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Your search keyword '"Guo, Yuanjun"' showing total 135 results
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135 results on '"Guo, Yuanjun"'

1. Three-Dimensional Reconstruction Optimization Algorithm Under Large Viewpoint Variations Scenes

Author

Gu, Yuntao, Yang, Zhile, Guo, Yuanjun, Ding, Wenjun, Cheng, Lan, Liu, Yu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, 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, Hirche, Sandra, 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, Tan, Kay Chen, Series Editor, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2024
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2. Multi-objective Optimization Scheduling of Photovoltaic Thermal Power Units Based on Electric Vehicles: An Improved NSGA-II Algorithm

Author

Li, Pengfei, Yang, Zhile, Guo, Yuanjun, Zhu, Xiaodong, Zhang, Linxin, Liang, Rui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, 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, Hirche, Sandra, 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, Tan, Kay Chen, Series Editor, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2024
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6. A Novel Multi-robot Path Planning Algorithm Considering Dynamic Environmental Information

Author

Zhang, Jiahao, Wu, Chengke, Cheng, Lan, Feng, Wei, Guo, Yuanjun, Yang, Zhile, Yang, Rui, 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, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2023
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7. Full Life Cycle Prediction of Nuclear Bearings Based on Digital Twin Hybrid Model

Author

Han, Chunyi, Guo, Yuanjun, Yang, Zhile, Feng, Wei, Zhang, Yanhui, Chen, Huanlin, Chen, Weihua, 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, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2023
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8. A Novel Distribution Network Operating State Monitoring and Fault Prediction Model Based on Digital Emulation

Author

Zhang, Jie, Liang, Rui, Liu, Changde, Sun, Jie, An, Zhao, Yang, Zhile, Guo, Yuanjun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Ferrari, Gianluigi, Series Editor, Duan, Haibin, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Cao, Wenping, editor, Hu, Cungang, editor, and Chen, Xiangping, editor

Published
2023
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19. A Double-Stage Attention-Based Recurrent Neural Network Model for Steam Demand Fault Prediction

Author

Sun, Bo, Qian, Hong, Guo, Yuanjun, Yang, Zhile, Feng, Wei, Ling, Jun, Zhou, Zhigang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Cao, Wenping, editor, Hu, Cungang, editor, Huang, Xiaoyan, editor, Chen, Xiangping, editor, and Tao, Jun, editor

Published
2022
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28. Electric Field Switching of Magnon Spin Current in a Compensated Ferrimagnet

Author

Li, Kaili, primary, Wang, Lei, additional, Wang, Yu, additional, Guo, Yuanjun, additional, Lv, Shuping, additional, He, Yuewei, additional, Lin, Weiwei, additional, Min, Tai, additional, Hu, Shaojie, additional, Yang, Sen, additional, Xue, Dezhen, additional, Zheng, Aqun, additional, Yang, Shuming, additional, and Ding, Xiangdong, additional

Published
2024
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34. Green- and Blue-Emitting Tb 3+ -Activated Linde Type A Zeolite-Derived Boro-Aluminosilicate Glass for Deep UV Detection/Imaging.

Author

Xiao, Yongneng, Hou, Shaoyi, Yang, Zhenhuai, Huang, Xingxing, Guo, Yuanjun, Ji, Siyu, Huang, Xiaochan, Wang, Fengshuang, Hu, Qiang, and Guo, Xiaodong

Subjects
PLASMA displays, GREEN light, TERBIUM, BLUE light, GLASS, MOLECULAR spectra
Abstract

Tb3+-activated LTA zeolite-derived boro-aluminosilicate glass samples with a composition of xTb2O3-68(Na2O-Al2O3-SiO2)–32B2O3 (x = 0.2, 1.0 and 10 extra wt%) were prepared using the melt-quenching method. The emission spectra recorded upon ultraviolet (UV) excitation with two different wavelengths of 193 and 378 nm showed blue light (5D3 to 7FJ=6,5,4 and 5D4 to 7F6 transitions of Tb3+) and green light (5D4 to 7F5 transition of Tb3+) emissions with comparable intensities up to a Tb3+ concentration of 10 extra wt%. Of note, the mean decay times of the green luminescence of the glass samples were relatively fast (<20 μs). The synthesized glass has potential in applications concerning UV imaging, UV detection, and plasma display panels. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm.

Author

Liu, Shili, Yang, Fan, Li, Jianqing, Cheng, Zhiyu, Zhang, Tianzhong, Cheng, Tingli, Guo, Yuanjun, and Yang, Zhile

Subjects
PHOTOVOLTAIC power systems, MAXIMUM power point trackers, DISTRIBUTED power generation, INDIVIDUALIZED instruction, SPACE exploration, ELECTRIC power distribution grids
Abstract

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity. [ABSTRACT FROM AUTHOR]

Published
2024
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39. MMFSL: A novel multi-modal few-shot learning framework for fault diagnosis of industrial bearings

Author

Cheng, Lan, An, Zhao, Guo, Yuanjun, Ren, MiFeng, Yang, Zhile, and McLoone, Seán

Abstract

Unbalanced data with very few samples for special abnormal conditions frequently occur in actual production processes, which can make accurate monitoring of the process state challenging. This paper proposes a multi-modal few-shot learning method (MMFSL) within a fault diagnosis framework for unbalanced data modelling of industrial bearings. MMFSL can handle two modes of data and therefore contains two data generation channels. The first channel deals with time series data and the second deals with images. The two modes of generated data are then evaluated using the MMFSL evaluation modules, IEI (image evaluation index) and TEI (time series evaluation index), to guarantee the quality of the generated data. In addition, the time series data are analyzed in the time-frequency domain to ensure the consistency of the frequency distribution. Original unbalanced and insufficient data samples are expanded so that the fault diagnosis model can be trained adequately. Through experiments using data from Case Western Reserve University(CWRU) and Pardborn University(PU), generated data by using the MMFSL model can increase fault detection accuracy from 80.9% to 97.5%. At the same time, false alarms rate can be reduced significantly from 4.6% to 0.1%. Moreover, fault classification model accuracy can reach 98.1% compared to 95.4% for the original time series dataset, and 99.3% compared with 96.5% for the image data extension. Various visualizations of comparison results are also provided to show the reliability of the MMFSL framework.

Published
2023

40. Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer.

Author

Zhu, Jian, Zhao, Zhiyuan, Zheng, Xiaoran, An, Zhao, Guo, Qingwu, Li, Zhikai, Sun, Jianling, and Guo, Yuanjun

Subjects
SOLAR energy, WIND power, RENEWABLE energy sources, CLEAN energy, WIND forecasting, DATA scrubbing, DEEP learning
Abstract

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model's ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R 2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting. [ABSTRACT FROM AUTHOR]

Published
2023
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