The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central an...The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central and southern regions of North China on 22 May 2017.The microphysical structural characteristics and processes near the embedded convection core and in the stratiform cloud are analyzed comparatively.Particular attention is given to the effect of riming on the microphysical properties near the upper boundary of the melting layer and to the factors influencing riming efficiency.The collaborative observations reveal that the particle size distributions observed near the convection core and in the stratiform region are close,while the particle properties like habit and riming degree are quite different.Above the melting layer,larger plate-like ice particles and supercooled water droplets(D>50μm)are more abundant near the convective core,leading to higher collision efficiencies between ice particles and supercooled water droplets.Larger fluctuation amplitudes of vertical airflow near the convective core also contribute to the increased riming activity and the formation of more heavily rimed particles,such as graupel.Furthermore,in situ measurements from airborne probes also revealed that above the melting layer,the riming process involves two stages:the mass of snow crystals grows as supercooled droplets merge internally without changing size,followed by external freezing that significantly enlarges the crystals.展开更多
[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,...Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.展开更多
This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow r...This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow rime on thousands of trees;conquer the ice and snow wilderness on a snowmobile and start an in depth magical mystery tour in lilin Province.展开更多
In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonder...In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonders of the ice city to the snowcovered wonderland of rime ice.展开更多
Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polari...Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergenceδderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP)formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.展开更多
When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to ...When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.展开更多
The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which ...The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.展开更多
The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the tw...The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the two-phase model of air/super-cooled droplets in the Eulerian coordinate system, this paper presents the simulation of the rime ice accretion on the NACA 0012 airfoil. The predicted rime ice shape is compared with those results of measurements and simulations by other icing codes. Also the resulting effects of rime ice on airfoil aerodynamic performance are discussed. Results indicate that the rime ice accretion leads to the loss of the maximum lift coefficient by 26%, the decrease of the stall angle by about 3° and the considerable increase of the drag coefficient.展开更多
Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall eve...Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall event on 20–21 April 2010.The simulation output was compared with precipitation and aircraft observations.The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage.Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles.The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other.However,differences existed in the temporal and spatial evolutions of the precipitation distribution.An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison,while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed.The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles.P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42025501)the Natural Science Foundation of Hebei Province(Grant No.D2024304015)+4 种基金the Fundamental Research Funds for the Central Universities,including Grant No.020714380217the Cemac“GeoX”Interdisciplinary Program(Grant No.020714380210)the Open Grants of the Key Laboratory of Radar Meteorology,China Meteorological Administration(Grant No.2023LRM-B05)the Hebei Meteorological Service Scientific Research and Development Project(Grant No.23ky08)the Open Research Program of the State Key Laboratory of Severe Weather(Grant No.2023LASW-A01)。
文摘The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central and southern regions of North China on 22 May 2017.The microphysical structural characteristics and processes near the embedded convection core and in the stratiform cloud are analyzed comparatively.Particular attention is given to the effect of riming on the microphysical properties near the upper boundary of the melting layer and to the factors influencing riming efficiency.The collaborative observations reveal that the particle size distributions observed near the convection core and in the stratiform region are close,while the particle properties like habit and riming degree are quite different.Above the melting layer,larger plate-like ice particles and supercooled water droplets(D>50μm)are more abundant near the convective core,leading to higher collision efficiencies between ice particles and supercooled water droplets.Larger fluctuation amplitudes of vertical airflow near the convective core also contribute to the increased riming activity and the formation of more heavily rimed particles,such as graupel.Furthermore,in situ measurements from airborne probes also revealed that above the melting layer,the riming process involves two stages:the mass of snow crystals grows as supercooled droplets merge internally without changing size,followed by external freezing that significantly enlarges the crystals.
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
基金supported by the National Natural Science Foundation of China[grant number 51775020]the Science Challenge Project[grant number.TZ2018007]+2 种基金the National Natural Science Foundation of China[grant number 62073009]the Postdoctoral Fellowship Program of CPSF[grant number GZC20233365]the Fundamental Research Funds for Central Universities[grant number JKF-20240559].
文摘Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.
文摘This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow rime on thousands of trees;conquer the ice and snow wilderness on a snowmobile and start an in depth magical mystery tour in lilin Province.
文摘In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonders of the ice city to the snowcovered wonderland of rime ice.
基金jointly funded by the Northwest Regional Weather Modification Capacity Building Project of the China Meteorological Administration(Grant No.ZQC-R18209)the National Natural Science Foundation of China(Grant Nos.41875172 and 42075192)。
文摘Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergenceδderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP)formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.
基金supported by the Yunnan Major Scientific and Technological Projects(Grant No.202302AD080001)the National Natural Science Foundation,China(No.52065033).
文摘When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185,62301367).
文摘The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
文摘The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the two-phase model of air/super-cooled droplets in the Eulerian coordinate system, this paper presents the simulation of the rime ice accretion on the NACA 0012 airfoil. The predicted rime ice shape is compared with those results of measurements and simulations by other icing codes. Also the resulting effects of rime ice on airfoil aerodynamic performance are discussed. Results indicate that the rime ice accretion leads to the loss of the maximum lift coefficient by 26%, the decrease of the stall angle by about 3° and the considerable increase of the drag coefficient.
基金supported by the National Key Research and Development Program of China (Grant No. 2018YFC1507900)the National Natural Science Foundation of China (Grant Nos. 41575131, 41530427 and 41875172)
文摘Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall event on 20–21 April 2010.The simulation output was compared with precipitation and aircraft observations.The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage.Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles.The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other.However,differences existed in the temporal and spatial evolutions of the precipitation distribution.An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison,while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed.The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles.P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.
