The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computatio...The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computational fluid dynamics and the flexible rod dynamics is proposed using a two-way domain expansion method.The gov-erning equations of the flexible rod dynamics are discretized and solved by the finite element method,and the fluid flow is simulated by the finite volume method.The interaction between fluids and solid rods is modeled by introducing body force terms into the momentum equations.Referred to the traditional semi-resolved numerical model,an anisotropic Gaussian kernel function method is proposed to specify the interactive forces between flu-ids and solid bodies for non-circle rod cross-sections.A benchmark of the flow passing around a single flexible plate with a rectangular cross-section is used to validate the algorithm.Focused on the engineering applications,a test case of a finite patch of cylinders is implemented to validate the accuracy and efficiency of the coupled model.展开更多
随着新能源汽车行业的迅猛发展,车载控制器局域网络(Controller Area Network,CAN)安全防护研究的重要性日益递增。为检测CAN总线异常攻击,保障车辆安全,提出一种基于支持向量数据描述(Support Vector Data Description,SVDD)的车载CAN...随着新能源汽车行业的迅猛发展,车载控制器局域网络(Controller Area Network,CAN)安全防护研究的重要性日益递增。为检测CAN总线异常攻击,保障车辆安全,提出一种基于支持向量数据描述(Support Vector Data Description,SVDD)的车载CAN总线入侵检测方法。提取CAN报文标识符和数据域的数据作为特征信息,经过数据预处理和PCA降维后,输入SVDD模型进行入侵检测。在模型训练中,选用高斯核函数以提高SVDD入侵检测模型的拟合能力,减少模型的冗余面积。实验表明,该文方法在保证了较高召回率和F1分数的同时,比传统SVDD模型的准确率提升了9.66%,与其他四种模型对比,其综合性能更好。展开更多
Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed b...Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed based on the initial corneal information collected using the Placido disc.A corneal point cloud model was established in polar coordinates,and an interpolation algorithm was proposed to fill missing points of the local bicubic B-spline by searching control points in the selfdefined interpolation matrix.The grid interpolation of the point cloud information and the smooth imaging of the final topographic map were achieved by Delaunay triangulation and Gaussian kernel function smoothing.Experiment results show that the proposed interpolation algorithm has higher accuracy than previous algorithms.The mean absolute error between the measured diopter of the original detection and the reconstructed is less than 0.300 D,indicating that this algorithm is feasible.展开更多
锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定...锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。展开更多
基金supported by Shanghai 2021“Science and Technology Innovation Action Plan”:Social Development Science and Technology Research Project(Grant No.21DZ1202703).
文摘The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computational fluid dynamics and the flexible rod dynamics is proposed using a two-way domain expansion method.The gov-erning equations of the flexible rod dynamics are discretized and solved by the finite element method,and the fluid flow is simulated by the finite volume method.The interaction between fluids and solid rods is modeled by introducing body force terms into the momentum equations.Referred to the traditional semi-resolved numerical model,an anisotropic Gaussian kernel function method is proposed to specify the interactive forces between flu-ids and solid bodies for non-circle rod cross-sections.A benchmark of the flow passing around a single flexible plate with a rectangular cross-section is used to validate the algorithm.Focused on the engineering applications,a test case of a finite patch of cylinders is implemented to validate the accuracy and efficiency of the coupled model.
文摘随着新能源汽车行业的迅猛发展,车载控制器局域网络(Controller Area Network,CAN)安全防护研究的重要性日益递增。为检测CAN总线异常攻击,保障车辆安全,提出一种基于支持向量数据描述(Support Vector Data Description,SVDD)的车载CAN总线入侵检测方法。提取CAN报文标识符和数据域的数据作为特征信息,经过数据预处理和PCA降维后,输入SVDD模型进行入侵检测。在模型训练中,选用高斯核函数以提高SVDD入侵检测模型的拟合能力,减少模型的冗余面积。实验表明,该文方法在保证了较高召回率和F1分数的同时,比传统SVDD模型的准确率提升了9.66%,与其他四种模型对比,其综合性能更好。
基金Shanghai Science and Technology Program,China (No.20DZ2251400)。
文摘Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed based on the initial corneal information collected using the Placido disc.A corneal point cloud model was established in polar coordinates,and an interpolation algorithm was proposed to fill missing points of the local bicubic B-spline by searching control points in the selfdefined interpolation matrix.The grid interpolation of the point cloud information and the smooth imaging of the final topographic map were achieved by Delaunay triangulation and Gaussian kernel function smoothing.Experiment results show that the proposed interpolation algorithm has higher accuracy than previous algorithms.The mean absolute error between the measured diopter of the original detection and the reconstructed is less than 0.300 D,indicating that this algorithm is feasible.
文摘锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。
