The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study wa...Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.展开更多
Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of...Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.展开更多
In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic systems.Firstly,two basic models are selected as combination componen...In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic systems.Firstly,two basic models are selected as combination components due to their proved effectiveness.One is Gaussian process(GP)model,which can provide the predictive variance of the predicted output and only has several optimizing parameters.The other is regularized extreme learning machine(RELM)model,which can improve the overfitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance.Then both of the models are updated continually using meaningful new data selected by data selection methods.Furthermore,recursive methods are employed in the two models to reduce the computational burden caused by continuous renewal.Finally,the two models are combined in IMM algorithm to realize the hybrid prediction,which can avoid the error accumulation in the single-model prediction.In order to verify the performance,the proposed method is applied to the prediction of moisture content of alkali-surfactant-polymer(ASP)flooding.The simulation results show that the proposed model can match the process very well.And IMM algorithm can outperform its components and provide a nice improvement in accuracy and robustness.展开更多
To investigate the effects of temperature and moisture content(MC) on acoustic wave velocity(AWV)in wood,the relationships between wood temperature,MC,and AWV were theoretically analyzed.According to the theoretical p...To investigate the effects of temperature and moisture content(MC) on acoustic wave velocity(AWV)in wood,the relationships between wood temperature,MC,and AWV were theoretically analyzed.According to the theoretical propagation characteristics of the acoustic waves in the wood mixture and the differences in velocity among various media(including ice,water,pure wood or oven-dried wood),theoretical relationships of temperature,MC,and AWV were established,assuming that the samples in question were composed of a simple mixture of wood and water or of wood and ice.Using the theoretical model,the phase transition of AWV in green wood near the freezing point(as derived from previous experimental results) was plausibly described.By comparative analysis between theoretical and experimental models for American red pine(Pinus resinosa) samples,it was established that the theoretically predicted AWV values matched the experiment results when the temperature of the wood was below the freezing point of water,with an averageprediction error of 1.66%.The theoretically predicted AWV increased quickly in green wood as temperature decreased and changed suddenly near 0 °C,consistent with the experimental observations.The prediction error of the model was relatively large when the temperature of the wood was above the freezing point,probably due to an overestimation of the effect of the liquid water content on the acoustic velocity and the limited variables of the model.The high correlation between the predicted and measured acoustic velocity values in frozen wood samples revealed the mechanisms of temperature,MC,and water status and how these affected the wood(particularly its acoustic velocity below freezing point of water).This result also verified the reliability of a previous experimental model used to adjust for the effect of temperature during field testing of trees.展开更多
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
In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32...In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32.2?-?54.1 for triaxial unconsolidated undrained shear test. The results show that?Cuu,?K?and?n?values all showed a downward trend, and?Rf?variation was not obvious with the increase of moisture content. The variation rule of each parameter is not obvious with the increase of plasticity index. When moisture content is constant,?Cuu?and?n?values do not change much,?K?increases with the increase of plasticity index within the range of 70%?-?80% moisture content, and does not change much with the increase of plasticity index when moisture content is greater than 80%,?Rf?has no obvious rule.?When the plasticity index is constant,?Cuu,?Kand?n?decrease with the increase of moisture content,?Rf?has no obvious rule. The maximum value of?Cuu?is 20.18?kPa, the minimum is 3.72?kPa, and the maximum to minimum ratio is 5.42. The maximum value of?K?is 0.517, the minimum is 0.022, and the maximum to minimum ratio is 23.5. The maximum value of?n?is 1.198, the minimum is 0.150, and the maximum to minimum ratio is 7.99. The maximum value of?Rf?is 0.872, the minimum is 0.679, and the maximum to minimum ratio is 1.28.展开更多
Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a bo...Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.展开更多
According to the existing method including testing the frequency and establishing the relationship between moisture content and frequency, a corresponding instrument was designed. In order to further improve the accur...According to the existing method including testing the frequency and establishing the relationship between moisture content and frequency, a corresponding instrument was designed. In order to further improve the accuracy and rapidity of the system, a new approach to describe the relationship between the measurement error and the temperature was proposed. The error band could be obtained and divided into several parts(based on the range of temperature) to indicate the error value that should compensate the grain moisture content for the changes in temperature. By calculating the error band at the maximum and the minimum operating temperatures, as well as by determining the error compensation value from the error band based on the measurement moisture content, the final effective result was derived.展开更多
Soyang Lake is the largest lake in Republic of Korea bordering Chuncheon,Yanggu,and Inje in Gangwon Province.It is widely used as an environmental resource for hydropower,flood control,and water supply.Therefore,we co...Soyang Lake is the largest lake in Republic of Korea bordering Chuncheon,Yanggu,and Inje in Gangwon Province.It is widely used as an environmental resource for hydropower,flood control,and water supply.Therefore,we conducted a survey of the floodplain of Soyang Lake to analyze the sediments in the area.We used global positioning system(GPS)data and aerial photography to monitor sediment deposits in the Soyang Lake floodplain.Data from three GPS units were compared to determine the accuracy of sampling location measurement.Sediment samples were collected at three sites:two in the eastern region of the floodplain and one in the western region.A total of eight samples were collected:Three samples were collected at 10 cm intervals to a depth of 30 cm from each site of the eastern sampling point,and two samples were collected at depths of 10 and 30 cm at the western sampling point.Samples were collected and analyzed for vertical and horizontal trends in particle size and moisture content.The sizes of the sediment samples ranged from coarse to very coarse sediments with a negative slope,which indicate eastward movement from the breach.The probability of a breach was indicated by the high water content at the eastern side of the floodplain,with the eastern sites showing a higher probability than the western sites.The results of this study indicate that analyses of grain fineness,moisture content,sediment deposits,and sediment removal rates can be used to understand and predict the direction of breach movement and sediment distribution in Soyang Lake.展开更多
Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the va...Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the variability of loess slope SWC through the change of resistivity.It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors.In this context,geoelectric and environmental factors such as soil temperature and SWC were monitored for three years.The prediction model of apparent resistivity and SWC was calibrated.The post processing of geoelectric data was introduced.In addition,the SWC collected by Time-Domain Reflectometry(TDR)was used to verify the feasibility of electrical resistivity tomography(ERT)data.The SWC variability in the process of rainfall,the evolution of four seasons,and the alternation of drying and wetting were evaluated.The research results show that:i)the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5-3.0 m depth.ii)The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring.iii)From 2017 to 2020,the SWC increased in August,and the soil became dry in January.iv)Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity.The outcome can provide the change information of SWC to a great extent without excavating boreholes.展开更多
