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
This study involved a comprehensive investigation aimed at achieving efficient multi-millijoule THz wave generation by exploiting the unique properties of cylindrical GaAs waveguides as effective mediators of the conv...This study involved a comprehensive investigation aimed at achieving efficient multi-millijoule THz wave generation by exploiting the unique properties of cylindrical GaAs waveguides as effective mediators of the conversion of laser energy into THz waves.Through meticulous investigation,valuable insights into optimizing THz generation processes for practical applications were unearthed.By investigating Hertz potentials,an eigen-value equation for the solutions of the guided modes(i.e.,eigenvalues)was found.The effects of various param-eters,including the effective mode index and the laser pulse power,on the electric field components of THz radia-tion,including the fundamental TE(transverse electric)and TM(transverse magnetic)modes,were evaluated.By analyzing these factors,this research elucidated the nuanced mechanisms governing THz wave generation within cylindrical GaAs waveguides,paving the way for refined methodologies and enhanced efficiency.The sig-nificance of cylindrical GaAs waveguides extends beyond their roles as mere facilitators of THz generation;their design and fabrication hold the key to unlocking the potential for compact and portable THz systems.This trans-formative capability not only amplifies the efficiency of THz generation but also broadens the horizons of practical applications.展开更多
Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applic...Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.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
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
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
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 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
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
The study presented in this paper is in continuation with the paper published by the authors on parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP ＋ FI ＋ FD） controller. It addresses the sta...The study presented in this paper is in continuation with the paper published by the authors on parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP ＋ FI ＋ FD） controller. It addresses the stability analysis of parallel FP ＋ FI ＋ FD controller. The famous＂small gain theorem＂ is used to study the bounded-input and bounded-output （BIBO） stability of the fuzzy controller. Sufficient BIBO-stability conditions are developed for parallel FP ＋ FI ＋ FD controller. FP ＋ FI ＋ FD controller is derived from the conventional parallel proportional plus integral plus derivative （PID） controller. The parallel FP ＋ FI ＋ FD controller is actually a nonlinear controller with variable gains. It shows much better set-point tracking, disturbance rejection and noise suppression for nonlinear processes as compared to conventional PID controller.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
The adiabatic shear instability of ductile materials has attracted more and more attentions of researchers and groups,who have been sparing no effort in further understanding of the underlying mechanism since the firs...The adiabatic shear instability of ductile materials has attracted more and more attentions of researchers and groups,who have been sparing no effort in further understanding of the underlying mechanism since the first experimental depiction of adiabatic shear instability by Zener and Hollomon.As for the adiabatic shear instability,many factors account for its occurrence,including heat conduction,inertia effect,microstructure effect and so on.However,lots of experimental evidence has shown that metal materials display a strong size effect when the characteristic length scale is in the order of microns.The size effect has also been observed in the analysis of shear band in the ductile materials because the order of the bandwidth stays within the microscale range.However,a comprehensive understanding of the whole process of adiabatic shear banding(ASB),including the early onset and the subsequent evolution,is still lacking.In this work,a gradient plasticity model based on the Taylor-based nonlocal theory feasible for the linear perturbation analysis and convenient for numerical calculation is proposed to investigate the strain gradient on the onset of ASB and the coupling effect of heat conduction,inertia effect and strain gradient at the early stage,as well as on the subsequent evolution process at later stages.As for the onset of ASB,the linear perturbation method is used to consider the effect on the initial formation of ASB.After the investigation of the onset of ASB.the characteristic line method is applied to describe the subsequent nonlinear evolution process of ASB.Three stages of ASB evolution are clearly depicted during the evolution process,and the significance of size effect on the ASB nonlinear evolution process of ASB at different stages is analyzed.With the help of linear perturbation analysis and characteristic line method,a comprehensive description of the role of strain gradient in the ASB from the early onset to the end of the evolution is provided.展开更多
Submicron-thick thin-film lithium niobate(TFLN)has emerged as a promising platform for nonlinear integrated photonics.In this work,we demonstrate the efficient simultaneous generation of broadband 2nd–8th harmonics i...Submicron-thick thin-film lithium niobate(TFLN)has emerged as a promising platform for nonlinear integrated photonics.In this work,we demonstrate the efficient simultaneous generation of broadband 2nd–8th harmonics in chirped periodically poled(CPP)TFLN.This is achieved through the synergistic effects of cascadedχ^((2))nonlinear up-conversion andχ^((3))self-phase modulation,driven by near-infrared femtosecond pulses with a central wavelength of 2100 nm and a pulse energy of 1.2μJ.Remarkably,the 7th and 8th harmonics extend into the deep ultraviolet(DUV)region,reaching wavelengths as short as 250 nm.The 3rd–8th harmonic spectra seamlessly connect,forming a broadband supercontinuum spanning from the DUV to the visible range(250–800 nm,-25 d B),with an on-chip conversion efficiency of 19%(0.23μJ).This achievement is attributed to the CPP-TFLN providing multiple broadband reciprocal lattice vector bands,enabling quasi-phase matching for a series ofχ^((2))nonlinear processes,including second harmonic generation(SHG),cascaded SHG,and third harmonic generation.Furthermore,we demonstrated the significant role of cascadedχ^((2))phase-mismatched nonlinear processes in high-harmonic generation(HHG).Our work unveils the intricate and diverse nonlinear optical interactions in TFLN,offering a clear path toward efficient on-chip HHG and compact coherent white-light sources extending into the DUV.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.
