{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T13:08:47Z","timestamp":1742389727162},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model\u2019s resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model\u2019s good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis.<\/jats:p>","DOI":"10.3233\/faia231042","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:08:15Z","timestamp":1702566495000},"source":"Crossref","is-referenced-by-count":1,"title":["Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression"],"prefix":"10.3233","author":[{"given":"Changhai","family":"Xia","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiping","family":"Peng","sequence":"additional","affiliation":[{"name":"Jiangmen Polytechnic College, Jiangmen City 529090, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong University of Petrochemical Technology, Maoming City 525000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qirui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong University of Petrochemical Technology, Maoming City 525000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihui","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:08:15Z","timestamp":1702566495000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231042","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}