This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂...【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂的高精度动力学模型和空间在轨动力学控制研究奠定基础。【方法】首先,采用一种基于终端交叉和转向的粒子群优化(Terminal Crossover and Steering-based Particle Swarm Optimization,TCS-PSO)算法来设计满足多约束条件的周期傅里叶级数,并将其作为最优的激励轨迹;其次,应用迭代加权最小二乘(Iterative Weighted Least Squares,IWLS)法获取最小参数集,通过迭代加权逐步剔除数据中的异常值,使得辨识结果更加鲁棒、准确。【结果】试验结果表明,在激励轨迹中,采用TCS优化方法获得的轨迹回归矩阵条件数更少,且能更好满足所给的约束条件。在参数辨识中,采用IWLS法辨识所得的结果对比递归最小二乘法,力矩残差均方根(Root Mean Square,RMS)值平均降低约2.22%;对比加权最小二乘法,力矩残差RMS值平均降低约4.85%。将获取的参数模型代入到零力控制试验中,实际效果符合预期。展开更多
A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle（FAHV）.By utilizing functional decomposition method, the dynamics of FAHV is ...A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle（FAHV）.By utilizing functional decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude subsystem.For each subsystem, only one neural network is employed for the unknown function approximation.To further reduce the computational burden, minimal-learning parameter（MLP）technology is used to estimate the norm of ideal weight vectors rather than their elements.By introducing sliding mode differentiator（SMD） to estimate the newly defined variables, there is no need for the strict-feedback form and virtual controller.Hence the developed control law is considerably simpler than the ones derived from back-stepping scheme.Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.展开更多
Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software c...Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 attributes.By applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model accuracy.Our findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation accuracy.It is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.展开更多
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
文摘【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂的高精度动力学模型和空间在轨动力学控制研究奠定基础。【方法】首先,采用一种基于终端交叉和转向的粒子群优化(Terminal Crossover and Steering-based Particle Swarm Optimization,TCS-PSO)算法来设计满足多约束条件的周期傅里叶级数,并将其作为最优的激励轨迹;其次,应用迭代加权最小二乘(Iterative Weighted Least Squares,IWLS)法获取最小参数集,通过迭代加权逐步剔除数据中的异常值,使得辨识结果更加鲁棒、准确。【结果】试验结果表明,在激励轨迹中,采用TCS优化方法获得的轨迹回归矩阵条件数更少,且能更好满足所给的约束条件。在参数辨识中,采用IWLS法辨识所得的结果对比递归最小二乘法,力矩残差均方根(Root Mean Square,RMS)值平均降低约2.22%;对比加权最小二乘法,力矩残差RMS值平均降低约4.85%。将获取的参数模型代入到零力控制试验中,实际效果符合预期。
基金supported by the Aeronautical Science Foundation of China (No.20130196004)
文摘A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle（FAHV）.By utilizing functional decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude subsystem.For each subsystem, only one neural network is employed for the unknown function approximation.To further reduce the computational burden, minimal-learning parameter（MLP）technology is used to estimate the norm of ideal weight vectors rather than their elements.By introducing sliding mode differentiator（SMD） to estimate the newly defined variables, there is no need for the strict-feedback form and virtual controller.Hence the developed control law is considerably simpler than the ones derived from back-stepping scheme.Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.
文摘Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 attributes.By applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model accuracy.Our findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation accuracy.It is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.