文摘This study involved a comprehensive investigation aimed at achieving efficient multi-millijoule THz wave generation by exploiting the unique properties of cylindrical GaAs waveguides as effective mediators of the conversion of laser energy into THz waves.Through meticulous investigation,valuable insights into optimizing THz generation processes for practical applications were unearthed.By investigating Hertz potentials,an eigen-value equation for the solutions of the guided modes(i.e.,eigenvalues)was found.The effects of various param-eters,including the effective mode index and the laser pulse power,on the electric field components of THz radia-tion,including the fundamental TE(transverse electric)and TM(transverse magnetic)modes,were evaluated.By analyzing these factors,this research elucidated the nuanced mechanisms governing THz wave generation within cylindrical GaAs waveguides,paving the way for refined methodologies and enhanced efficiency.The sig-nificance of cylindrical GaAs waveguides extends beyond their roles as mere facilitators of THz generation;their design and fabrication hold the key to unlocking the potential for compact and portable THz systems.This trans-formative capability not only amplifies the efficiency of THz generation but also broadens the horizons of practical applications.
基金supported by the National Key Research and Development Program of China(No.2023YFF0715103)-financial supportNational Natural Science Foundation of China(Grant Nos.62306237 and 62006191)-financial support+1 种基金Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-149 and 2021ZDLGY15-04)-financial support,NorthwestUniversity Graduate Innovation Project(No.CX2023194)-financial supportNatural Science Foundation of Shaanxi(No.2023-JC-QN-0750)-financial support.
文摘Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
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 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 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.
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.
文摘The study presented in this paper is in continuation with the paper published by the authors on parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP ＋ FI ＋ FD） controller. It addresses the stability analysis of parallel FP ＋ FI ＋ FD controller. The famous＂small gain theorem＂ is used to study the bounded-input and bounded-output （BIBO） stability of the fuzzy controller. Sufficient BIBO-stability conditions are developed for parallel FP ＋ FI ＋ FD controller. FP ＋ FI ＋ FD controller is derived from the conventional parallel proportional plus integral plus derivative （PID） controller. The parallel FP ＋ FI ＋ FD controller is actually a nonlinear controller with variable gains. It shows much better set-point tracking, disturbance rejection and noise suppression for nonlinear processes as compared to conventional PID controller.
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the financial support from the National Natural Science Foundation of China(Grant Nos.11522220,11772268,11527803,11390361).
文摘The adiabatic shear instability of ductile materials has attracted more and more attentions of researchers and groups,who have been sparing no effort in further understanding of the underlying mechanism since the first experimental depiction of adiabatic shear instability by Zener and Hollomon.As for the adiabatic shear instability,many factors account for its occurrence,including heat conduction,inertia effect,microstructure effect and so on.However,lots of experimental evidence has shown that metal materials display a strong size effect when the characteristic length scale is in the order of microns.The size effect has also been observed in the analysis of shear band in the ductile materials because the order of the bandwidth stays within the microscale range.However,a comprehensive understanding of the whole process of adiabatic shear banding(ASB),including the early onset and the subsequent evolution,is still lacking.In this work,a gradient plasticity model based on the Taylor-based nonlocal theory feasible for the linear perturbation analysis and convenient for numerical calculation is proposed to investigate the strain gradient on the onset of ASB and the coupling effect of heat conduction,inertia effect and strain gradient at the early stage,as well as on the subsequent evolution process at later stages.As for the onset of ASB,the linear perturbation method is used to consider the effect on the initial formation of ASB.After the investigation of the onset of ASB.the characteristic line method is applied to describe the subsequent nonlinear evolution process of ASB.Three stages of ASB evolution are clearly depicted during the evolution process,and the significance of size effect on the ASB nonlinear evolution process of ASB at different stages is analyzed.With the help of linear perturbation analysis and characteristic line method,a comprehensive description of the role of strain gradient in the ASB from the early onset to the end of the evolution is provided.
基金National Natural Science Foundation of China(12434016,11974119)Science and Technology Project of Guangdong(2020B010190001)National Funded Postdoctoral Researcher Program(GZB20240785)。
文摘Submicron-thick thin-film lithium niobate(TFLN)has emerged as a promising platform for nonlinear integrated photonics.In this work,we demonstrate the efficient simultaneous generation of broadband 2nd–8th harmonics in chirped periodically poled(CPP)TFLN.This is achieved through the synergistic effects of cascadedχ^((2))nonlinear up-conversion andχ^((3))self-phase modulation,driven by near-infrared femtosecond pulses with a central wavelength of 2100 nm and a pulse energy of 1.2μJ.Remarkably,the 7th and 8th harmonics extend into the deep ultraviolet(DUV)region,reaching wavelengths as short as 250 nm.The 3rd–8th harmonic spectra seamlessly connect,forming a broadband supercontinuum spanning from the DUV to the visible range(250–800 nm,-25 d B),with an on-chip conversion efficiency of 19%(0.23μJ).This achievement is attributed to the CPP-TFLN providing multiple broadband reciprocal lattice vector bands,enabling quasi-phase matching for a series ofχ^((2))nonlinear processes,including second harmonic generation(SHG),cascaded SHG,and third harmonic generation.Furthermore,we demonstrated the significant role of cascadedχ^((2))phase-mismatched nonlinear processes in high-harmonic generation(HHG).Our work unveils the intricate and diverse nonlinear optical interactions in TFLN,offering a clear path toward efficient on-chip HHG and compact coherent white-light sources extending into the DUV.
