本研究利用基于有界平均振荡模型(boundary mean oscillation, BMO)的ZQ梯度算子,结合各向异性非线性偏微分方程模型,构造用于图像处理的BMO滤波器,对水稻谷粒的数字图像进行去噪增强、边缘检测和特征提取,并对粒形参数进行统计分析。...本研究利用基于有界平均振荡模型(boundary mean oscillation, BMO)的ZQ梯度算子,结合各向异性非线性偏微分方程模型,构造用于图像处理的BMO滤波器,对水稻谷粒的数字图像进行去噪增强、边缘检测和特征提取,并对粒形参数进行统计分析。在此基础上,比较了BMO滤波与中值滤波的处理效果,并分析了BMO滤波技术的准确性与稳定性。结果表明,BMO滤波在保留图像的边界与细节特征方面显著优于中值滤波,其处理图像获取的谷粒粒长、粒宽和长宽比与人工测量值无显著差异(p <0.05),平均粒面积与千粒重正相关性强(R2 = 0.942, p <0.001),且粒形参数提取结果在不同水稻品种间有较好的稳定性。展开更多
This work investigates the boreal-summer intraseasonal variability(ISV)of the precipitation over the lower reaches of the Yangtze River basin(LYRB)during 1979–2016,based on daily Climate Prediction Center global prec...This work investigates the boreal-summer intraseasonal variability(ISV)of the precipitation over the lower reaches of the Yangtze River basin(LYRB)during 1979–2016,based on daily Climate Prediction Center global precipitation data.The ISV of the summer monsoon rainfall over the LYRB is mainly dominated by the lower-frequency 12–20-day variability and the higher-frequency 8–12-day variability.The lower-frequency variability is found to be related to the northwestwardpropagating quasi-biweekly oscillation(QBWO)over the western North Pacific spanning the South China Sea(SCS)and Philippine Sea,while the higher-frequency variability is related to the southeastward propagating midlatitude wave train(MLWT).Moreover,not each active QBWO(MLWT)in the SCS(East Asia)can generate ISV components of the precipitation anomaly over the LYRB.The QBWO can change the rainfall significantly with the modulation of mean state precipitation,while the quasi-11-day mode mainly depends on the intensity of the MLWT rather than the mean precipitation change.These findings should enrich our understanding of the ISV of the East Asian summer monsoon and improve its predictability.展开更多
As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a loo...As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.展开更多
Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumen...Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson's correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate 'good' precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.展开更多
文摘本研究利用基于有界平均振荡模型(boundary mean oscillation, BMO)的ZQ梯度算子,结合各向异性非线性偏微分方程模型,构造用于图像处理的BMO滤波器,对水稻谷粒的数字图像进行去噪增强、边缘检测和特征提取,并对粒形参数进行统计分析。在此基础上,比较了BMO滤波与中值滤波的处理效果,并分析了BMO滤波技术的准确性与稳定性。结果表明,BMO滤波在保留图像的边界与细节特征方面显著优于中值滤波,其处理图像获取的谷粒粒长、粒宽和长宽比与人工测量值无显著差异(p <0.05),平均粒面积与千粒重正相关性强(R2 = 0.942, p <0.001),且粒形参数提取结果在不同水稻品种间有较好的稳定性。
基金This work was supported by the National Natural Science Foundation of China[grant number 41420104002]the Natural Science Foundation of Jiangsu Province[grant numbers BK20150907 and 14KJA170002].
文摘This work investigates the boreal-summer intraseasonal variability(ISV)of the precipitation over the lower reaches of the Yangtze River basin(LYRB)during 1979–2016,based on daily Climate Prediction Center global precipitation data.The ISV of the summer monsoon rainfall over the LYRB is mainly dominated by the lower-frequency 12–20-day variability and the higher-frequency 8–12-day variability.The lower-frequency variability is found to be related to the northwestwardpropagating quasi-biweekly oscillation(QBWO)over the western North Pacific spanning the South China Sea(SCS)and Philippine Sea,while the higher-frequency variability is related to the southeastward propagating midlatitude wave train(MLWT).Moreover,not each active QBWO(MLWT)in the SCS(East Asia)can generate ISV components of the precipitation anomaly over the LYRB.The QBWO can change the rainfall significantly with the modulation of mean state precipitation,while the quasi-11-day mode mainly depends on the intensity of the MLWT rather than the mean precipitation change.These findings should enrich our understanding of the ISV of the East Asian summer monsoon and improve its predictability.
基金Project(531107040300) supported by the Fundamental Research Funds for the Central Universities in ChinaProject(2006BAJ04B04) supported by the National Science and Technology Pillar Program during the Eleventh Five-year Plan Period of China
文摘As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.
文摘Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson's correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate 'good' precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.
