Zobrazeno 1 - 10
of 216
pro vyhledávání: '"In-taek Kong"'
Autor:
Sajjan Parajuli, Younsu Jung, Sagar Shrestha, Jinhwa Park, Chanyeop Ahn, Kiran Shrestha, Bijendra Bishow Maskey, Tae-Yeon Cho, Ji-Ho Eom, Changwoo Lee, Jeong-Taek Kong, Byung-Sung Kim, Taik-Min Lee, SoYoung Kim, Gyoujin Cho
Publikováno v:
npj Flexible Electronics, Vol 8, Iss 1, Pp 1-11 (2024)
Abstract Despite the roll-to-roll (R2R) gravure printing method emerging as an alternative sustainable technology for fabricating logic circuits based on p- and n-types of single-walled carbon nanotube thin film transistors (p,n-SWCNT-TFTs), the wide
Externí odkaz:
https://doaj.org/article/151ba9bc5f644106ba35b4c425a982e6
Autor:
Jinyoung Choi, Hyunjoon Jeong, Sangmin Woo, Hyungmin Cho, Yohan Kim, Jeong-Taek Kong, Soyoung Kim
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 12, Pp 65-73 (2024)
The artificial neural network (ANN)-based compact model has significant advantages over physics-based standard compact models such as BSIM-CMG because it can achieve higher accuracy over a wide range of geometric parameters. This makes it particularl
Externí odkaz:
https://doaj.org/article/049f485f178443b79577e12895cf46a6
Autor:
Jaemin Son, Joo Young Shin, Seo Taek Kong, Jeonghyuk Park, Gitaek Kwon, Hoon Dong Kim, Kyu Hyung Park, Kyu-Hwan Jung, Sang Jun Park
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided d
Externí odkaz:
https://doaj.org/article/db40dd2c407843a68edf21b13b516105
Autor:
Hyunjoon Jeong, Sangmin Woo, Jinyoung Choi, Hyungmin Cho, Yohan Kim, Jeong-Taek Kong, Soyoung Kim
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 11, Pp 153-160 (2023)
In this paper, we present a fast and expandable artificial neural network (ANN)-based compact model and parameter extraction flow to replace the existing complicated compact model implementation and model parameter extraction (MPE) method. In additio
Externí odkaz:
https://doaj.org/article/0cc50706c7ca472e91db8af1acef6556
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract The keystone perforator island flap (KPIF) is popular in reconstructive surgery. However, despite its versatility, its biomechanical effectiveness is unclear. We present our experience of KPIF reconstruction in the human back and evaluate th
Externí odkaz:
https://doaj.org/article/20ae05b566e74b5b8caf5d147d2601da
Autor:
Jeonghyuk Park, Yul Ri Chung, Seo Taek Kong, Yeong Won Kim, Hyunho Park, Kyungdoc Kim, Dong-Il Kim, Kyu-Hwan Jung
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an a
Externí odkaz:
https://doaj.org/article/cc3a6ff7b5f74169a3a27530a75eb747
Publikováno v:
IEEE Access, Vol 6, Pp 45439-45447 (2018)
We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step
Externí odkaz:
https://doaj.org/article/d9bce5b379d842539b9996be7e375744
Publikováno v:
IEEE Access, Vol 6, Pp 54636-54650 (2018)
In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data
Externí odkaz:
https://doaj.org/article/c724b2fefd43450c9d4ae5f2c578f5c9
Publikováno v:
Aesthetic Plastic Surgery.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61d0620eda0aa69a7f99c07beb2618ea
https://doi.org/10.1007/s00266-023-03318-x
https://doi.org/10.1007/s00266-023-03318-x
Publikováno v:
Journal of Digital Imaging. 35:1061-1068
Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fbf8b951ecaa9069addd173dbb450a9
https://doi.org/10.1007/s10278-022-00608-9
https://doi.org/10.1007/s10278-022-00608-9