Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-part...Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-particle interaction in space plasmas. The signals considered here are medium scale electron density irregularities and ELF/ULF electrostatic turbulence. Nonlinearities are mainly observed in the ELF range. They are independently pointed out in time series associated with fluctuations in electronic density and in time series associated with the measurement of one electric field component. Peaks in cross-bicorrelation function and in mutual information clearly show that, in well delimited frequency bands, the wave-particle interactions are nonlinear above a certain level of fluctuations. The way the energy is transferred within the frequencies of density fluctuations is indicated by a bi-spectra analysis.展开更多
Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP i...Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.展开更多
This paper presents a method to design a cost-optimal nonredundant sensor network to observe all variables in a general nonlinear process. A mixed integer linear programming model was used to minimize the cost with d...This paper presents a method to design a cost-optimal nonredundant sensor network to observe all variables in a general nonlinear process. A mixed integer linear programming model was used to minimize the cost with data classification to check the observability of all unmeasured variables. This work is a starting point for designing sensor networks for general nonlinear processes based on various criteria, such as reliability and accuracy.展开更多
In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new...In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.展开更多
Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degrad...Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.展开更多
The decadal variability of the North Atlantic thermohaline circulation(THC) is investigated within a three-dimensional ocean circulation model using the conditional nonlinear optimal perturbation method. The results s...The decadal variability of the North Atlantic thermohaline circulation(THC) is investigated within a three-dimensional ocean circulation model using the conditional nonlinear optimal perturbation method. The results show that the optimal initial perturbations of temperature and salinity exciting the strongest decadal THC variations have similar structures: the perturbations are mainly in the northwestern basin at a depth ranging from 1500 to 3000 m. These temperature and salinity perturbations act as the optimal precursors for future modifications of the THC, highlighting the importance of observations in the northwestern basin to monitor the variations of temperature and salinity at depth. The decadal THC variation in the nonlinear model initialized by the optimal salinity perturbations is much stronger than that caused by the optimal temperature perturbations, indicating that salinity variations might play a relatively important role in exciting the decadal THC variability. Moreover, the decadal THC variations in the tangent linear and nonlinear models show remarkably different characteristics, suggesting the importance of nonlinear processes in the decadal variability of the THC.展开更多
To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and t...To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization(EM) algorithm. Next, with known thresholds,the RUL of each degradation can be predicted by using the first hitting time(FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.展开更多
The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing general...The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.展开更多
With the L-P approximate method(variation of parameter method), a barotropic channel model in β-plane is used to study the effect of nonlinear interaction between two waves with different scales on the formation of b...With the L-P approximate method(variation of parameter method), a barotropic channel model in β-plane is used to study the effect of nonlinear interaction between two waves with different scales on the formation of blocking. The approximate analytical solution, which can describe the process of the blocking formation, maintenance and breakdown, has been obtained by using the method of aproximate expansion. The importance of nonlinear interaction between two waves with different scales is stressed in the solution. The result suggests that the nonlinear interaction is the main dynamic process of the blocking formation. Some required conditions of blocking formation are also discussed.展开更多
This article establishes a universal robust limit theorem under a sublinear expectation framework.Under moment and consistency conditions,we show that,forα∈(1,2),the i.i.d.sequence{(1/√∑_(i=1)^(n)X_(i),1/n∑_(i=1)...This article establishes a universal robust limit theorem under a sublinear expectation framework.Under moment and consistency conditions,we show that,forα∈(1,2),the i.i.d.sequence{(1/√∑_(i=1)^(n)X_(i),1/n∑_(i=1)^(n)X_(i)Y_(i),1/α√n∑_(i=1)^(n)X_(i))}_(n=1)^(∞)converges in distribution to L_(1),where L_(t=(ε_(t),η_(t),ζ_(t))),t∈[0,1],is a multidimensional nonlinear Lévy process with an uncertainty■set as a set of Lévy triplets.This nonlinear Lévy process is characterized by a fully nonlinear and possibly degenerate partial integro-differential equation(PIDE){δ_(t)u(t,x,y,z)-sup_(F_(μ),q,Q)∈■{∫_(R^(d)δλu(t,x,y,z)(dλ)with.To construct the limit process,we develop a novel weak convergence approach based on the notions of tightness and weak compactness on a sublinear expectation space.We further prove a new type of Lévy-Khintchine representation formula to characterize.As a byproduct,we also provide a probabilistic approach to prove the existence of the above fully nonlinear degenerate PIDE.展开更多
An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partia...An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.展开更多
