Higher-Order Statistics and Nonlinear Processes in Space Plasmas

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.

An Agent Interaction Based Method for Nonlinear Process Plan Scheduling

This article puts forward a scheduling method for nonlinear process plan shop floor.Task allocation and load bal- ance are realized by bidding mechanism.Though the agent interaction process,the execution of tasks is determined and the coherence of manufacturing decision is verified.The employment of heuristic index can help to optimize the system performance.

Experimental observations of the characteristics of hot electron and nonlinear processesproduced in special material

Making use of disk targets composed of several peculiar materials (foamAn, foam C8H8) and hohlraum with a special structure, experiments have been doneat "Xing Guang - II" laser facility, which study the characteristics of hot electronsand the related nonlinear processes such as Stimulated airman Scattering (SRS), TwoPlasma Decay (TPD), Stimulated Brillouin Scattering (SBS), etc.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

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.

Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

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.

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

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.

Sensor Network Design for Nonlinear Processes

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.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

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.

High-Order Volterra Model Predictive Control and Its Application to a Nonlinear Polymerisation Process

Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order. Keywords Model predictive control - Volterra series - process control - nonlinear control Yun Li is a senior lecturer at University of Glasgow, UK, where has taught and researched in evolutionary computation and control engineering since 1991. He worked in the UK National Engineering Laboratory and Industrial Systems and Control Ltd, Glasgow in 1989 and 1990. In 1998, he established the IEEE CACSD Evolutionary Computation Working Group and the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on Systems, Control, and Drives. In summer 2002, he served as a visiting professor to Kumamoto University, Japan. He is also a visiting professor at University of Electronic Science and Technology of China. His research interests are in parallel processing, design automation and discovery of engineering systems using evolutionary learning and intelligent search techniques. Applications include control, system modelling and prediction, circuit design, microwave engineering, and operations management. He has advised 12 Ph.D.s in evolutionary computation and has 140 publications.Hiroshi Kashiwagi received B.E, M.E. and Ph.D. degrees in measurement and control engineering from the University of Tokyo, Japan, in 1962, 1964 and 1967 respectively. In 1967 he became an Associate Professor and in 1976 a Professor at Kumamoto University. From 1973 to 1974, he served as a visiting Associate Professor at Purdue University, Indiana, USA. From 1990 to 1994, he was the Director at Computer Center of Kumamoto University. He has also served as a member of Board of Trustees of Society of Instrument and Control Engineers (SICE), Japan, Chairman of Kyushu Branch of SICE and General Chair of many international conferences held in Japan, Korea, Chin and India. In 1994, he was awarded SICE Fellow for his contributions to the field of measurement and control engineering through his various academic activities. He also received the Gold Medal Prize at ICAUTO’95 held in India. In 1997, he received the “Best Book Award” from SICE for his new book entitled “M-sequence and its application” written in Japanese and published in 1996 by Shoukoudou Publishing Co. in Japan. In 1999, he received the “Best Paper Award” from SICE for his paper “M-transform and its application to system identification”. His research interests include signal processing and applications, especially pseudorandom sequence and its applications to measurement and control engineering.

SYNTHESIS AND OPTICAL PROPERTIES OF A NOVEL ORGANIC/INORGANIC HYBRID NONLINEAR OPTICAL POLYMER VIA SOL-GEL PROCESS

A new organic/inorganic hybrid nonlinear optical (NLO) material was developed by the sol-gel process of an alkoxysilane dye with tetraethoxysilane. A NLO moiety based on 4-nitro-4 ' -hydroxy azobenzene was covalently bonded to the triethoxysilane derivative, i.e, gamma -isocyanatopropyl triethoxysilane. The preparation process and properties of the sol-gel derived NLO polymer were studied and characterized by SEM, FTIR,H-1-NMR, UV-Vis, DSC and second harmonic generation (SHG) measurement. The results indicated that the chemical bonding of the chromophores to the inorganic SiO2 networks induces low dipole alignment relaxation and preferable orientational stability. The SHG measurements also showed that the bonded polymer film containing 75 wt% of the akoxysilane dye has a high electro-optic coefficient (r(33)) of 7.1 pm/V at 1.1 mum wavelength, and exhibit good SHG stability, the r(33) values can maintain about 92.7% of its initial value at room temperature for 90 days, and can maintain about 59.3% at 100 degreesC for 300 min.

