针对间歇生产过程中,采集的数据存在非高斯、非线性的特征,本文将支持向量数据描述(Support Vector Data Description,SVDD)的方法应用到间歇过程故障监测中。首先,将数据按照批次展开并进行标准化,再按照变量展开;然后,建立SVDD模型,...针对间歇生产过程中,采集的数据存在非高斯、非线性的特征,本文将支持向量数据描述(Support Vector Data Description,SVDD)的方法应用到间歇过程故障监测中。首先,将数据按照批次展开并进行标准化,再按照变量展开;然后,建立SVDD模型,应用核函数求出模型半径R(?)对新的待检测样本,先计算其与模型中心的距离,再与半径比较,判断它是否正常。因为SVDD可以利用核函数替代向量内积的计算,所以能够解决非高斯、非线性数据的检测问题。最后,在青霉素发酵过程监测的成功应用,验证了该方法的有效性、准确性。展开更多
A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling...A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.展开更多
For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature ...For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model.Therefore,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation.Then,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal state.Finally,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.展开更多
采用径向基函数神经网络(Radical Basis Function Neutral Networks,简称RBF神经网络)来模拟大跨度结构的非高斯风压场.根据某大跨度结构的形式特点,将结构风场看成是屋面位置和时间的函数,将风压场分解为一系列径向基函数.再利用单调...采用径向基函数神经网络(Radical Basis Function Neutral Networks,简称RBF神经网络)来模拟大跨度结构的非高斯风压场.根据某大跨度结构的形式特点,将结构风场看成是屋面位置和时间的函数,将风压场分解为一系列径向基函数.再利用单调非线性无记忆转换映射和RBF中获得的风场函数定义向量过程,从而将非高斯场的模拟转换为互相关高斯过程的模拟.将RBF神经网络应用于一大跨度屋盖的非高斯场模拟,得到结构上非高斯风压场的分布.结果对比表明,RBF神经网络模拟非高斯风压场具有较高的准确性.该方法可直接利用RBF神经网络的输出结果,避免推导高斯过程和非高斯过程的关系式,因此具有较高的效率.RBF神经网络模拟非高斯风压场在准确性和效率上均具有显著优势.展开更多
Stochastic simulation is an important means of acquiring fluctuating wind pressures for wind induced response analyses in structural engineering. The wind pressure acting on a large-span space structure can be charact...Stochastic simulation is an important means of acquiring fluctuating wind pressures for wind induced response analyses in structural engineering. The wind pressure acting on a large-span space structure can be characterized as a stationary non-Gaussian field. This paper reviews several simulation algorithms related to the Spectral Representation Method （SRM） and the Static Transformation Method （STM）. Polynomial and Exponential transformation functions （PSTM and ESTM） are discussed. Deficiencies in current algorithms, with respect to accuracy, stability and efficiency, are analyzed, and the algorithms are improved for better practical application. In order to verify the improved algorithm, wind pressure fields on a large-span roof are simulated and compared with wind tunnel data. The simulation results fit well with the wind tunnel data, and the algorithm accuracy, stability and efficiency are shown to be better than those of current algorithms.展开更多
Both wave-frequency(WF) and low-frequency(LF) components of mooring tension are in principle non-Gaussian due to nonlinearities in the dynamic system.This paper conducts a comprehensive investigation of applicable pro...Both wave-frequency(WF) and low-frequency(LF) components of mooring tension are in principle non-Gaussian due to nonlinearities in the dynamic system.This paper conducts a comprehensive investigation of applicable probability density functions(PDFs) of mooring tension amplitudes used to assess mooring-line fatigue damage via the spectral method.Short-term statistical characteristics of mooring-line tension responses are firstly investigated,in which the discrepancy arising from Gaussian approximation is revealed by comparing kurtosis and skewness coefficients.Several distribution functions based on present analytical spectral methods are selected to express the statistical distribution of the mooring-line tension amplitudes.Results indicate that the Gamma-type distribution and a linear combination of Dirlik and Tovo-Benasciutti formulas are suitable for separate WF and LF mooring tension components.A novel parametric method based on nonlinear transformations and stochastic optimization is then proposed to increase the effectiveness of mooring-line fatigue assessment due to non-Gaussian bimodal tension responses.Using time domain simulation as a benchmark,its accuracy is further validated using a numerical case study of a moored semi-submersible platform.展开更多
Brownian particles suspended in disordered crowded environments often exhibit non-Gaussian normal diffusion(NGND),whereby their displacements grow with mean square proportional to the observation time and non-Gaussian...Brownian particles suspended in disordered crowded environments often exhibit non-Gaussian normal diffusion(NGND),whereby their displacements grow with mean square proportional to the observation time and non-Gaussian statistics.Their distributions appear to decay almost exponentially according to“universal”laws largely insensitive to the observation time.This effect is generically attributed to slow environmental fluctuations,which perturb the local configuration of the suspension medium.To investigate the microscopic mechanisms responsible for the NGND phenomenon,we study Brownian diffusion in low dimensional systems,like the free diffusion of ellipsoidal and active particles,the diffusion of colloidal particles in fluctuating corrugated channels and Brownian motion in arrays of planar convective rolls.NGND appears to be a transient effect related to the time modulation of the instantaneous particle’s diffusivity,which can occur even under equilibrium conditions.Consequently,we propose to generalize the definition of NGND to include transient displacement distributions which vary continuously with the observation time.To this purpose,we provide a heuristic one-parameter function,which fits all time-dependent transient displacement distributions corresponding to the same diffusion constant.Moreover,we reveal the existence of low dimensional systems where the NGND distributions are not leptokurtic(fat exponential tails),as often reported in the literature,but platykurtic(thin sub-Gaussian tails),i.e.,with negative excess kurtosis.The actual nature of the NGND transients is related to the specific microscopic dynamics of the diffusing particle.展开更多
文摘针对间歇生产过程中,采集的数据存在非高斯、非线性的特征,本文将支持向量数据描述(Support Vector Data Description,SVDD)的方法应用到间歇过程故障监测中。首先,将数据按照批次展开并进行标准化,再按照变量展开;然后,建立SVDD模型,应用核函数求出模型半径R(?)对新的待检测样本,先计算其与模型中心的距离,再与半径比较,判断它是否正常。因为SVDD可以利用核函数替代向量内积的计算,所以能够解决非高斯、非线性数据的检测问题。最后,在青霉素发酵过程监测的成功应用,验证了该方法的有效性、准确性。
基金the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089) Funded by Seoul Development Institute (CS070160)
文摘A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.