The nonlinear branching process with immigration is constructed as the pathwise unique solution of a stochastic integral equation driven by Poisson random measures. Some criteria for the regularity, recurrence, ergodi...The nonlinear branching process with immigration is constructed as the pathwise unique solution of a stochastic integral equation driven by Poisson random measures. Some criteria for the regularity, recurrence, ergodicity and strong ergodicity of the process are then established.展开更多
In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener mode...In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.展开更多
Urban and regional air pollutions are characterized by high concentrations of secondary pollutants such as photo-oxidants （mainly ozone） and fine particulate matter, which are formed through chemical reactions of th...Urban and regional air pollutions are characterized by high concentrations of secondary pollutants such as photo-oxidants （mainly ozone） and fine particulate matter, which are formed through chemical reactions of the primary pollutants emitted from various sources. The accumulation of these pollutants under stagnant meteorological conditions results in the formation of gray haze, reducing visibility and causing major impacts on human health and climate. In an air pollution complex, the co- existence of high concentrations of primary and secondary gaseous and particulate pollutants provides a large amount of reac- tants for heterogeneous reactions on the surface of fine particles; these reactions change the oxidizing capacity of the atmos- phere, as well as chemical compositions along with the physicochemical and optical properties of particulate matter, thereby accelerating formation of the air pollution complex and gray haze. Using in situ technologies, such as diffuse reflectance infra- red Fourier-transform spectroscopy and single-particle Raman spectroscopy, we systematically investigated the reaction kinet- ics and mechanisms of gaseous pollutants （i.e., NO2, SO2, 03, and formaldehyde） on the surfaces of the major components of atmospheric particles such as CaCO3, kaolinite, montmorillonite, NaC1, sea salt, A1203, and Tit2. We found that the main re- action products were sulfate, nitrate, or formate, which can change the hygroscopicity and light extinction parameters of those particles significantly. By analyzing the reaction kinetics of these heterogeneous reactions, we identified synergetic mechanisms of the three ternary reaction systems, ,i.e., NOE-particles-H2O, SO2-particles-O3, and organics/SO2-particles-UV illumination. These synergetic mechanisms can provide experimental and theoretical bases for understanding the feedback mechanisms and nonlinear processes in the formation of an air pollution complex and gray haze.展开更多
A modified Strong Tracking Filter (STF) is used to develop a new approach to sensor fault tolerant control. Generic Model Control (GMC) is used to control the nonlinear process while the process runs normally becaus...A modified Strong Tracking Filter (STF) is used to develop a new approach to sensor fault tolerant control. Generic Model Control (GMC) is used to control the nonlinear process while the process runs normally because of its robust control performance. If a fault occurs in the sensor, a sensor bias vector is then introduced to the output equation of the process model. The sensor bias vector is estimated on line during every control period using the STF. The estimated sensor bias vector is used to develop a fault detection mechanism to supervise the sensors. When a sensor fault occurs, the conventional GMC is switched to a fault tolerant control scheme, which is, in essence, a state estimation and output prediction based GMC. The laboratory experimental results on a three tank system demonstrate the effectiveness of the proposed Sensor Fault Tolerant Generic Model Control (SFTGMC) approach.展开更多
Infrared imaging is a crucial technique in a multitude of applications,including night vision,autonomous vehicle navigation,optical tomography,and food quality control.Conventional infrared imaging technologies,howeve...Infrared imaging is a crucial technique in a multitude of applications,including night vision,autonomous vehicle navigation,optical tomography,and food quality control.Conventional infrared imaging technologies,however,require the use of materials such as narrow bandgap semiconductors,which are sensitive to thermal noise and often require cryogenic cooling.We demonstrate a compact all-optical alternative to perform infrared imaging in a metasurface composed of GaAs semiconductor nanoantennas,using a nonlinear wave-mixing process.We experimentally show the upconversion of short-wave infrared wavelengths via the coherent parametric process of sum-frequency generation.In this process,an infrared image of a target is mixed inside the metasurface with a strong pump beam,translating the image from the infrared to the visible in a nanoscale ultrathin imaging device.Our results open up new opportunities for the development of compact infrared imaging devices with applications in infrared vision and life sciences.展开更多
Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recur...Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.展开更多
