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380 results on '"Wu, Junyong"'

27. Research on Smart Grid Comprehensive Development Level Based on the Improved Cloud Matter Element Analysis Method

Author

Song, Chengze, Wu, Junyong, Shao, Meiyang, Hao, Liangliang, Liu, Lin, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Li, Kang, editor, Zhang, Jianhua, editor, Chen, Minyou, editor, Yang, Zhile, editor, and Niu, Qun, editor

Published
2018
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28. Research on Refined Load Forecasting Method Based on Data Mining

Author

Xi, Yawen, Wu, Junyong, Shi, Chen, Zhu, Xiaowen, An, Ran, Cai, Rong, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Li, Kang, editor, Zhang, Jianhua, editor, Chen, Minyou, editor, Yang, Zhile, editor, and Niu, Qun, editor

Published
2018
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33. Analysis and Design of Independent DC Bus Structure Multiport Power Electronic Transformer Based on Maximum Power Transmission Capability of Low-Voltage DC Ports.

Author

Li, Junchi, Wu, Junyong, and Xiong, Fei

Subjects
*POWER transformers, *POWER transmission, *BUSES, *DESIGN
Abstract

Owing to the diverse connection configurations of dual active bridge converters, a multiplicity of low-voltage DC port structures are anticipated to emerge in the independent DC bus structure multiport power electronic transformer (IDBS-MPET). An inadequate low-voltage DC port structure exacerbates the power imbalance in IDBS-MPET, presenting a risk of overmodulation even when transmitting relatively low levels of power. To overcome this limitation, a design scheme of IDBS-MPET topology based on the maximum power transmission capability of the low-voltage DC ports is proposed in this paper. Three topology design rules are derived from the maximum power transmission capability results of more than 80 typical IDBS-MPET topologies. The symmetrical triple cross-phase connection structure, the symmetrical double cross-phase connection structure and the single-phase connection structure are sequentially identified as the three most optimal structures of low-voltage DC ports. By employing the proposed design methodology, each low-voltage DC port achieves its maximum power transfer capability relative to other configurations. The effectiveness of the proposed design scheme is validated by an optimal designed IDBS-MPET topology with six low-voltage DC ports. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Research on Model and Method of Maturity Evaluation of Smart Grid Industry

Author

He, Yue, Wu, Junyong, Ge, Yi, Li, Dezhi, Yan, Huaguang, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Li, Kang, editor, Xue, Yusheng, editor, Cui, Shumei, editor, Niu, Qun, editor, Yang, Zhile, editor, and Luk, Patrick, editor

Published
2017
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40. Research on power demand forecasting based on attention mechanism and deep learning network

Author

Zhang Chenglong, Yao Li, Zhang Jinjin, Wu Junyong, Shan Baoguo, and Lan Dong

Subjects
power demand forecasting, attention mechanism, convolutional neural networks, long short-term memory, multiple influencing factors, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

Published
2022
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43. Power System Frequency Safety Assessment Scheme: Multi-Branch Learning Method Based on Ensemble Full Connection

Author

Wu, Junyong and Li, Lusu

Abstract

Facing the frequency safety problem caused by the large-scale application of renewable energy in power systems, this article proposes a frequency safety assessment scheme (FSAS) for power systems based on a novel deep learning structure. Firstly, a novel data preprocessing method is proposed, which takes the feature data blocks of the same attribute as the normalization object, which can effectively improve the performance of the evaluation scheme. Then, referring to the idea of ensemble learning, a multi-branch learning network based on ensemble full connection is designed. This network uses a multi-branch structure to fully mine and learn the deep features from different aspects and uses the ensemble full connection structure to integrate these features and further fit them. Finally, a multi-task FSAS with a parallel structure is proposed, which achieves the dual goals of simultaneously evaluating the Frequency Response Safety Level and Frequency Response Safety Time. Taking the IEEE 39 bus system and the Illinois 200 bus system as examples, both contain renewable energy, the example test proves the effectiveness of the data processing method and the rationality of FSAS structure, and the comparative experiment shows that it has the highest accuracy. Moreover, FSAS has good anti-noise, robustness.

Published
2024
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49. Additional file 1 of Functional insights from targeted imaging BACE1: the first near-infrared fluorescent probe for Alzheimer’s disease diagnosis

Author

Bi, Anyao, Wu, Junyong, Huang, Shuai, Li, Yongjiang, Zheng, Fan, Ding, Jipeng, Dong, Jie, Xiang, Daxiong, and Zeng, Wenbin

Abstract

Additional file 1: Scheme S1. Synthesis of Probe HBAE. Figure S1. UV-Vis spectrum and of fluorescent spectrum of HBAE in DMSO. Figure S2. Particle size of HBAE by dynamic light scattering. Average size=133.4 nm. Figure S3. TEM image of HBAE at pH=7.4. Figure S4. Fluorescence intensity of HBAE (10 μM) at 650 nm versus the different cells membranes protein. a. U87-MG cell membranes protein, b. N2a cell membranes protein, c. Bend.3 cell membranes protein, d.U251 cell membranes protein. The excitation wavelength was 560 nm. Figure S5. Fluorescence intensity of HBAE (10 μM) at 650 nm versus the different cells membranes protein. a. U87-MG cell membranes protein, b. N2a cell membranes protein, c. Bend.3 cell membranes protein, d. HEK293 cell membranes protein. Figure S6. CCK8 of the cell viability of HBAE. (Each sample was tested using three replicates, and the results are reported as the mean ± standard deviation). Figure S7. Western blot images of BACE1 in 4 cell lines. Figure S8. Supplementary information establishment of blood-brain barrier in vitro. Figure S9. Gene banding of AD and wild-type mice model. 1, 2, 3 and 6 belong to AD model mice, and 4, 5, 7 and 8 belong to wild-type mice. Figure S10. Test of hemolysis activity of formulations. (A) Photos of hemolysis after incubation with different formulations. HBAE of different concentrations are 12.5, 25, 50, 100, 200, 400 μ g/mL, respectively. Figure S11. H&E staining of heart, liver, spleen, lung, kidney tissues of C57 BL/6 mice after HBAE treatment, scale bar = 200 μm. Figure S12. Analysis of serum levels of ALT, AST, BUN and Cr in tumor-bearing mice after HBAE treatment. Data were mean ± SD (n = 5). Figure S13. The H&E staining of the AD mice brain after HBAE treatment. Figure S14. 1H NMR (500 MHz, DMSO-d6) of HBAE. Figure S15. 13C NMR (125 MHz, DMSO-d6) of HBAE. Figure S16. MS spectra of HBAE.

Published
2023
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