-Yong Kim,Baek-Gyu Choi,Gwang-Won Oh,Chan-Jung Kim,Young-Seok Jung,Jin-Seok Jang,Kwan-Young Joung,Junho Suh,Inpil Kang +8 more
TL;DR: In this paper, a simple design to improve NCSS (nanocarbon composite strain sensor) sensitivity by using its geometric pattern at a macro scale was proposed for an engineering feasibility study, and the results showed that the longer sensor length results in a larger change of resistance due to its piezoresistive unit summation.
Proceedings ArticleDOI
Development of a spoke type novel joint torque sensor by using nano carbon strain sensors
TL;DR: A preliminary on going work to develop a novel spoke type joint torque sensor of robots by using multi-walled carbon nanotube (MWCNT) strain sensors using a nanocomposite solution casting process to fabricate a liquid type strain sensor which can be easily installed on complicate surfaces.
Journal ArticleDOI
A Study on the Development of a Novel Pressure Sensor based on Nano Carbon Piezoresistive Composite by Using 3D Printing
Sung Yong Kim,Inpil Kang +1 more
Proceedings ArticleDOI
Carbon nano artificial neuron system for flexible tactile sensing
TL;DR: To mimic tactile system, ANMS (Artificial Neuron Matrix System) consisting the array of neurons and develop tactile algorithm to localize the contact information with a signal processing system are designed.
Proceedings ArticleDOI
Health monitoring method for plate structures using continuous sensors and neural network analysis
TL;DR: In this article, a method for impact and damage detection on a plate using strain responses from long continuous sensors and analysis by a neural network technique was presented and verified by numerical simulation.