Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Seonghoon Jang"'
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract Generally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector–matrix multiplication between the input bias signal and th
Externí odkaz:
https://doaj.org/article/fab889d7777348f9b33509d3c52379b5
Autor:
Kyuho Lee, Seonghoon Jang, Kang Lib Kim, Min Koo, Chanho Park, Seokyeong Lee, Junseok Lee, Gunuk Wang, Cheolmin Park
Publikováno v:
Advanced Science, Vol 7, Iss 22, Pp n/a-n/a (2020)
Abstract Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential appli
Externí odkaz:
https://doaj.org/article/def373e6d75a4dee922858799f66e95f
Publikováno v:
Journal of Construction Automation and Robotics. 1:31-37
Autor:
Woong Huh, Donghun Lee, Seonghoon Jang, Jung Hoon Kang, Tae Hyun Yoon, Jae‐Pil So, Yeon Ho Kim, Jong Chan Kim, Hong‐Gyu Park, Hu Young Jeong, Gunuk Wang, Chul‐Ho Lee
Publikováno v:
Advanced Materials.
Autor:
Youngwoo Kim, Cheolmin Park, Junseok Lee, Hyunhaeng Lee, HoYeon Kim, Seonghoon Jang, Gunuk Wang, Kyu-Ho Lee, Seung Won Lee
Publikováno v:
ACS Nano. 15:20116-20126
Extrasensory neuromorphic devices that can recognize, memorize, and learn stimuli imperceptible to human beings are of considerable interest in interactive intelligent electronics research. This study presents an artificially intelligent magnetorecep
Autor:
Jingon, Jang, Sanggyun, Gi, Injune, Yeo, Sanghyeon, Choi, Seonghoon, Jang, Seonggil, Ham, Byunggeun, Lee, Gunuk, Wang
Publikováno v:
Advanced science (Weinheim, Baden-Wurttemberg, Germany). 9(22)
Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO
Autor:
Jung Hwan Moon, Kyoung J. Lee, Sanghyeon Choi, Hu Young Jeong, Seonghoon Jang, Gunuk Wang, Jong Chan Kim, Peong-Hwa Jang
Publikováno v:
NPG Asia Materials, Vol 10, Iss 12, Pp 1097-1106 (2018)
The human brain intrinsically operates with a large number of synapses, more than 1015. Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for whic
Autor:
Chanho Park, Seokyeong Lee, Junseok Lee, Cheolmin Park, Kyu-Ho Lee, Seonghoon Jang, Kang Lib Kim, Gunuk Wang, Min Koo
Publikováno v:
Adv Sci (Weinh)
An artificially intelligent electronic skin that is capable of sensing and learning tactile stimuli was developed by Gunuk Wang, Cheolmin Park, and co‐workers in article number 2001662. This ferroelectric field effect transistor platform can implem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09a577225874c1cf2f060b8627150d87
https://europepmc.org/articles/PMC7675045/
https://europepmc.org/articles/PMC7675045/
Autor:
Seonghoon Jang, Kyu-Ho Lee, Gunuk Wang, Chanho Park, Kang Lib Kim, Seokyeong Lee, Min Koo, Junseok Lee, Cheolmin Park
Publikováno v:
Advanced Science
Advanced Science, Vol 7, Iss 22, Pp n/a-n/a (2020)
Advanced Science, Vol 7, Iss 22, Pp n/a-n/a (2020)
Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications i
Publikováno v:
Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Generally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector–matrix multiplication between the input bias signal and the analog