During the low-pressure casting of extra-large size C95800 copper alloy components,traditional linear pressurization technique leads to a rapid surge of liquid metal inlet velocity at the regions where the mold cavity...During the low-pressure casting of extra-large size C95800 copper alloy components,traditional linear pressurization technique leads to a rapid surge of liquid metal inlet velocity at the regions where the mold cavity cross-section enlarges.This rapid increasement of liquid metal inlet velocity causes serious entrapment of gas and oxide films,and results in various casting defects such as the bifilm defects.These defects detrimentally deteriorate mechanical properties of the castings.To address this issue,an innovative nonlinear pressurization strategy timely matching to the casting structure was proposed.The pressurization rate decreases at sections where the cross-section widens,but it gradually increases as the liquid metal level rises.By this way,the inlet velocity remains below a critical threshold to prevent the entrapment of gas and oxide films.Comparative analyses involving numerical simulations and casting verification illustrate that the nonlinear pressurization technique,compared to the linear pressurization,effectively diminishes both the size and number of bifilm defects.Furthermore,the nonlinear pressurization method enhances the casting yield strength by 10%,tensile strength by 14%,and elongation by 10%.Examination through scanning electron microscopy highlights that the bifilm defects arising from the linear pressurization process result in the reduction of the castings’mechanical properties.These observations underscore the efficacy of nonlinear pressurization in enhancing the quality and reliability of gigantic castings,as exemplified by a 5.4-ton extra-large sized C95800 copper alloy propeller hub with complex structures in the current study.展开更多
In this paper, operator-based nonlinear water temperature control for a group of three connected microreactors actuated by Peltier devices is proposed. To control the water temperature of tube in the microreactor, the...In this paper, operator-based nonlinear water temperature control for a group of three connected microreactors actuated by Peltier devices is proposed. To control the water temperature of tube in the microreactor, the temperature change of aluminum effects is considered. Therefore, the temperature change of aluminum becomes the part of an input of the tube. First, nonlinear thermal models of aluminum plates and tubes that structure the microreactor are obtained. Then, an operator based nonlinear water temperature control system for the microreactor is designed. Finally, the effectiveness of the proposed models and methods is confirmed by simulation and experimental results.展开更多
In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot d...In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.展开更多
文摘Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-particle interaction in space plasmas. The signals considered here are medium scale electron density irregularities and ELF/ULF electrostatic turbulence. Nonlinearities are mainly observed in the ELF range. They are independently pointed out in time series associated with fluctuations in electronic density and in time series associated with the measurement of one electric field component. Peaks in cross-bicorrelation function and in mutual information clearly show that, in well delimited frequency bands, the wave-particle interactions are nonlinear above a certain level of fluctuations. The way the energy is transferred within the frequencies of density fluctuations is indicated by a bi-spectra analysis.
基金Supported by the National Natural Science Foundation of China (No. 60025307, No. 60234010) the National 863 Project(No. 2001AA413130,2002AA412420)+1 种基金 Research Fund for the Doctoral Program of Higher Education (No. 20020003063) the National 973 Program
文摘Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.
基金the Major Research Project of the Ninth-Five Plan of China (No.96 - 5 4 3- 0 3- 0 6 ) and theNational High- Tech Research and Developm entProgram (No.86 3- 5 11- 94 5 )
文摘This paper presents a method to design a cost-optimal nonredundant sensor network to observe all variables in a general nonlinear process. A mixed integer linear programming model was used to minimize the cost with data classification to check the observability of all unmeasured variables. This work is a starting point for designing sensor networks for general nonlinear processes based on various criteria, such as reliability and accuracy.
基金Supported by the Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities(YYY11076)
文摘In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.
基金Projects(51475462,61374138,61370031)supported by the National Natural Science Foundation of China
文摘Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
基金supported by the National Basic Research Program of China(973 Program,Grant No.2012CB417404)
文摘The decadal variability of the North Atlantic thermohaline circulation(THC) is investigated within a three-dimensional ocean circulation model using the conditional nonlinear optimal perturbation method. The results show that the optimal initial perturbations of temperature and salinity exciting the strongest decadal THC variations have similar structures: the perturbations are mainly in the northwestern basin at a depth ranging from 1500 to 3000 m. These temperature and salinity perturbations act as the optimal precursors for future modifications of the THC, highlighting the importance of observations in the northwestern basin to monitor the variations of temperature and salinity at depth. The decadal THC variation in the nonlinear model initialized by the optimal salinity perturbations is much stronger than that caused by the optimal temperature perturbations, indicating that salinity variations might play a relatively important role in exciting the decadal THC variability. Moreover, the decadal THC variations in the tangent linear and nonlinear models show remarkably different characteristics, suggesting the importance of nonlinear processes in the decadal variability of the THC.
基金supported by the National Natural Science Foundation of China(6129032461473164+1 种基金61490701)the Research Fund for the Taishan Scholar Project of Shandong Province of China(LZB2015-162)
文摘To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization(EM) algorithm. Next, with known thresholds,the RUL of each degradation can be predicted by using the first hitting time(FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.
文摘The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.
