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24 results on '"Wang, Yitang"'

5. Outlier Detection and Correction for Time Series Data of Tunnel Boring Machine

Author

Wang, Yitang, Wang, Suhang, Pang, Yong, Song, Xueguan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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6. Tunneling Operational Data Imputation with Radial Basis Function Neural Network

Author

Wang, Yitang, Pang, Yong, Song, Xueguan, Sun, Wei, 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. Recognition of Tunneling Boring Machine Operating Status Based on the Time Series Analysis

Author

Pang, Yong, Wang, Yitang, Zhang, Shuai, Wang, Suhang, Song, Xueguan, Sun, Wei, 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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14. A Denoising Method by Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine

Author

Wang, Yitang, Fu, Tau, Zhang, Tianci, Song, Xueguan, 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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16. Ethiodized poppyseed oil-based contrast medium is superior to water-based contrast medium during hysterosalpingography regarding image quality improvement and fertility enhancement: A multicentric, randomized and controlled trial

Author

Zhang, Jing, Lan, Weishun, Wang, Yitang, Chen, Kunshan, Zhang, Guofu, Yang, Wenzhong, Chen, Huichun, Xu, Wenjian, Ma, Jianxin, Qin, Wenhua, Zhang, Yao, Wang, Wuquan, Wang, Huichun, Dong, Zijun, Wang, Yanli, Chen, Yi, Gang, Ning, and Tang, Yichuan

Published
2022
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19. An Expensive Many-Objective Optimization Algorithm Based on Efficient Expected Hypervolume Improvement

Author

Pang, Yong, Wang, Yitang, Zhang, Shuai, Lai, Xiaonan, Sun, Wei, and Song, Xueguan

Abstract

The expected hypervolume improvement (EHVI) is one of the most popular infill criteria for multiobjective optimization problems. Although it has a significant advantage in exploring potential Pareto-optimal solutions, it has rarely been applied in many-objective problems due to its high computational cost. To address this issue, this article proposes an expensive many-objective optimization algorithm based on the framework of nondominated sorting genetic algorithm III (NSGA-III) and assisted by the kriging surrogate models. In the proposed algorithm, the Monte Carlo sampling (MCS) method for EHVI estimation is improved by importance sampling, in which only one sampling process is required during the entire optimization process using a uniform distribution in normalized objective space. Considering the predicted uncertainty from the kriging model, an uncertainty-assisted nondominated sorting approach is proposed to substitute for the conventional approach in NSGA-III. In the proposed method, the predicted uncertainty is incorporated into the objective space as one independent dimension for nondominated sorting, which can enable the exploration of potential points with desirable EHVI values. In addition, the proposed algorithm considers the diversity of the solutions by de-emphasizing the pursuit of the best EHVI. The experimental results on benchmark problems demonstrate that the proposed EHVI calculation method can save computational costs compared with MCS and indicate the superiority of the proposed algorithm over the others.

Published
2023
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