Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challen...Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.展开更多
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ...Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.展开更多
为了解决传统变换器电压增益低的问题,将Boost变换器与Cuk变换器进行并联集成,并利用耦合电感倍压技术提高变换器的电压增益。设计而成的高增益耦合电感组合Boost-Cuk变换器保留了Cuk变换器输出电流的连续性,新型结构中使用无源钳位来...为了解决传统变换器电压增益低的问题,将Boost变换器与Cuk变换器进行并联集成,并利用耦合电感倍压技术提高变换器的电压增益。设计而成的高增益耦合电感组合Boost-Cuk变换器保留了Cuk变换器输出电流的连续性,新型结构中使用无源钳位来吸收漏感能量,对寄生电容与漏感谐振引起的电压尖峰起到约束作用,降低了开关管的电压应力。描述了变换器电感电流连续模式(Continuous current mode,CCM)下的运行特点,并进行了该变换器的参数设计。最后,通过搭建一台100 W的试验样机来求证理论的正确性。展开更多
协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两...协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。展开更多
提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制...提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。展开更多
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
基金funded by the Natural Science Foundation of Chongqing(Grants No.CSTB2022NSCQ-MSX0594)the Humanities and Social Sciences Research Project of the Ministry of Education(Grants No.16YJCZH061).
文摘Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.
文摘Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.
文摘为了解决传统变换器电压增益低的问题,将Boost变换器与Cuk变换器进行并联集成,并利用耦合电感倍压技术提高变换器的电压增益。设计而成的高增益耦合电感组合Boost-Cuk变换器保留了Cuk变换器输出电流的连续性,新型结构中使用无源钳位来吸收漏感能量,对寄生电容与漏感谐振引起的电压尖峰起到约束作用,降低了开关管的电压应力。描述了变换器电感电流连续模式(Continuous current mode,CCM)下的运行特点,并进行了该变换器的参数设计。最后,通过搭建一台100 W的试验样机来求证理论的正确性。
文摘协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。
文摘提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。
