Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for...Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.展开更多
The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss i...The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.展开更多
气氛环境下原位研究催化剂的烧结行为,能够为理解催化剂在预处理以及反应条件下的烧结机理和高稳定催化剂的设计提供重要的实验依据。本文以Au/CeO_(2)模型纳米催化剂为研究对象,利用环境透射电子显微镜原位观察其在O_(2)与CO气氛下的...气氛环境下原位研究催化剂的烧结行为,能够为理解催化剂在预处理以及反应条件下的烧结机理和高稳定催化剂的设计提供重要的实验依据。本文以Au/CeO_(2)模型纳米催化剂为研究对象,利用环境透射电子显微镜原位观察其在O_(2)与CO气氛下的高温动态烧结过程。实验发现,负载在CeO_(2)上的Au纳米颗粒在O_(2)与CO气氛环境中表现出不同的烧结行为,其在O_(2)气氛下具有较高的烧结速度,同时存在颗粒迁移与聚集长大(particle migration and coalescence,PMC)和奥斯特瓦尔德熟化(Ostwald ripening,OR)两种烧结过程;在CO气氛下烧结速度较慢,烧结过程以OR为主。对比不同气氛环境下烧结后催化剂的表面结构可知,CO增加了CeO_(2)表面台阶的数量以及表面氧空位浓度,增强了载体对Au颗粒的锚定作用,从而提升Au/CeO_(2)催化剂的稳定性。展开更多
基金National Key R&D Program of China(2022YFB3605702)National Natural Science Foundation of China(61925508,61905289)+2 种基金Key-area Research and Development Program of Guangdong Province(2020B090922006)Guangzhou Science and Technology Project(202201010427)CAS Project for Young Scientists in Basic Research(YSBR-024)。
基金financially supported by the National Council for Scientific and Technological Development(CNPq,Brazil),Swedish-Brazilian Research and Innovation Centre(CISB),and Saab AB under Grant No.CNPq:200053/2022-1the National Council for Scientific and Technological Development(CNPq,Brazil)under Grants No.CNPq:312924/2017-8 and No.CNPq:314660/2020-8.
文摘Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
文摘The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.
文摘气氛环境下原位研究催化剂的烧结行为,能够为理解催化剂在预处理以及反应条件下的烧结机理和高稳定催化剂的设计提供重要的实验依据。本文以Au/CeO_(2)模型纳米催化剂为研究对象,利用环境透射电子显微镜原位观察其在O_(2)与CO气氛下的高温动态烧结过程。实验发现,负载在CeO_(2)上的Au纳米颗粒在O_(2)与CO气氛环境中表现出不同的烧结行为,其在O_(2)气氛下具有较高的烧结速度,同时存在颗粒迁移与聚集长大(particle migration and coalescence,PMC)和奥斯特瓦尔德熟化(Ostwald ripening,OR)两种烧结过程;在CO气氛下烧结速度较慢,烧结过程以OR为主。对比不同气氛环境下烧结后催化剂的表面结构可知,CO增加了CeO_(2)表面台阶的数量以及表面氧空位浓度,增强了载体对Au颗粒的锚定作用,从而提升Au/CeO_(2)催化剂的稳定性。
