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35 results on '"Yang, Dingkun"'

1. Vanishing of Dimits Shift in Realistic Fusion Plasmas with Negative Magnetic Shear

Author

Yang, Dingkun, Li, Shengming, Xiao, Yong, and Lin, Zhihong

Subjects
Physics - Plasma Physics
Abstract

This study employs gyrokinetic simulations to investigate ion temperature gradient (ITG) turbulence in realistic fusion plasmas featuring reverse magnetic shear. Negative magnetic shear is found to suppress the ITG instability due to the scarcity of mode rational surfaces, as evidenced by a comparison of instabilities for different magnetic shears. This suppression effect remains observable in nonlinear turbulence with zonal flow artificially eliminated, where the emergence of turbulence solitons aligns with mode rational surface peaks. However, the suppression effect diminishes in the presence of self-consistently generated zonal flow, along with the occurance of turbulence solitons. The zonal flow is found to originated from a force driven process by the primary instability, instead of the conventional modulational instability. The study further reveals a remarkable phenomenon that the Dimits shift no longer exists for negative magnetic shear, which are attributed to the weakness of zonal flow around marginal stability. However, away from marginal stability, the turbulent transport is primarily regulated by the zonal flow regardless of different magnetic shears., Comment: 10 pages

Published
2024

7. An Edge-Cloud Collaborative Object Detection System

Author

Xu, Lei, Yang, Dingkun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Guojun, editor, Choo, Kim-Kwang Raymond, editor, Ko, Ryan K. L., editor, Xu, Yang, editor, and Crispo, Bruno, editor

Published
2022
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12. An Efficient Framework Based on Semi‐Supervised Learning for Transformer Fault Diagnosis.

Author

Yang, Jiarong, Yang, Dingkun, Bao, Jinshan, Zhang, Jing, He, Yu, Yan, Rujing, Zhang, Ying, and Hu, Kelin

Abstract

Transformer fault diagnosis is very important for the stable operation of transformers, but it is still difficult to recognize transformer faults. The main obstacle is attributed to the lack of labeled data for transformer faults. Due to the limited data of transformer faults, a great deal of unlabeled data in real life is not difficult to obtain and contains a lot of effective information. Semi‐supervised self‐training can significantly enhance the classification accuracy and performance of the base classifier by applying unlabeled data. However, one of the reasons for the degradation of semi‐supervised self‐training is the lack of initial labeled data to train the initial classifier. In this paper, we propose an improved semi‐supervised SMOTE (SS‐SMOTE), which uses the potential information contained in unlabeled data to generate synthetic samples, and then reasonably increases the labeled data set. Meanwhile, we combine SS‐SMOTE with self‐training into an algorithm framework. Finally, a series of experiments are carried out to evaluate the performance of the proposed algorithm, and compared with other existing state‐of‐the‐art semi‐supervised algorithms. The results show that the performance improvement of the classifier using the semi‐supervised improved SMOTE algorithm framework is effective, and has a good effect on solving the problem of small sample classification. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Fault detection of transformers based on Raman spectra of the dissolved gas in transformer oil

Author

Ma Zhipeng, Chen Xingang, and Yang Dingkun

Subjects
Voigt profile, Engineering, Transformer oil, business.industry, Dissolved gas analysis, Gaussian, 030231 tropical medicine, 020206 networking & telecommunications, 02 engineering and technology, Laser, Fault detection and isolation, law.invention, Computational physics, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, law, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, symbols, Transformer, business, Raman spectroscopy
Abstract

Dissolved Gas Analysis (DGA) is one of the best way to monitor the running state of power transformers, but there are some defects of traditional DGA. Different from other DGA, Raman spectra gas detection technology using a single wavelength laser is able to detect dissolved gas in oil-immersed transformer without contact and loss, and it overcame the disadvantages of low accuracy and sensitivity of traditional DGA. With Gaussian View and Gaussian 09w simulation, the Raman spectra of 7 kinds of transformer fault gases have been obtained, and the Voigt function fitting has been made. The establishment of Raman spectroscopy detection platform aiming at analyzing the Raman spectra of fault gases has been completed in the laboratory, which accomplished the mission of improving the single gas Voigt function model of Raman spectra. Comparisons between the simulation values and the experimental values are made, and all the proofs tend that the synthesis data are in accord with the test data.

Published
2016

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