The commercial open-ended coaxial probe(Agilent 85070E)is the most commonly used sensor to determine the permittivity of wet materials.This paper extends the usability and applicability of the sensor to the estimation...The commercial open-ended coaxial probe(Agilent 85070E)is the most commonly used sensor to determine the permittivity of wet materials.This paper extends the usability and applicability of the sensor to the estimation of moisture content in Hevea Rubber Latex.The dielectric constant and loss factor were measured using the commercial probe whilst the moisture contents were obtained using the standard oven drying method.Comparison results were obtained between the different dielectric models to predict moisture content in latex.Both the dielectric constant and the loss factor of rubber latex linearly increased with moisture content at all selected frequencies.Calibration equations were established to relate both the dielectric constant and the loss factor with moisture content.These equations were used to predict moisture content in Hevea latex from measured values of the dielectric constant and the loss factor.The lowest mean relative error between actual and predicted moisture contents was 0.02 at 1 GHz when using the Cole-Cole dielectric constant calibration equation.展开更多
A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seawe...A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seaweed were known to be used in this system. The non-linear regression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed being dried on the daily average of air temperature about 40℃. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.99027), the lowest MBE (0.00044) and RMSE (0.03039) indicated that the Page model was the best mathematical model to describe the drying behavior of sauna dried seaweed. The percentage of the saved time using this technique was calculated at 57.9% on the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s.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
木材密度包括基本密度、气干密度等,在12%含水率条件下的气干密度(D12)较常用,因此有必要将木材气干密度换算为基本密度(Db)。目前利用木材气干密度计算基本密度的模型有Reyes、Chave、Simpson和Vieilledent模型等,然而这些模型预测结...木材密度包括基本密度、气干密度等,在12%含水率条件下的气干密度(D12)较常用,因此有必要将木材气干密度换算为基本密度(Db)。目前利用木材气干密度计算基本密度的模型有Reyes、Chave、Simpson和Vieilledent模型等,然而这些模型预测结果不完全一致。利用中国林业科学研究院木材工业研究所(Research Institute of Wood Industry,Chinese Academy of Forestry,CRIWI)和法国农业国际合作研究发展中心(French Agricultural Research Centre for International Development,CIRAD)的木材D12和Db数据,首先基于CRIWI的木材密度数据建立D12与Db的关系模型,然后将CRIWI和CIRAD的D12数据分别代入Reyes模型、Chave模型、Simpson模型、Vieilledent模型和新建模型,获得每个树种木材Db的预测值,并根据Db预测值和实测值计算残差绝对值均值。不同模型残差绝对值均值比较结果表明:Reyes模型在利用CRIWI和CIRAD的木材密度数据时预测Db的准确性都比较高,适用性最广;Simpson模型、新建模型在D12高于1.0 g/cm3时预测Db的准确性降低。展开更多
花椒热风干燥降速期水分含量低,水分扩散慢,导致热风干燥耗时长。为提高干燥效率,并通过热风与微波组合干燥,分别进行热风干燥、微波干燥和热风-微波组合干燥实验,探究不同干燥参数对花椒失水特性的影响,以确定合理的干燥转换临界点和...花椒热风干燥降速期水分含量低,水分扩散慢,导致热风干燥耗时长。为提高干燥效率,并通过热风与微波组合干燥,分别进行热风干燥、微波干燥和热风-微波组合干燥实验,探究不同干燥参数对花椒失水特性的影响,以确定合理的干燥转换临界点和最优组合干燥模型,并将傅里叶准则数(F_(0))引入Fick第二扩散定律方程,求解有效水分扩散系数(D_(eff))。研究结果表明:热风和微波单独干燥时,升高风温风速和增加微波功率均有利于缩短干燥时间;热风-微波组合干燥花椒时,热风段转微波段的最佳目标含水率即为热风干燥的临界点含水率(65%(w.b)),且高热风温度和高微波功率均可使微波干燥段获得高失水速率;热风-微波组合干燥花椒热风段和微波段对应的最优模型分别为Wang and Singh模型和Page模型,D_(eff)范围分别为1.908×10^(-9)~3.547×10^(-9)m^(2)/s和1.883×10^(-8)~3.321×10^(-8)m^(2)/s。热风-微波组合干燥方式能够显著提高干燥效率,促进花椒内部水分扩散,干燥模型可为优化干燥工艺和设计干燥设备提供理论依据。展开更多
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
基金the National Key Research and Development Program of ChinaKey Projects for Strategic International Innovative Cooperation in Science and Technology(2018YFE0207800)+1 种基金Fundamental Research Funds for the Central Universities(2572019BA03)partly by the China Scholarship Council(CSC No.2016DFH417)。
文摘Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.
基金financially supported by the National Natural Science Foundation of China (Grant No.51975165)。
文摘Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.
基金supported by National Natural Science Foundation under Grant No.60974039National Natural Science Foundation under Grant No.61573378+1 种基金Natural Science Foundation of Shandong province under Grant No.ZR2011FM002the Fundamental Research Funds for the Central Universities under Grant No.15CX06064A.
文摘In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic systems.Firstly,two basic models are selected as combination components due to their proved effectiveness.One is Gaussian process(GP)model,which can provide the predictive variance of the predicted output and only has several optimizing parameters.The other is regularized extreme learning machine(RELM)model,which can improve the overfitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance.Then both of the models are updated continually using meaningful new data selected by data selection methods.Furthermore,recursive methods are employed in the two models to reduce the computational burden caused by continuous renewal.Finally,the two models are combined in IMM algorithm to realize the hybrid prediction,which can avoid the error accumulation in the single-model prediction.In order to verify the performance,the proposed method is applied to the prediction of moisture content of alkali-surfactant-polymer(ASP)flooding.The simulation results show that the proposed model can match the process very well.And IMM algorithm can outperform its components and provide a nice improvement in accuracy and robustness.