文摘With the L-P approximate method(variation of parameter method), a barotropic channel model in β-plane is used to study the effect of nonlinear interaction between two waves with different scales on the formation of blocking. The approximate analytical solution, which can describe the process of the blocking formation, maintenance and breakdown, has been obtained by using the method of aproximate expansion. The importance of nonlinear interaction between two waves with different scales is stressed in the solution. The result suggests that the nonlinear interaction is the main dynamic process of the blocking formation. Some required conditions of blocking formation are also discussed.
基金supported by the National Key R&D Program of China(Grant No.2018YFA0703900)the National Natural Science Foundation of China(Grant No.11671231)+2 种基金the Qilu Young Scholars Program of Shandong Universitysupported by the Tian Yuan Projection of the National Natural Science Foundation of China(Grant Nos.11526205,11626247)the National Basic Research Program of China(973 Program)(Grant No.2007CB814900(Financial Risk)).
文摘This article establishes a universal robust limit theorem under a sublinear expectation framework.Under moment and consistency conditions,we show that,forα∈(1,2),the i.i.d.sequence{(1/√∑_(i=1)^(n)X_(i),1/n∑_(i=1)^(n)X_(i)Y_(i),1/α√n∑_(i=1)^(n)X_(i))}_(n=1)^(∞)converges in distribution to L_(1),where L_(t=(ε_(t),η_(t),ζ_(t))),t∈[0,1],is a multidimensional nonlinear Lévy process with an uncertainty■set as a set of Lévy triplets.This nonlinear Lévy process is characterized by a fully nonlinear and possibly degenerate partial integro-differential equation(PIDE){δ_(t)u(t,x,y,z)-sup_(F_(μ),q,Q)∈■{∫_(R^(d)δλu(t,x,y,z)(dλ)with.To construct the limit process,we develop a novel weak convergence approach based on the notions of tightness and weak compactness on a sublinear expectation space.We further prove a new type of Lévy-Khintchine representation formula to characterize.As a byproduct,we also provide a probabilistic approach to prove the existence of the above fully nonlinear degenerate PIDE.
基金National Natural Science Foundation of China (No. 61074079)Shanghai Leading Academic Discipline Project,China (No.B504)
文摘An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.
基金Supported by NSFC(Grant Nos.1131003,11626245 and 11571043)
文摘The nonlinear branching process with immigration is constructed as the pathwise unique solution of a stochastic integral equation driven by Poisson random measures. Some criteria for the regularity, recurrence, ergodicity and strong ergodicity of the process are then established.
基金supported by the National Natural Science Foundation of China(Grant No.62003151)the Natural Science Foundation of Jiangsu Province(Grant No.BK20191035)+1 种基金the Changzhou Sci&Tech Program(Grant No.CJ20220065)the“Blue Project”of Universities in Jiangsu Province,and Zhongwu Youth Innovative Talents Support Program in Jiangsu Institute of Technology.
文摘In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.
基金financially supported by the National Natural Science Foundation of China (20637020, 40490265 & 20077001)National Basic Research Program of China (2002CB410802)special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control
文摘Urban and regional air pollutions are characterized by high concentrations of secondary pollutants such as photo-oxidants （mainly ozone） and fine particulate matter, which are formed through chemical reactions of the primary pollutants emitted from various sources. The accumulation of these pollutants under stagnant meteorological conditions results in the formation of gray haze, reducing visibility and causing major impacts on human health and climate. In an air pollution complex, the co- existence of high concentrations of primary and secondary gaseous and particulate pollutants provides a large amount of reac- tants for heterogeneous reactions on the surface of fine particles; these reactions change the oxidizing capacity of the atmos- phere, as well as chemical compositions along with the physicochemical and optical properties of particulate matter, thereby accelerating formation of the air pollution complex and gray haze. Using in situ technologies, such as diffuse reflectance infra- red Fourier-transform spectroscopy and single-particle Raman spectroscopy, we systematically investigated the reaction kinet- ics and mechanisms of gaseous pollutants （i.e., NO2, SO2, 03, and formaldehyde） on the surfaces of the major components of atmospheric particles such as CaCO3, kaolinite, montmorillonite, NaC1, sea salt, A1203, and Tit2. We found that the main re- action products were sulfate, nitrate, or formate, which can change the hygroscopicity and light extinction parameters of those particles significantly. By analyzing the reaction kinetics of these heterogeneous reactions, we identified synergetic mechanisms of the three ternary reaction systems, ,i.e., NOE-particles-H2O, SO2-particles-O3, and organics/SO2-particles-UV illumination. These synergetic mechanisms can provide experimental and theoretical bases for understanding the feedback mechanisms and nonlinear processes in the formation of an air pollution complex and gray haze.
