{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:42:16Z","timestamp":1776811336875,"version":"3.51.2"},"reference-count":6,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,11,1]]},"abstract":"<jats:p>Stored-grain temperature is the most important factor in grain storage. According to the measured data, the temperature in the grain pile can be effectively predicted, which can find problems in advance, reduce grain loss and increase grain quality. Long Short-Term memory (LSTM) can perform better in longer sequences than ordinary RNN. This paper is applied to the analysis of big data of grain storage and the early warning of grain storage temperature. In this paper, the selected LSTM is optimized and the early warning model of grain situation is established, and the analysis steps of the early warning model are given. In order to verify the availability of the improved LSTM network structure, RNN and three variants were used to predict the grain temperature under the same conditions, the prediction effect of the improved CLSTM is better.<\/jats:p>","DOI":"10.3233\/jcm-204751","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T13:44:38Z","timestamp":1616766278000},"page":"1145-1154","source":"Crossref","is-referenced-by-count":2,"title":["Research on grain storage temperature prediction model based on improved long short-term memory"],"prefix":"10.66113","volume":"21","author":[{"given":"Liang","family":"Ge","sequence":"first","affiliation":[{"name":"National Engineering Laboratory of Speech and Language Information Processing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"3","key":"10.3233\/JCM-204751_ref1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MIM.2017.7951690","article-title":"Recent developnents in storedgrain sensors, monitoring and management technology","volume":"20","author":"Singh","year":"2017","journal-title":"IEEE Instrumentation & Measurement Magazine"},{"issue":"3\/4\/5","key":"10.3233\/JCM-204751_ref8","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.jsv.2007.06.028","article-title":"MIMO adaptive vibration control of smart structures with quickly varying parameters: neural networks Vs classical control approach","volume":"307","author":"Kumar","year":"2007","journal-title":"Journal of Sound and Vibration"},{"key":"10.3233\/JCM-204751_ref10","doi-asserted-by":"crossref","unstructured":"H. Shi, S. Hu and J. Zhang, LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell, International Journal of Intelligent Computing and Cybernetics 12(2) (2019).","DOI":"10.1108\/IJICC-11-2018-0152"},{"issue":"7","key":"10.3233\/JCM-204751_ref11","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1007\/s00521-017-3210-6","article-title":"A comparative performance analysis of different activation functions in LSTM networks for classification","volume":"31","author":"Farzad","year":"2019","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/JCM-204751_ref12","first-page":"1","article-title":"Aeration strategy for contolling grain storage based on simulation and on real data acqusition","author":"de Carvalho Lopes","year":"2008","journal-title":"Computers and Electronics in Agriculture"},{"issue":"3","key":"10.3233\/JCM-204751_ref13","doi-asserted-by":"crossref","first-page":"288","DOI":"10.3788\/GXJS20123803.0288","article-title":"The application of fiber Bragg grating temperature testing system in the barn","volume":"38","author":"Zhang","year":"2012","journal-title":"Optical Technique"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-204751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:06:04Z","timestamp":1776809164000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-204751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":6,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jcm-204751","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,1]]}}}