Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage

Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hongwei Cui [verfasserIn] Qu Zhang [verfasserIn] Wenfu Wu [verfasserIn] Haolei Zhang [verfasserIn] Jiangtao Ji [verfasserIn] Hao Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	grain storage grain storage condition temperature data correlation clustering

Übergeordnetes Werk:	In: Agriculture - MDPI AG, 2012, 12(2022), 11, p 1883
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:11, p 1883

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agriculture12111883

Katalog-ID:	DOAJ083512160

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allfields	10.3390/agriculture12111883 doi (DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013 DE-627 ger DE-627 rakwb eng S1-972 Hongwei Cui verfasserin aut Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. grain storage grain storage condition temperature data correlation clustering Agriculture (General) Qu Zhang verfasserin aut Wenfu Wu verfasserin aut Haolei Zhang verfasserin aut Jiangtao Ji verfasserin aut Hao Ma verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 11, p 1883 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:11, p 1883 https://doi.org/10.3390/agriculture12111883 kostenfrei https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 kostenfrei https://www.mdpi.com/2077-0472/12/11/1883 kostenfrei https://doaj.org/toc/2077-0472 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 11, p 1883
spelling	10.3390/agriculture12111883 doi (DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013 DE-627 ger DE-627 rakwb eng S1-972 Hongwei Cui verfasserin aut Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. grain storage grain storage condition temperature data correlation clustering Agriculture (General) Qu Zhang verfasserin aut Wenfu Wu verfasserin aut Haolei Zhang verfasserin aut Jiangtao Ji verfasserin aut Hao Ma verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 11, p 1883 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:11, p 1883 https://doi.org/10.3390/agriculture12111883 kostenfrei https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 kostenfrei https://www.mdpi.com/2077-0472/12/11/1883 kostenfrei https://doaj.org/toc/2077-0472 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 11, p 1883
allfields_unstemmed	10.3390/agriculture12111883 doi (DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013 DE-627 ger DE-627 rakwb eng S1-972 Hongwei Cui verfasserin aut Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. grain storage grain storage condition temperature data correlation clustering Agriculture (General) Qu Zhang verfasserin aut Wenfu Wu verfasserin aut Haolei Zhang verfasserin aut Jiangtao Ji verfasserin aut Hao Ma verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 11, p 1883 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:11, p 1883 https://doi.org/10.3390/agriculture12111883 kostenfrei https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 kostenfrei https://www.mdpi.com/2077-0472/12/11/1883 kostenfrei https://doaj.org/toc/2077-0472 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 11, p 1883
allfieldsGer	10.3390/agriculture12111883 doi (DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013 DE-627 ger DE-627 rakwb eng S1-972 Hongwei Cui verfasserin aut Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. grain storage grain storage condition temperature data correlation clustering Agriculture (General) Qu Zhang verfasserin aut Wenfu Wu verfasserin aut Haolei Zhang verfasserin aut Jiangtao Ji verfasserin aut Hao Ma verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 11, p 1883 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:11, p 1883 https://doi.org/10.3390/agriculture12111883 kostenfrei https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 kostenfrei https://www.mdpi.com/2077-0472/12/11/1883 kostenfrei https://doaj.org/toc/2077-0472 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 11, p 1883
allfieldsSound	10.3390/agriculture12111883 doi (DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013 DE-627 ger DE-627 rakwb eng S1-972 Hongwei Cui verfasserin aut Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. grain storage grain storage condition temperature data correlation clustering Agriculture (General) Qu Zhang verfasserin aut Wenfu Wu verfasserin aut Haolei Zhang verfasserin aut Jiangtao Ji verfasserin aut Hao Ma verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 11, p 1883 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:11, p 1883 https://doi.org/10.3390/agriculture12111883 kostenfrei https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 kostenfrei https://www.mdpi.com/2077-0472/12/11/1883 kostenfrei https://doaj.org/toc/2077-0472 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 11, p 1883
language	English
source	In Agriculture 12(2022), 11, p 1883 volume:12 year:2022 number:11, p 1883
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authorswithroles_txt_mv	Hongwei Cui @@aut@@ Qu Zhang @@aut@@ Wenfu Wu @@aut@@ Haolei Zhang @@aut@@ Jiangtao Ji @@aut@@ Hao Ma @@aut@@
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Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. 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title	Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage
ctrlnum	(DE-627)DOAJ083512160 (DE-599)DOAJ7af63ac88f1c49c3811825599cfab013
title_full	Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage
author_sort	Hongwei Cui
journal	Agriculture
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author_browse	Hongwei Cui Qu Zhang Wenfu Wu Haolei Zhang Jiangtao Ji Hao Ma
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author-letter	Hongwei Cui
doi_str_mv	10.3390/agriculture12111883
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title_sort	modeling and application of temporal correlation of grain temperature during grain storage
callnumber	S1-972
title_auth	Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage
abstract	Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.
abstractGer	Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.
abstract_unstemmed	Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the <i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (<i<XOY</i<, <i<XOZ</i< and <i<YOZ</i< planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (<i<R<sup<2</sup<</i<), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, <i<R<sup<2</sup<</i< and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.
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container_issue	11, p 1883
title_short	Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage
url	https://doi.org/10.3390/agriculture12111883 https://doaj.org/article/7af63ac88f1c49c3811825599cfab013 https://www.mdpi.com/2077-0472/12/11/1883 https://doaj.org/toc/2077-0472
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author2	Qu Zhang Wenfu Wu Haolei Zhang Jiangtao Ji Hao Ma
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