基金the National Natural Science Foundationof China!( No. 697740 2 2 ) the State High-TechDevelopments Plan! ( 863 -5 11-84
文摘A modified Strong Tracking Filter (STF) is used to develop a new approach to sensor fault tolerant control. Generic Model Control (GMC) is used to control the nonlinear process while the process runs normally because of its robust control performance. If a fault occurs in the sensor, a sensor bias vector is then introduced to the output equation of the process model. The sensor bias vector is estimated on line during every control period using the STF. The estimated sensor bias vector is used to develop a fault detection mechanism to supervise the sensors. When a sensor fault occurs, the conventional GMC is switched to a fault tolerant control scheme, which is, in essence, a state estimation and output prediction based GMC. The laboratory experimental results on a three tank system demonstrate the effectiveness of the proposed Sensor Fault Tolerant Generic Model Control (SFTGMC) approach.
基金The authors acknowledge the use of the Australian National Fabrication Facility(ANFF),ACT Node.Rocio CamachoMorales acknowledges a grant from the Consejo Nacional de Ciencia y Tecnología(CONACYT),MexicoNikolay Dimitrov and Lyubomir Stoyanov acknowledge a grant from the EU Marie-Curie RISE program NOCTURNO+1 种基金Mohsen Rahmani acknowledges support from the UK Research and Innovation Future Leaders Fellowship(MR/T040513/1)Dragomir N.Neshev acknowledges a grant from the Australian Research Council(CE20010001,DP190101559).
文摘Infrared imaging is a crucial technique in a multitude of applications,including night vision,autonomous vehicle navigation,optical tomography,and food quality control.Conventional infrared imaging technologies,however,require the use of materials such as narrow bandgap semiconductors,which are sensitive to thermal noise and often require cryogenic cooling.We demonstrate a compact all-optical alternative to perform infrared imaging in a metasurface composed of GaAs semiconductor nanoantennas,using a nonlinear wave-mixing process.We experimentally show the upconversion of short-wave infrared wavelengths via the coherent parametric process of sum-frequency generation.In this process,an infrared image of a target is mixed inside the metasurface with a strong pump beam,translating the image from the infrared to the visible in a nanoscale ultrathin imaging device.Our results open up new opportunities for the development of compact infrared imaging devices with applications in infrared vision and life sciences.
基金Project supported by the National Basic Research Program (973) of China (No. 2009CB320600) the National Natural Science Foun-dation of China (No. 60974057)
文摘Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.
基金supported by the National Natural Science Foundation of China(Granted Nos.51827801,52371152)the Foundation of National Key Laboratory of Precision Hot Processing of Metals(Granted No.DCQQ2790100724).
文摘During the low-pressure casting of extra-large size C95800 copper alloy components,traditional linear pressurization technique leads to a rapid surge of liquid metal inlet velocity at the regions where the mold cavity cross-section enlarges.This rapid increasement of liquid metal inlet velocity causes serious entrapment of gas and oxide films,and results in various casting defects such as the bifilm defects.These defects detrimentally deteriorate mechanical properties of the castings.To address this issue,an innovative nonlinear pressurization strategy timely matching to the casting structure was proposed.The pressurization rate decreases at sections where the cross-section widens,but it gradually increases as the liquid metal level rises.By this way,the inlet velocity remains below a critical threshold to prevent the entrapment of gas and oxide films.Comparative analyses involving numerical simulations and casting verification illustrate that the nonlinear pressurization technique,compared to the linear pressurization,effectively diminishes both the size and number of bifilm defects.Furthermore,the nonlinear pressurization method enhances the casting yield strength by 10%,tensile strength by 14%,and elongation by 10%.Examination through scanning electron microscopy highlights that the bifilm defects arising from the linear pressurization process result in the reduction of the castings’mechanical properties.These observations underscore the efficacy of nonlinear pressurization in enhancing the quality and reliability of gigantic castings,as exemplified by a 5.4-ton extra-large sized C95800 copper alloy propeller hub with complex structures in the current study.
文摘In this paper, operator-based nonlinear water temperature control for a group of three connected microreactors actuated by Peltier devices is proposed. To control the water temperature of tube in the microreactor, the temperature change of aluminum effects is considered. Therefore, the temperature change of aluminum becomes the part of an input of the tube. First, nonlinear thermal models of aluminum plates and tubes that structure the microreactor are obtained. Then, an operator based nonlinear water temperature control system for the microreactor is designed. Finally, the effectiveness of the proposed models and methods is confirmed by simulation and experimental results.
文摘In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.
