Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Jin‐Woong Lee"'
Publikováno v:
Agriculture, Vol 14, Iss 4, p 544 (2024)
In this study, crank-locker kinematic equations were used to analyze the three-point hitch behavior when the dynamometer was connected to the work machine. The dynamometer was statically tested with a hydraulic actuator, and the accuracy of the three
Externí odkaz:
https://doaj.org/article/86b1ec7767874f34969ebe03b8d6999f
Publikováno v:
Advanced Intelligent Systems, Vol 5, Iss 9, Pp n/a-n/a (2023)
A deep learning (DL)‐based approach for analysis is proposed. Using synthetic XRD data for a DL approach is inevitable due to the lack of real‐world XRD data. There are two main challenges when conducting a DL‐based XRD analysis: generating rea
Externí odkaz:
https://doaj.org/article/b5b1013807f5469095ddeab97b7a6ce1
Parameter Study for Establishing a Synchronizer Control Strategy in Tractor Dual-Clutch Transmission
Publikováno v:
Agriculture, Vol 14, Iss 2, p 218 (2024)
Research on dual-clutch transmissions in agricultural tractors is on the rise. Dual-clutch transmissions provide a smooth driving experience and easy operation compared to manual transmissions, as they eliminate power interruptions. Accordingly, ther
Externí odkaz:
https://doaj.org/article/e1f7b2886b7e464eaef43c78032a56b9
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-12 (2022)
Abstract The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic (PV) and light-emitting diode (LED) applications lags far behind their 3D counterparts. Here, we propose a computational strategy for discovering novel p
Externí odkaz:
https://doaj.org/article/9f61d999c3eb460d8d332b1e754f1122
Autor:
Sunggeun Shim, Woon Bae Park, Jungmin Han, Jinhyeok Lee, Byung Do Lee, Jin‐Woong Lee, Jung Yong Seo, S. J. Richard Prabakar, Su Cheol Han, Satendra Pal Singh, Chan‐Cuk Hwang, Docheon Ahn, Sangil Han, Kyusung Park, Kee‐Sun Sohn, Myoungho Pyo
Publikováno v:
Advanced Science, Vol 9, Iss 28, Pp n/a-n/a (2022)
Abstract A tandem (two‐step) particle swarm optimization (PSO) algorithm is implemented in the argyrodite‐based multidimensional composition space for the discovery of an optimal argyrodite composition, i.e., with the highest ionic conductivity (
Externí odkaz:
https://doaj.org/article/f1c1575b7803470c95cd12f7ec56dc19
Autor:
Byung Do Lee, Jin-Woong Lee, Woon Bae Park, Joonseo Park, Min-Young Cho, Satendra Pal Singh, Myoungho Pyo, Kee-Sun Sohn
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 7, Pp n/a-n/a (2022)
Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For thi
Externí odkaz:
https://doaj.org/article/760e235e86aa4ae08c5a79c88276d052
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
Abstract Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established theoretical and empirical models. A data-driven approach
Externí odkaz:
https://doaj.org/article/4f8b5e5a22314369822c383ea96bedd5
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
Abstract Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the i
Externí odkaz:
https://doaj.org/article/97f45af18b5640c5a76a5470b7e82b31
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Identifying the composition of multiphase inorganic compounds from XRD patterns is challenging. Here the authors use a convolutional neural network to identify phases in unknown multiphase mixed inorganic powder samples with an accuracy of nearly 90%
Externí odkaz:
https://doaj.org/article/b35e778496fb46cb99fe1ad0f5d9d40d
Autor:
Min-Young Cho, Jin-Woong Lee, Chaewon Park, Byung Do Lee, Joon Seok Kyeong, Eun Jeong Park, Kee Yang Lee, Kee-Sun Sohn
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 1, Pp n/a-n/a (2022)
The revolutionary concept of creating a large‐area tactile sensor by training a simple, bulky material through deep learning (DL) is proposed. This enables the replacement of the conventional tactile sensor comprising a patterned array structure wi
Externí odkaz:
https://doaj.org/article/a8e9724d94dd47359ee8cc208af66834