基金supported by the National Natural Science Foundation of China(Nos.61873064 and 51375087)the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2016139)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX18_0073)。
文摘For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model.Therefore,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation.Then,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal state.Finally,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.
文摘采用径向基函数神经网络(Radical Basis Function Neutral Networks,简称RBF神经网络)来模拟大跨度结构的非高斯风压场.根据某大跨度结构的形式特点,将结构风场看成是屋面位置和时间的函数,将风压场分解为一系列径向基函数.再利用单调非线性无记忆转换映射和RBF中获得的风场函数定义向量过程,从而将非高斯场的模拟转换为互相关高斯过程的模拟.将RBF神经网络应用于一大跨度屋盖的非高斯场模拟,得到结构上非高斯风压场的分布.结果对比表明,RBF神经网络模拟非高斯风压场具有较高的准确性.该方法可直接利用RBF神经网络的输出结果,避免推导高斯过程和非高斯过程的关系式,因此具有较高的效率.RBF神经网络模拟非高斯风压场在准确性和效率上均具有显著优势.
基金National Natural Science Foundation of China under Grant Nos.51278160,51478155,51378147
文摘Stochastic simulation is an important means of acquiring fluctuating wind pressures for wind induced response analyses in structural engineering. The wind pressure acting on a large-span space structure can be characterized as a stationary non-Gaussian field. This paper reviews several simulation algorithms related to the Spectral Representation Method （SRM） and the Static Transformation Method （STM）. Polynomial and Exponential transformation functions （PSTM and ESTM） are discussed. Deficiencies in current algorithms, with respect to accuracy, stability and efficiency, are analyzed, and the algorithms are improved for better practical application. In order to verify the improved algorithm, wind pressure fields on a large-span roof are simulated and compared with wind tunnel data. The simulation results fit well with the wind tunnel data, and the algorithm accuracy, stability and efficiency are shown to be better than those of current algorithms.
基金the financial support of the Major Program of the National Natural Science Foundation of China(No.51490675)the National Science Fund for Distinguished Young Scholars(No.51625902)+1 种基金the Taishan Scholars Program of Shandong Provincethe Fundamental Research Funds for the Central Universities(No.841713035)
文摘Both wave-frequency(WF) and low-frequency(LF) components of mooring tension are in principle non-Gaussian due to nonlinearities in the dynamic system.This paper conducts a comprehensive investigation of applicable probability density functions(PDFs) of mooring tension amplitudes used to assess mooring-line fatigue damage via the spectral method.Short-term statistical characteristics of mooring-line tension responses are firstly investigated,in which the discrepancy arising from Gaussian approximation is revealed by comparing kurtosis and skewness coefficients.Several distribution functions based on present analytical spectral methods are selected to express the statistical distribution of the mooring-line tension amplitudes.Results indicate that the Gamma-type distribution and a linear combination of Dirlik and Tovo-Benasciutti formulas are suitable for separate WF and LF mooring tension components.A novel parametric method based on nonlinear transformations and stochastic optimization is then proposed to increase the effectiveness of mooring-line fatigue assessment due to non-Gaussian bimodal tension responses.Using time domain simulation as a benchmark,its accuracy is further validated using a numerical case study of a moored semi-submersible platform.
基金Y.L.was supported by the NSF China under Grant Nos.11875201 and 11935010P.K.G.was supported by SERB Start-up Research Grant(Young Scientist)No.YSS/2014/000853the UGC-BSR Start-Up Grant No.F.30-92/2015.
文摘Brownian particles suspended in disordered crowded environments often exhibit non-Gaussian normal diffusion(NGND),whereby their displacements grow with mean square proportional to the observation time and non-Gaussian statistics.Their distributions appear to decay almost exponentially according to“universal”laws largely insensitive to the observation time.This effect is generically attributed to slow environmental fluctuations,which perturb the local configuration of the suspension medium.To investigate the microscopic mechanisms responsible for the NGND phenomenon,we study Brownian diffusion in low dimensional systems,like the free diffusion of ellipsoidal and active particles,the diffusion of colloidal particles in fluctuating corrugated channels and Brownian motion in arrays of planar convective rolls.NGND appears to be a transient effect related to the time modulation of the instantaneous particle’s diffusivity,which can occur even under equilibrium conditions.Consequently,we propose to generalize the definition of NGND to include transient displacement distributions which vary continuously with the observation time.To this purpose,we provide a heuristic one-parameter function,which fits all time-dependent transient displacement distributions corresponding to the same diffusion constant.Moreover,we reveal the existence of low dimensional systems where the NGND distributions are not leptokurtic(fat exponential tails),as often reported in the literature,but platykurtic(thin sub-Gaussian tails),i.e.,with negative excess kurtosis.The actual nature of the NGND transients is related to the specific microscopic dynamics of the diffusing particle.
