FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4211226422> ?p ?o ?g. }

• W4211226422 endingPage "526" @default.
• W4211226422 startingPage "510" @default.
• W4211226422 abstract "Soft sensor technique has become a promising solution to enable real-time estimations of difficult-to-measure quality variables in industrial processes. However, traditional soft sensor models cannot always function well due to two challenging issues. First, labeled data are usually expensive to obtain in many real-world applications, thus leading to unsatisfactory performance for traditional supervised soft sensors. Meanwhile, the information behind abundant unlabeled data is not fully exploited. Second, it is very common for soft sensors to encounter the modeling uncertainty resulting from the diversity of training data, model hyperparameters, and optimization parameters. Therefore, a new soft sensor method called semi-supervised ensemble support vector regression (SSESVR) is proposed by combining semi-supervised learning with ensemble learning. The SSESVR method first formulates the estimation of pseudo-labels as a multi-learner pseudo-labeling optimization problem and then solve it through evolutionary approach, thus extending the labeled training set using satisfactory pseudo-labeled data. Further, by considering multimodal perturbation mechanism, a two-level ensemble architecture is employed to enable efficient cooperation of semi-supervised and ensemble learning framework. Two case studies are conducted to verify the effectiveness and superiority of the proposed SSESVR approach." @default.
• W4211226422 created "2022-02-13" @default.
• W4211226422 creator A5015115331 @default.
• W4211226422 creator A5037780604 @default.
• W4211226422 creator A5067127013 @default.
• W4211226422 creator A5067937192 @default.
• W4211226422 creator A5080222208 @default.
• W4211226422 creator A5088041850 @default.
• W4211226422 date "2022-03-01" @default.
• W4211226422 modified "2023-10-01" @default.
• W4211226422 title "Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data" @default.
• W4211226422 cites W1164930623 @default.
• W4211226422 cites W1571801203 @default.
• W4211226422 cites W1964357740 @default.
• W4211226422 cites W1969833588 @default.
• W4211226422 cites W1970360626 @default.
• W4211226422 cites W1972195634 @default.
• W4211226422 cites W1978982221 @default.
• W4211226422 cites W1979923568 @default.
• W4211226422 cites W1986795354 @default.
• W4211226422 cites W1992796724 @default.
• W4211226422 cites W1994292816 @default.
• W4211226422 cites W2000651380 @default.
• W4211226422 cites W2004186751 @default.
• W4211226422 cites W2041755017 @default.
• W4211226422 cites W2047094503 @default.
• W4211226422 cites W2061972209 @default.
• W4211226422 cites W2085862958 @default.
• W4211226422 cites W2091706822 @default.
• W4211226422 cites W2093851253 @default.
• W4211226422 cites W2100128988 @default.
• W4211226422 cites W2106401878 @default.
• W4211226422 cites W2111072639 @default.
• W4211226422 cites W2144097240 @default.
• W4211226422 cites W2149489749 @default.
• W4211226422 cites W2164762195 @default.
• W4211226422 cites W2167055186 @default.
• W4211226422 cites W2207744263 @default.
• W4211226422 cites W2226450201 @default.
• W4211226422 cites W2283626220 @default.
• W4211226422 cites W2408812858 @default.
• W4211226422 cites W2565108701 @default.
• W4211226422 cites W2570107029 @default.
• W4211226422 cites W2598225641 @default.
• W4211226422 cites W2604407846 @default.
• W4211226422 cites W2742763523 @default.
• W4211226422 cites W2766725700 @default.
• W4211226422 cites W2768191664 @default.
• W4211226422 cites W2789440825 @default.
• W4211226422 cites W2791748024 @default.
• W4211226422 cites W2808462996 @default.
• W4211226422 cites W2810016643 @default.
• W4211226422 cites W2886613299 @default.
• W4211226422 cites W2889017122 @default.
• W4211226422 cites W2890579411 @default.
• W4211226422 cites W2903010511 @default.
• W4211226422 cites W2903530788 @default.
• W4211226422 cites W2904067769 @default.
• W4211226422 cites W2906083317 @default.
• W4211226422 cites W2909687010 @default.
• W4211226422 cites W2914185918 @default.
• W4211226422 cites W2941089296 @default.
• W4211226422 cites W2942496699 @default.
• W4211226422 cites W2965420085 @default.
• W4211226422 cites W2971190199 @default.
• W4211226422 cites W2976234163 @default.
• W4211226422 cites W2984353870 @default.
• W4211226422 cites W2990240608 @default.
• W4211226422 cites W3001458159 @default.
• W4211226422 cites W3003166104 @default.
• W4211226422 cites W3004625110 @default.
• W4211226422 cites W3004957259 @default.
• W4211226422 cites W3011933561 @default.
• W4211226422 cites W3015790446 @default.
• W4211226422 cites W3016388251 @default.
• W4211226422 cites W3021294679 @default.
• W4211226422 cites W3023211159 @default.
• W4211226422 cites W3049640903 @default.
• W4211226422 cites W3088157283 @default.
• W4211226422 cites W3092628223 @default.
• W4211226422 cites W3123899295 @default.
• W4211226422 cites W3127211694 @default.
• W4211226422 cites W3135396474 @default.
• W4211226422 cites W3138399413 @default.
• W4211226422 cites W3156136661 @default.
• W4211226422 cites W3190052090 @default.
• W4211226422 cites W3192169238 @default.
• W4211226422 cites W3196869853 @default.
• W4211226422 cites W3207596548 @default.
• W4211226422 doi "https://doi.org/10.1016/j.cherd.2022.01.026" @default.
• W4211226422 hasPublicationYear "2022" @default.
• W4211226422 type Work @default.
• W4211226422 citedByCount "13" @default.
• W4211226422 countsByYear W42112264222022 @default.
• W4211226422 countsByYear W42112264222023 @default.
• W4211226422 crossrefType "journal-article" @default.
• W4211226422 hasAuthorship W4211226422A5015115331 @default.
• W4211226422 hasAuthorship W4211226422A5037780604 @default.

• next