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Your search keyword '"Cheng, Yonghong"' showing total 400 results
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400 results on '"Cheng, Yonghong"'

1. In-situ observation of field-induced nano-protrusion growth on a carbon-coated tungsten nanotip

Author

Meng, Guodong, Li, Yimeng, Koitermaa, Roni Aleksi, Zadin, Veronika, Cheng, Yonghong, and Kyritsakis, Andreas

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract

Nano-protrusion (NP) on metal surface and its inevitable contamination layer under high electric field is often considered as the primary precursor that leads to vacuum breakdown, which plays an extremely detrimental effect for high energy physics equipment and many other devices. Yet, the NP growth has never been experimentally observed. Here, we conduct field emission (FE) measurements along with in-situ Transmission Electron Microscopy (TEM) imaging of an amorphous-carbon (a-C) coated tungsten nanotip at various nanoscale vacuum gap distances. We find that under certain conditions, the FE current-voltage (I-V) curves switch abruptly into an enhanced-current state, implying the growth of an NP. We then run field emission simulations, demonstrating that the temporary enhanced-current I-V is perfectly consistent with the hypothesis that a NP has grown at the apex of the tip. This hypothesis is also confirmed by the repeatable in-situ observation of such a nano-protrusion and its continued growth during successive FE measurements in TEM. We tentatively attribute this phenomenon to field-induced biased diffusion of surface a-C atoms, after performing a finite element analysis that excludes the alternative possibility of field-induced plastic deformation.

Published
2024

4. FEcMD: A multi-physics and multi-scale computational program for electron emission characteristics dynamically coupled with atomic structure in metal nano-emitters

Author

Li, Nan, Gao, Xinyu, Feng, Xianghui, Wu, Kai, Cheng, Yonghong, and Xiao, Bing

Subjects
Physics - Computational Physics
Abstract

Field emission coupled with molecular dynamics simulation (FEcMD) software package is a computational tool for studying the electron emission characteristics and the atomic structure evolution of micro- and nano-protrusions made of pure metals or multi-component alloys by means of multi-physics and multi-scale methodology. The implementations of molecular dynamics, the electrodynamics, and the heat conduction in FEcMD program are addressed. For molecular dynamics simulation, the Lennard-Jones potentials, embedded atomic method (EAM), and moment tensor potentials (MTP) are fully supported for both alloys and pure metals. In the electrodynamics, the FEcMD program incorporates the space charge fields (space charge potential and exchange-correlation effects) in the Wentzel-Kramers-Brillouin-Jeffreys (WKBJ) approximation to evaluate the field emission current density more reliably for nano-gaps between two metal electrodes. Additionally, the advanced two-temperature heat conduction model is implemented in FEcMD program, and which provides more reliable descriptions for the temperature evolutions of electron and phonon subsystems under the radiofrequency (RF) or pulse electric fields. Comprehensive benchmark tests are performed for each module in FEcMD software to validate the numerical results, and also to access the accuracy and efficiency of the implemented algorithms. Finally, some typical applications of FEcMD program are also demonstrated for understanding the evolution of temperature and electric field coupled with the dynamic changing of atomic structures for metal micro- and nano-protrusions.

Published
2023

6. Statistical Analysis of Electrothermal Damage Characteristics of Nanoelectrode Under High Electric Fields

Author

Gao, Xinyu, Feng, Fei, Teng, Guofei, Zhao, Jun, Xiao, Bing, Cheng, Yonghong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li, editor

Published
2024
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11. Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction

Author

Qin, Yuwen, Cai, Ningbo, Gao, Chen, Zhang, Yadong, Cheng, Yonghong, and Chen, Xin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract

The precise estimate of remaining useful life (RUL) is vital for the prognostic analysis and predictive maintenance that can significantly reduce failure rate and maintenance costs. The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction. The Temporal deep degradation network (TDDN) model is proposed to make the RUL prediction with the degradation-related features given by the one-dimensional convolutional neural network (1D CNN) feature extraction and attention mechanism. 1D CNN is used to extract the temporal features from the streaming sensor data. Temporal features have monotonic degradation trends from the fluctuating raw sensor streaming data. Attention mechanism can improve the RUL prediction performance by capturing the fault characteristics and the degradation development with the attention weights. The performance of the TDDN model is evaluated on the public C-MAPSS dataset and compared with the existing methods. The results show that the TDDN model can achieve the best RUL prediction accuracy in complex conditions compared to current machine learning models. The degradation-related features extracted from the high-dimension sensor streaming data demonstrate the clear degradation trajectories and degradation stages that enable TDDN to predict the turbofan-engine RUL accurately and efficiently.

Published
2022

31. Assessing Mandarin Chinese Students' Interlanguage Pragmatic Competence at the DLIFLC: Results and Pedagogical Implications

Author

Cheng, Yonghong, Wang, Yan, and Chen, Wei

Abstract

Language instruction at the Defense Language Institute Foreign Language Center (DLIFLC), as it does at colleges and universities, focuses on developing linguistic competence. There is no textbook or teaching materials that explicitly and systematically teach students the pragmatic knowledge of the target language. Such knowledge is either introduced by teachers sporadically or learned by students themselves. The current study reports the reality of the Chinese students' interlanguage pragmatic competence by administering a survey questionnaire with 50 situational scenarios that measure interlanguage pragmatic competence (ILP) on Requests, Apologies, Refusals, Compliments, Thanksgivings, Conversational Implicatures, and Formulaic Routines. The survey results demonstrate the students' current level of the pragmatic competence in Chinese language and culture and provide formation for future curriculum revision and material development. The study also explores possible and effective approaches to teaching culture and pragmatics in classrooms at the DLIFLC.

Published
2022

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