{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:08:01Z","timestamp":1760609281836,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T00:00:00Z","timestamp":1594684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["No.61903108"],"award-info":[{"award-number":["No.61903108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.<\/jats:p>","DOI":"10.3390\/e22070770","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T11:03:23Z","timestamp":1594724603000},"page":"770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Sea States Identification Based on Maximum Likelihood Evidential Reasoning Rule"],"prefix":"10.3390","volume":"22","author":[{"given":"Xuelin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojian","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diju","family":"Gao","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0251-6257","authenticated-orcid":false,"given":"Guodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computer Engineering, Vienna University of Technology, 1040 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu","family":"Grosu","sequence":"additional","affiliation":[{"name":"Institute of Computer Engineering, Vienna University of Technology, 1040 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,14]]},"reference":[{"key":"ref_1","first-page":"101","article-title":"Motor technology applied in naval ship electric drive propulsion","volume":"005","author":"Hua","year":"2015","journal-title":"Micromotors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"190","DOI":"10.4028\/www.scientific.net\/AMM.65.190","article-title":"Application of Direct-Drive Technology in Marine Electric Propulsion","volume":"65","author":"Ji","year":"2011","journal-title":"Appl. Mech. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s00773-016-0372-3","article-title":"Numerical analysis of surface piercing propeller in unsteady conditions and cupped effect on ventilation pattern of blade cross-section","volume":"21","author":"Yari","year":"2016","journal-title":"J. Mar. Sci. Tech."},{"key":"ref_4","unstructured":"Koushan, K. (2004, January 12\u201317). Environmental and interaction effects on propulsion systems used in dynamic positioning, an overview. Proceedings of the 9th International Symposium on Practical Design of Ships and other Floating Structures (PRADS), Luebeck-Travemuende, Germany."},{"key":"ref_5","unstructured":"Dou, X.Q. (2015). Simulation Study on Control Strategy of Electric Propulsion System under Severe Sea Conditions, Wuhan University of Technology."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.apenergy.2017.02.060","article-title":"Design and control of hybrid power and propulsion systems for smart ships: A review of developments","volume":"194","author":"Geertsma","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1016\/j.oceaneng.2011.07.009","article-title":"Identification of ventilation regimes of a marine propeller by means of dynamic-loads analysis","volume":"38","author":"Califano","year":"2011","journal-title":"Ocean. Eng."},{"key":"ref_8","unstructured":"Smogeli, \u00d8.N. (2006). Control of Marine Propellers: From Normal to Extreme Conditions, University of Science and Technology."},{"key":"ref_9","unstructured":"Smogeli, O., Hansen, J., Serensen, A., and Johansen, T.A. (1997, January 15\u201317). Anti-spin control for marine propulsion systems. Proceedings of the 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No. 04CH37601), Paradise Island, Bahamas."},{"key":"ref_10","first-page":"187","article-title":"Anti-spin thruster control in extreme seas","volume":"36","author":"Aarseth","year":"2003","journal-title":"IFAC Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TCST.2008.2009065","article-title":"Antispin thruster control for ships","volume":"17","author":"Smogeli","year":"2009","journal-title":"IEEE. Contr. Syst."},{"key":"ref_12","unstructured":"Kozlowska, A.M., Steen, S., and Koushan, K. (1999, January 13\u201317). Classification of different type of propeller ventilation and ventilation inception mechanism. Proceedings of the First International Symposium on Marine Propulsors, Trondheim, Norway."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Savio, L., and Steen, S. (2012). Identification and analysis of full scale ventilation events. Int. J. Rotat. Mach., 1\u201319.","DOI":"10.1155\/2012\/951642"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1007\/s00500-017-2512-z","article-title":"Toward a soft computing-based correlation between oxygen toxicity seizures and hyperoxic hyperpnea","volume":"22","author":"Raffaele","year":"2018","journal-title":"Soft. