The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often d...The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.展开更多
Background:Drying is a necessary component of traditional Chinese medicine extracts.The heating principle of microwave vacuum drying is different from that of the conventional heat method.However,at present,there is p...Background:Drying is a necessary component of traditional Chinese medicine extracts.The heating principle of microwave vacuum drying is different from that of the conventional heat method.However,at present,there is paucity of information on the drying process of traditional Chinese medicine extract by microwave vacuum drying,and the results of such process are unclear.Methods:To study the dynamic changes in the chemical characteristics of microwave vacuum drying under different drying conditions,ultrahigh-performance liquid chromatography fingerprint profiles were established using Radix isatidis extract as a model drug and analyzed using similarity analysis,partial least squares-discriminant analysis,and semi-quantitative analysis.In addition,a backpropagation artificial neural network model was developed to predict the moisture ratio of the drying process.Results:Qualitative results showed that the similarity between different drying conditions was greater than 0.95,and 2 amino acid components(peaks 5 and 6)affected by process fluctuations were screened out.The quantitative results showed that the mass concentration of component 1 fluctuated after drying,while that of component 2 increased.The optimal backpropagation artificial neural network model structure used to predict the moisture ratio was 5-4-1,with regression and mean squared error values of 0.996 and 0.0003,respectively,after training,which were well fitted and had a strong approximation ability.Conclusion:Upon comparison of fingerprints and the evaluation of statistical methods,common components of Radix isatidis extract had little variation under different drying conditions,and the selected components provided a reference for the establishment of process evaluation indexes.The establishment of backpropagation artificial neural network provides a theoretical basis for the application of microwave vacuum drying technology and online monitoring of moisture ratio.展开更多
Background:To predict the moisture ratio of Radix isatidis extract during drying.Methods:Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of...Background:To predict the moisture ratio of Radix isatidis extract during drying.Methods:Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of Radix isatidis extract during hot air drying and vacuum drying,where regression values and mean squared error were used as evaluation indexes to optimize the number of hidden layer nodes and determine the topological structure of artificial neural networks model.In addition,the drying curves for the different drying parameters were analyzed.Results:The optimal topological structure of the moisture ratio prediction model for hot air drying and vacuum drying of Radix isatidis extract were“4-9-1”and“5-9-1”respectively,and the regression values between the predicted value and the experimental value is close to 1.This indicates that it has a high prediction accuracy.The moisture ratio gradually decreases with an increase in the drying time,reducing the loading,initial moisture content,increasing the temperature,and pressure can shorten the drying time and improve the drying efficiency.Conclusion:Artificial neural networks technology has the advantages of rapid and accurate prediction,and can provide a theoretical basis and technical support for online prediction during the drying process of the extract.展开更多
Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral par...Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(p<0.05).The R^(2),MAE,and RMSE validation models were 0.53 and 0.58,3.19 and 2.97,4.76 and 4.41,respectively,which was accurate enough to be applied in a large-area field.Furthermore,the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward.The research results could guide the agricultural production of winter wheat in northern China.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.2572020AW43NO.2572019CP19)+2 种基金the National Natural Science Foundation of China(Grant No.31470715)the Natural Science Foundation of Hei-longjiang Province(Grant No.TD2020C001)the project for cultivating excellent doctoral dissertation of forestry engineering(Grant No.LYGCYB202009).
文摘The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.
基金found by Guizhou Province Science and Technology Plan Project(No.Qiankeheji-ZK[2021]General 533)Domestic First-Class Discipline Construction Project in Guizhou Province(No.GNYL[2017]008)Guizhou Province Drug New Formulation New Process Technology Innovation Talent Team Project(No.Qiankehe Platform Talents[2017]5655).
文摘Background:Drying is a necessary component of traditional Chinese medicine extracts.The heating principle of microwave vacuum drying is different from that of the conventional heat method.However,at present,there is paucity of information on the drying process of traditional Chinese medicine extract by microwave vacuum drying,and the results of such process are unclear.Methods:To study the dynamic changes in the chemical characteristics of microwave vacuum drying under different drying conditions,ultrahigh-performance liquid chromatography fingerprint profiles were established using Radix isatidis extract as a model drug and analyzed using similarity analysis,partial least squares-discriminant analysis,and semi-quantitative analysis.In addition,a backpropagation artificial neural network model was developed to predict the moisture ratio of the drying process.Results:Qualitative results showed that the similarity between different drying conditions was greater than 0.95,and 2 amino acid components(peaks 5 and 6)affected by process fluctuations were screened out.The quantitative results showed that the mass concentration of component 1 fluctuated after drying,while that of component 2 increased.The optimal backpropagation artificial neural network model structure used to predict the moisture ratio was 5-4-1,with regression and mean squared error values of 0.996 and 0.0003,respectively,after training,which were well fitted and had a strong approximation ability.Conclusion:Upon comparison of fingerprints and the evaluation of statistical methods,common components of Radix isatidis extract had little variation under different drying conditions,and the selected components provided a reference for the establishment of process evaluation indexes.The establishment of backpropagation artificial neural network provides a theoretical basis for the application of microwave vacuum drying technology and online monitoring of moisture ratio.
基金found by Guizhou Province Science and Technology Plan Project(No.Qiankeheji-ZK(2021)General 533)Domestic First-Class Discipline Construction Project in Guizhou Province(No.GNYL(2017)008)Guizhou Province Drug New Formulation New Process Technology Innovation Talent Team Project(No.Qiankehe Platform Talents(2017)5655).
文摘Background:To predict the moisture ratio of Radix isatidis extract during drying.Methods:Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of Radix isatidis extract during hot air drying and vacuum drying,where regression values and mean squared error were used as evaluation indexes to optimize the number of hidden layer nodes and determine the topological structure of artificial neural networks model.In addition,the drying curves for the different drying parameters were analyzed.Results:The optimal topological structure of the moisture ratio prediction model for hot air drying and vacuum drying of Radix isatidis extract were“4-9-1”and“5-9-1”respectively,and the regression values between the predicted value and the experimental value is close to 1.This indicates that it has a high prediction accuracy.The moisture ratio gradually decreases with an increase in the drying time,reducing the loading,initial moisture content,increasing the temperature,and pressure can shorten the drying time and improve the drying efficiency.Conclusion:Artificial neural networks technology has the advantages of rapid and accurate prediction,and can provide a theoretical basis and technical support for online prediction during the drying process of the extract.
基金This study was financially supported by the National Natural Science Foundation of China No.31700640the National Key R&D Program of China(Grant No.2018YFC0407703)+3 种基金the Key R&D Projects of Ningxia Hui Autonomous Region(Grant No.2018BBF02022)the IWHR Research&Development Support Program(Grant No.ID0145B082017)Beijing Municipal Education Commission Innovative Transdisciplinary Program"Ecological Restoration Engineering"the National Key Laboratory Open Fund(Grant No.IWHR-SKL-KF201903).
文摘Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(p<0.05).The R^(2),MAE,and RMSE validation models were 0.53 and 0.58,3.19 and 2.97,4.76 and 4.41,respectively,which was accurate enough to be applied in a large-area field.Furthermore,the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward.The research results could guide the agricultural production of winter wheat in northern China.