基金funded by the National Natural Science Foundation of China(Grant Nos.31600453 and 31570547)Fundamental Research Funds for the Central Universities(Grant No.2572017EB02)Natural Science Foundation of Heilongjiang Province,China(Grant No.C201403)
文摘To investigate the effects of temperature and moisture content(MC) on acoustic wave velocity(AWV)in wood,the relationships between wood temperature,MC,and AWV were theoretically analyzed.According to the theoretical propagation characteristics of the acoustic waves in the wood mixture and the differences in velocity among various media(including ice,water,pure wood or oven-dried wood),theoretical relationships of temperature,MC,and AWV were established,assuming that the samples in question were composed of a simple mixture of wood and water or of wood and ice.Using the theoretical model,the phase transition of AWV in green wood near the freezing point(as derived from previous experimental results) was plausibly described.By comparative analysis between theoretical and experimental models for American red pine(Pinus resinosa) samples,it was established that the theoretically predicted AWV values matched the experiment results when the temperature of the wood was below the freezing point of water,with an averageprediction error of 1.66%.The theoretically predicted AWV increased quickly in green wood as temperature decreased and changed suddenly near 0 °C,consistent with the experimental observations.The prediction error of the model was relatively large when the temperature of the wood was above the freezing point,probably due to an overestimation of the effect of the liquid water content on the acoustic velocity and the limited variables of the model.The high correlation between the predicted and measured acoustic velocity values in frozen wood samples revealed the mechanisms of temperature,MC,and water status and how these affected the wood(particularly its acoustic velocity below freezing point of water).This result also verified the reliability of a previous experimental model used to adjust for the effect of temperature during field testing of trees.
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.
文摘In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32.2?-?54.1 for triaxial unconsolidated undrained shear test. The results show that?Cuu,?K?and?n?values all showed a downward trend, and?Rf?variation was not obvious with the increase of moisture content. The variation rule of each parameter is not obvious with the increase of plasticity index. When moisture content is constant,?Cuu?and?n?values do not change much,?K?increases with the increase of plasticity index within the range of 70%?-?80% moisture content, and does not change much with the increase of plasticity index when moisture content is greater than 80%,?Rf?has no obvious rule.?When the plasticity index is constant,?Cuu,?Kand?n?decrease with the increase of moisture content,?Rf?has no obvious rule. The maximum value of?Cuu?is 20.18?kPa, the minimum is 3.72?kPa, and the maximum to minimum ratio is 5.42. The maximum value of?K?is 0.517, the minimum is 0.022, and the maximum to minimum ratio is 23.5. The maximum value of?n?is 1.198, the minimum is 0.150, and the maximum to minimum ratio is 7.99. The maximum value of?Rf?is 0.872, the minimum is 0.679, and the maximum to minimum ratio is 1.28.
基金financially supported by the Special Fund for Forest Scientific Research in the Public Welfare(No.201404402)Fundamental Research Funds for the Central Universities(Nos.C2572014BA23 and 2572019BA03)。
文摘Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.
基金Supported by the National Natural Science Foundation of China(51275145)
文摘According to the existing method including testing the frequency and establishing the relationship between moisture content and frequency, a corresponding instrument was designed. In order to further improve the accuracy and rapidity of the system, a new approach to describe the relationship between the measurement error and the temperature was proposed. The error band could be obtained and divided into several parts(based on the range of temperature) to indicate the error value that should compensate the grain moisture content for the changes in temperature. By calculating the error band at the maximum and the minimum operating temperatures, as well as by determining the error compensation value from the error band based on the measurement moisture content, the final effective result was derived.
基金This research was supported by a grant from the National Research Foundation of Korea provided by the government of Republic of Korea(2019R1A2C1085686).
文摘Soyang Lake is the largest lake in Republic of Korea bordering Chuncheon,Yanggu,and Inje in Gangwon Province.It is widely used as an environmental resource for hydropower,flood control,and water supply.Therefore,we conducted a survey of the floodplain of Soyang Lake to analyze the sediments in the area.We used global positioning system(GPS)data and aerial photography to monitor sediment deposits in the Soyang Lake floodplain.Data from three GPS units were compared to determine the accuracy of sampling location measurement.Sediment samples were collected at three sites:two in the eastern region of the floodplain and one in the western region.A total of eight samples were collected:Three samples were collected at 10 cm intervals to a depth of 30 cm from each site of the eastern sampling point,and two samples were collected at depths of 10 and 30 cm at the western sampling point.Samples were collected and analyzed for vertical and horizontal trends in particle size and moisture content.The sizes of the sediment samples ranged from coarse to very coarse sediments with a negative slope,which indicate eastward movement from the breach.The probability of a breach was indicated by the high water content at the eastern side of the floodplain,with the eastern sites showing a higher probability than the western sites.The results of this study indicate that analyses of grain fineness,moisture content,sediment deposits,and sediment removal rates can be used to understand and predict the direction of breach movement and sediment distribution in Soyang Lake.