New Algorithm Model for Processing GeneralizedDynamic Nonlinear Data Derived from Deformation Monitoring Network

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.

Effect of Nonlinear Dynamic Process on Formation and Breakdown of Blocking

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.

Nonlinear Modeling for a Two-Stage Degradation System Based on Nonhomogeneous Poisson Process

The degradation process modeling is one of research hotspots of prognostic and health management(PHM),which can be used to estimate system reliability and remaining useful life(RUL).In order to study system degradation process,cumulative damage model is used for degradation modeling.Assuming that damage increment is Gamma distribution,shock counting subjects to a homogeneous Poisson process(HPP)when degradation process is linear,and shock counting is a non-homogeneous Poisson process(NHPP)when degradation process is nonlinear.A two-stage degradation system is considered in this paper,for which the degradation process is linear in the first stage and the degradation process is nonlinear in the second stage.A nonlinear modeling method for considered system is put forward,and reliability model and remaining useful life model are established.A case study is given to validate the veracities of established models.

Nonlinear Model-Based Process Operation under UncertaintyUsing Exact Parametric Programming

In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onprocess synthesis problems. The algorithms are developed for the special case in which the nonlinearitiesarise because of logarithmic terms, with the first one being developed for the deterministic case, and thesecond for the parametric case （p-MINLP）. The key idea is to formulate and solve the square system of thefirst-order Karush-Kuhn-Tucker （KKT） conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution tech-niques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, twoprocess synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicitfunction of the uncertain parameters.

Optimization of pressurization process in low-pressure casting of a 5.4-ton gigantic C95800 copper alloy casting

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.

High-Accuracy Pneumatic Position Control by Applying Nonlinear Control and Arranging Transient Process

By applying a nonlinear control and arranging a transient process, the initiative error of the pneumatic servo positioning system is reduced largely, and a larger gain of the controller is used to improve the responding speed of the system at the same damping ratio. Therefore, a compromise is made among the responding speed, overshoot, robustness, adaptability and stability. In addition, a dynamic output feedback controller, including position velocity and acceleration （PVA） feedback, is designed to improve the performance of the system. And a nonlinear controller is reconstructed based on the linear output feedback controller to decrease noises and disturbances. The dynamic responses of the system are simulated and tested. Results show that the error is kept within 0.02 mm under different mass loads and the positioning transient process is smooth, without overshoot and speedy.

Nonlinear Model of Image Noise: An Application on Computed Tomography including Beam Hardening and Image Processing Algorithms

This paper proposes a more inclusive statistical model for predicting image noise in Computed Tomography (CT), associated with scanning factors, considering the effect of beam hardening and image processing filters. It is based on power functions where the levels of the parameters will determine the rate of noise variation with respect to a given scanning factor. It includes the influence of tube potential, tube current, slice thickness, Field of View (FOV), reconstruction methods and post-processing filters. To validate the model, tomographic measurements were made by using a PMMA phantom that simulates paediatric head and adult abdomen, a PET bottle was used to simulate the head of the new-born. The influence of ROI (Region Of Interest) size over nonlinear model parameters was analysed, and high variations of powers of attenuation and FOV were found depending on ROI size. A nonlinear robust regression method was used. The validation was performed graphically by weighted residual analysis. A nonlinear noise model was obtained with an adjusted coefficient of determination for ROI sizes between 10% and 70% of the phantom diameter or FOV. The model confirms the significance of the tube current, slice thickness and beam hardening effect on image. The process of estimation of the parameters of the model by Nonlinear Robust Regression turned out to be optimal.

PREPARATION AND SECOND-ORDER OPTICAL NONLINEARITY OF NOVEL PHENOXYSILICON NETWORKS BY SOL-GEL PROCESS

Four phenoxysilicon networks for nonlinear optical (NLO) applications were designed and prepared by an extended sol-gel process without additional H2O and catalyst. All poled polymer network films possess high second-order nonlinear optical coefficients (d(33)) Of 10(-7)similar to 10(-8) esu. The investigation of NLO temporal stability at room temperature and elevated temperature (120 degreesC) indicated that these films exhibit high d(33) stability because the orientation of the chromophores are locked in the phenoxysilicon organic/inorganic networks.

On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares

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.

Three-Dimensional Structure of Optimal Nonlinear Excitation for Decadal Variability of the Thermohaline Circulation

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.