Open Access
Volume 42, Number 2, April 2024
Page(s) 344 - 352
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.