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11775","DOI":"10.1007\/s00500-018-03729-y","article-title":"A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees","volume":"23","author":"Raffaele","year":"2019","journal-title":"Soft. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s00521-017-2853-7","article-title":"Virtual reality research of the dynamic characteristics of soft soil under metro vibration loads based on BP neural networks","volume":"29","author":"Cui","year":"2018","journal-title":"Neural. Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, J.-B., and Xu, D.-L. (2017, January 7\u20138). Inferential modelling and decision making with data. Proceedings of the 23rd International Conference on Automation and Computing (ICAC), Huddersfield, UK.","DOI":"10.23919\/IConAC.2017.8082048"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, X., Sachan, S., Yang, J.-B., and Xu, D.-L. (2019, January 5\u20137). Maximum Likelihood Evidential Reasoning-Based Hierarchical Inference with Incomplete Data. Proceedings of the 25th International Conference on Automation and Computing (ICAC), Lancaster, UK.","DOI":"10.23919\/IConAC.2019.8895062"},{"key":"ref_19","unstructured":"Koushan, K. (2007). Dynamics of propeller blade and duct loading on ventilated thrusters in dynamic positioning mode. DP Conf. Ser., 1\u201313."},{"key":"ref_20","unstructured":"Koushan, K. (2006, January 17\u201322). Dynamics of ventilated propeller blade loading on thrusters due to forced sinusoidal heave motion. Proceedings of the 26th Symposium on Naval Hydrodynamics, Rome, Italy."},{"key":"ref_21","unstructured":"Koushan, K. (2006, January 3\u20135). Dynamics of propeller blade and duct loadings on ventilated ducted thrusters operating at zero speed. Proceedings of the T-POD06-2nd International Conference on Technological Advances in Podded Propulsion, Brest, France."},{"key":"ref_22","unstructured":"Koushan, K., Spence, S.J., and Hamstad, T. (2009, January 22\u201324). Experimental investigation of the effect of waves and ventilation on thruster loadings. Proceedings of the 1st International Symposium on Marine Propulsors (SMP\u201909), Trondheim, Norway."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2013.09.003","article-title":"Evidential reasoning rule for evidence combination","volume":"205","author":"Yang","year":"2013","journal-title":"Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press.","DOI":"10.1515\/9780691214696"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0377-2217(99)00441-5","article-title":"Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties","volume":"131","author":"Yang","year":"2001","journal-title":"Eur. J. Oper. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, Y., Gong, G., and Li, N. (2018). A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0193827"},{"key":"ref_27","first-page":"157","article-title":"A multi-objective optimization model solving method based on genetic algorithm and scheme evaluation","volume":"24","author":"Baoying","year":"2019","journal-title":"J. China Agric. Univ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.conengprac.2006.06.004","article-title":"The concept of anti-spin thruster control","volume":"16","author":"Smogeli","year":"2008","journal-title":"Control. Eng. Pract."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.knosys.2016.11.001","article-title":"Data classification using evidence reasoning rule","volume":"116","author":"Xu","year":"2017","journal-title":"Knowl. Based. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107329","DOI":"10.1016\/j.oceaneng.2020.107329","article-title":"Improved control of propeller ventilation using an evidence reasoning rule based Adaboost.M1 approach","volume":"209","author":"Gao","year":"2020","journal-title":"Ocean. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Duong, B.P., and Kim, J. (2018). Non-mutually exclusive deep neural network classifier for combined modes of bearing fault diagnosis. Sensors, 18.","DOI":"10.3390\/s18041129"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cao, Z.J., Ge, Y.C., and Feng, J.L. (2017). SAR image classification with a sample reusable domain adaptation algorithm based on SVM classifier. Pattern. Recogn., S0031320317303035.","DOI":"10.1016\/j.patcog.2017.07.032"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:51:27Z","timestamp":1760176287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,14]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["e22070770"],"URL":"https:\/\/doi.org\/10.3390\/e22070770","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,7,14]]}}}