基金supported by the National Natural Science Foundation of China(Grant Nos:42107209and 41530640)the National Key Research and Development Program of China(Grant No:2018YFC1504701)。
文摘Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the variability of loess slope SWC through the change of resistivity.It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors.In this context,geoelectric and environmental factors such as soil temperature and SWC were monitored for three years.The prediction model of apparent resistivity and SWC was calibrated.The post processing of geoelectric data was introduced.In addition,the SWC collected by Time-Domain Reflectometry(TDR)was used to verify the feasibility of electrical resistivity tomography(ERT)data.The SWC variability in the process of rainfall,the evolution of four seasons,and the alternation of drying and wetting were evaluated.The research results show that:i)the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5-3.0 m depth.ii)The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring.iii)From 2017 to 2020,the SWC increased in August,and the soil became dry in January.iv)Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity.The outcome can provide the change information of SWC to a great extent without excavating boreholes.
基金Ministry of Science,Technology and Innovation(MOSTI)for National Science Fellowship(NSF).
文摘The commercial open-ended coaxial probe(Agilent 85070E)is the most commonly used sensor to determine the permittivity of wet materials.This paper extends the usability and applicability of the sensor to the estimation of moisture content in Hevea Rubber Latex.The dielectric constant and loss factor were measured using the commercial probe whilst the moisture contents were obtained using the standard oven drying method.Comparison results were obtained between the different dielectric models to predict moisture content in latex.Both the dielectric constant and the loss factor of rubber latex linearly increased with moisture content at all selected frequencies.Calibration equations were established to relate both the dielectric constant and the loss factor with moisture content.These equations were used to predict moisture content in Hevea latex from measured values of the dielectric constant and the loss factor.The lowest mean relative error between actual and predicted moisture contents was 0.02 at 1 GHz when using the Cole-Cole dielectric constant calibration equation.
文摘A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seaweed were known to be used in this system. The non-linear regression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed being dried on the daily average of air temperature about 40℃. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.99027), the lowest MBE (0.00044) and RMSE (0.03039) indicated that the Page model was the best mathematical model to describe the drying behavior of sauna dried seaweed. The percentage of the saved time using this technique was calculated at 57.9% on the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s.
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.
文摘木材密度包括基本密度、气干密度等,在12%含水率条件下的气干密度(D12)较常用,因此有必要将木材气干密度换算为基本密度(Db)。目前利用木材气干密度计算基本密度的模型有Reyes、Chave、Simpson和Vieilledent模型等,然而这些模型预测结果不完全一致。利用中国林业科学研究院木材工业研究所(Research Institute of Wood Industry,Chinese Academy of Forestry,CRIWI)和法国农业国际合作研究发展中心(French Agricultural Research Centre for International Development,CIRAD)的木材D12和Db数据,首先基于CRIWI的木材密度数据建立D12与Db的关系模型,然后将CRIWI和CIRAD的D12数据分别代入Reyes模型、Chave模型、Simpson模型、Vieilledent模型和新建模型,获得每个树种木材Db的预测值,并根据Db预测值和实测值计算残差绝对值均值。不同模型残差绝对值均值比较结果表明:Reyes模型在利用CRIWI和CIRAD的木材密度数据时预测Db的准确性都比较高,适用性最广;Simpson模型、新建模型在D12高于1.0 g/cm3时预测Db的准确性降低。
文摘花椒热风干燥降速期水分含量低,水分扩散慢,导致热风干燥耗时长。为提高干燥效率,并通过热风与微波组合干燥,分别进行热风干燥、微波干燥和热风-微波组合干燥实验,探究不同干燥参数对花椒失水特性的影响,以确定合理的干燥转换临界点和最优组合干燥模型,并将傅里叶准则数(F_(0))引入Fick第二扩散定律方程,求解有效水分扩散系数(D_(eff))。研究结果表明:热风和微波单独干燥时,升高风温风速和增加微波功率均有利于缩短干燥时间;热风-微波组合干燥花椒时,热风段转微波段的最佳目标含水率即为热风干燥的临界点含水率(65%(w.b)),且高热风温度和高微波功率均可使微波干燥段获得高失水速率;热风-微波组合干燥花椒热风段和微波段对应的最优模型分别为Wang and Singh模型和Page模型,D_(eff)范围分别为1.908×10^(-9)~3.547×10^(-9)m^(2)/s和1.883×10^(-8)~3.321×10^(-8)m^(2)/s。热风-微波组合干燥方式能够显著提高干燥效率,促进花椒内部水分扩散,干燥模型可为优化干燥工艺和设计干燥设备提供理论依据。