We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-base...We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.展开更多
为提高元分类器的预测精度,在基于分类器类向量输出的Stacking算法基础上,提出一种基于熵权法的堆叠泛化算法E-Stacking (Stacking based entropy),对于基分类器的输出类别,引入一种熵权法ELFMF (label frequency and mistake frequency...为提高元分类器的预测精度,在基于分类器类向量输出的Stacking算法基础上,提出一种基于熵权法的堆叠泛化算法E-Stacking (Stacking based entropy),对于基分类器的输出类别,引入一种熵权法ELFMF (label frequency and mistake frequency based entropy)。通过考虑基分类器预测结果出现的频率及错误率,以及预测结果在各个类别中的分散度,增强多个元分类器成员之间的差异性,增加堆叠算法的泛化效果。实验结果表明,与传统及各种改进的Stacking算法相比,该算法有效提高了预测精度且更具有适用性。展开更多
采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,...采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,利用机器学习风速与有效波高之间的关系,将2016年10-11月的风速、波高数据用于预报结果的对比分析,预报前45 d R^2拟合优度达到0.97以上,平均误差最大值为0.08 m,平均相对误差最大值为0.05,预报结果与波浪谱模型结果趋势一致,准确度较高;预报结果后15 d误差增长较快,这与训练集数据中寒潮浪占比较少有关。展开更多
It is necessary to understand the features of air pressure in a drainage stack of a high-rise building for properly designing and operating a drainage system. This paper presents a mathematical model for predicting th...It is necessary to understand the features of air pressure in a drainage stack of a high-rise building for properly designing and operating a drainage system. This paper presents a mathematical model for predicting the stack performance. A step function is used to describe the effect of the air entrainment caused by the water discharged from branch pipes. An additional source term is introduced to reflect the gas-liquid interphase interaction （GLII） and stack base effect. The drainage stack is divided into upper and base parts. The air pressure in the upper part is predicted by a total variation diminishing （TVD） scheme, while in the base part, it is predicted by a characteristic line method （CLM）. The predicted results are compared with the data measured in a real-scale high- rise test building. It is found that the additional source term in the present model is effective. It intensively influences the air pressure distribution in the stack. The air pressure is also sensitive to the velocity-adjusting parameter （VAP）, the branch pipe air entrainment, and the conditions on the stack bottom.展开更多
基金supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 200983512)Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A6A3A03063900)the Brain Korea 21
文摘We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.
文摘为提高元分类器的预测精度,在基于分类器类向量输出的Stacking算法基础上,提出一种基于熵权法的堆叠泛化算法E-Stacking (Stacking based entropy),对于基分类器的输出类别,引入一种熵权法ELFMF (label frequency and mistake frequency based entropy)。通过考虑基分类器预测结果出现的频率及错误率,以及预测结果在各个类别中的分散度,增强多个元分类器成员之间的差异性,增加堆叠算法的泛化效果。实验结果表明,与传统及各种改进的Stacking算法相比,该算法有效提高了预测精度且更具有适用性。
文摘采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,利用机器学习风速与有效波高之间的关系,将2016年10-11月的风速、波高数据用于预报结果的对比分析,预报前45 d R^2拟合优度达到0.97以上,平均误差最大值为0.08 m,平均相对误差最大值为0.05,预报结果与波浪谱模型结果趋势一致,准确度较高;预报结果后15 d误差增长较快,这与训练集数据中寒潮浪占比较少有关。
基金Project supported by the National Natural Science Foundation of China (No. 10972212)
文摘It is necessary to understand the features of air pressure in a drainage stack of a high-rise building for properly designing and operating a drainage system. This paper presents a mathematical model for predicting the stack performance. A step function is used to describe the effect of the air entrainment caused by the water discharged from branch pipes. An additional source term is introduced to reflect the gas-liquid interphase interaction （GLII） and stack base effect. The drainage stack is divided into upper and base parts. The air pressure in the upper part is predicted by a total variation diminishing （TVD） scheme, while in the base part, it is predicted by a characteristic line method （CLM）. The predicted results are compared with the data measured in a real-scale high- rise test building. It is found that the additional source term in the present model is effective. It intensively influences the air pressure distribution in the stack. The air pressure is also sensitive to the velocity-adjusting parameter （VAP）, the branch pipe air entrainment, and the conditions on the stack bottom.
