数据清洗、特征选择和预测模型建立是基于数据采集与监视控制系统(supervisory control and data acquisition,SCADA)数据,实现风电机组异常状态预警不可缺少的重要环节。先结合孤立森林(isolation forest,iForest)和基于密度的空间聚类...数据清洗、特征选择和预测模型建立是基于数据采集与监视控制系统(supervisory control and data acquisition,SCADA)数据,实现风电机组异常状态预警不可缺少的重要环节。先结合孤立森林(isolation forest,iForest)和基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对SCADA数据异常点进行有效清洗,并采用随机森林算法(random forests,RF)与Person相关系数法优选模型输入参数;再进而基于Optuna优化的类别提升树(categorical boosting,CATBoost)算法,建立风电机组正常工况齿轮箱油池温度的预测模型;然后采用滑动窗方法,构建状态评价指标,并使用区间估计理论确定油温异常状态判别的临界阈值;实现油温异常预警;最后,采用某风电机组SCADA系统油温异常的真实历史故障数据进行检验,验证了该方法的有效性。展开更多
Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for mari...Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
文摘数据清洗、特征选择和预测模型建立是基于数据采集与监视控制系统(supervisory control and data acquisition,SCADA)数据,实现风电机组异常状态预警不可缺少的重要环节。先结合孤立森林(isolation forest,iForest)和基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对SCADA数据异常点进行有效清洗,并采用随机森林算法(random forests,RF)与Person相关系数法优选模型输入参数;再进而基于Optuna优化的类别提升树(categorical boosting,CATBoost)算法,建立风电机组正常工况齿轮箱油池温度的预测模型;然后采用滑动窗方法,构建状态评价指标,并使用区间估计理论确定油温异常状态判别的临界阈值;实现油温异常预警;最后,采用某风电机组SCADA系统油温异常的真实历史故障数据进行检验,验证了该方法的有效性。
文摘Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
