Open Access
Issue
JNWPU
Volume 42, Number 2, April 2024
Page(s) 344 - 352
DOI https://doi.org/10.1051/jnwpu/20244220344
Published online 30 May 2024
  1. GOPAKUMAR V, TIWARI S, RAHMAN I. A deep learning based data driven soft sensor for bioprocesses[J]. Biochemical Engineering Journal, 2018, 136: 28–39. [Article] [Google Scholar]
  2. KADLEC P, GABRYS B, STRANDT S. Data-driven soft sensors in the process industry[J]. Computers & Chemical Engineering, 2009, 33(4): 795–814 [Google Scholar]
  3. SHANG C, YANG F, HUANG D, et al. Data-driven soft sensor development based on deep learning technique[J]. Journal of Process Control, 2014, 24(3): 223–233. [Article] [Google Scholar]
  4. ZHU Q, HOU K, CHEN Z, et al. Novel virtual sample generation using conditional GAN for developing soft sensor with small data[J]. Engineering Applications of Artificial Intelligence, 2021, 106: 104497. [Article] [Google Scholar]
  5. KHOSBAYAR A, VALLURU J, HUANG B. Multi-rate gaussian bayesian network soft sensor development with noisy input and missing data[J]. Journal of Process Control, 2021, 105: 48–61. [Article] [Google Scholar]
  6. LYU Y, CHEN J, SONG Z. Synthesizing labeled data to enhance soft sensor performance in data-scarce regions[J]. Control Engineering Practice, 2021, 115: 104903. [Article] [Google Scholar]
  7. ZHOU X, LIU X, LAN G, et al. Federated conditional generative adversarial nets imputation method for air quality missing data[J]. Knowledge-Based Systems, 2021, 228: 107261. [Article] [Google Scholar]
  8. XIONG Zhongmin, GUO Huaiyu, WU Yuexin. Review of missing data processing methods[J]. Computer Engineering and Applications, 2019, 57(14): 27–38. [Article] (in Chinese) [Google Scholar]
  9. CHEN Jingnian. Research on selective bayesian classification algorithm[D]. Beijing: Beijing Jiaotong University, 2008 (in Chinese) [Google Scholar]
  10. WANG P, CHEN X. Three-way ensemble clustering for incomplete data[J]. IEEE Access, 2020, 8: 91855–91864. [Article] [Google Scholar]
  11. ELREEDY D, ATIYA A F. A comprehensive analysis of synthetic minority oversampling technique(SMOTE) for handling class imbalance[J]. Information Sciences, 2019, 505: 32–64 [Article] [Google Scholar]
  12. JIANG J, ZHOU H, ZHANG T, et al. Machine learning to predict dynamic changes of pathogenic vibrio spp.abundance on microplastics in marine environment[J]. Environmental Pollution, 2022, 305: 119257. [Article] [Google Scholar]
  13. YU Y, SRIVASTAVA A, CANALES S. Conditional LSTM-GAN for melody generation from lyrics[J]. ACM Trans on Multimedia Computing Communications and Applications, 2021, 17(1): 1–20 [Google Scholar]
  14. YAO Z, ZHAO C. FIGAN: a missing industrial data imputation method customized for soft sensor application[J]. IEEE Trans on Automation Science and Engineering, 2021, 19(4): 3712–3722 [Google Scholar]
  15. WANG X. Data preprocessing for soft sensor using generative adversarial networks[C]//15th International Conference on Control, Automation, Robotics and Vision, 2018: 1355–1360 [Google Scholar]
  16. LIU F T, TING K M, ZHOU Z. Isolation forest[C]//2008 Eighth IEEE International Conference on Data Mining, 2008 [Google Scholar]
  17. GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139–144. [Article] [Google Scholar]
  18. MIRZA M, OSINDERO S. Conditional generative adversarial nets[J/OL]. (2014-11-06)[2023-02-15]. [Article] [Google Scholar]

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