HBGC123D: a high-performance computer model of coupled hydrogeological and biogeochemical processes

Groundwater flow and transport models have been used to assist management of subsurface water resources and water quality. The needs of more efficient use of technical and financial resources have recently motivated the development of more effective remediation techniques and complex models of coupled hydrogeological and biogeochemical processes. We present a high-performance computer model of the coupled processes, HBGC123D. The model uses a hybrid Eulerian–Lagrangian finite element method to solve the solute transport equation and a Newton's method to solve the system of nonlinear, mixed kinetics and equilibrium reaction equations. Application of the model to a laboratory soil column with multispecies tracer injection suggests that one may use the model to derive important parameters of subsurface solute fate and transport. These parameters may be used for predictive purpose in similar field problems. To this end, we present a three-dimensional, hypothetical bioremediation simulation on an aquifer contaminated by CoNTA. The simulation suggests that, using oxygen alone to stimulate the biodegradation of the contaminant, one may reduce the waste to 40% in 10 years. Using a refined mesh of this three-dimensional model, we also conduct a performance study of HBGC123D on an array of SGI Origin 2000 distributed shared-memory processors. Both the computational kernels and the entire model show very good performance up to 32 processors. The CPU time is essentially reduced by 20-fold using 64 processors. This result suggests that HBGC123D may be a useful tool in assisting environmental restoration efforts such as waste site characterization and remediation.     

Identification of continuous-time models for nonlinear dynamic systems from discrete data

A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed.     

A procedure of parameter inversion for a nonlinear constitutive model of soils with shield tunneling

The identification of the parameters of a nonlinear constitutive model of soil mass is based on an inverse analysis procedure, which consists of minimizing the objective function representing the difference between the experimental data and the calculated data of the mechanical model. A gradient-based optimization algorithm is developed for estimating model parameters of soils in earth pressure balance (EPB) shield tunneling. The parameter values of the nonlinear constitutive model are searched for by using the Levenberg–Marquardt approximation which can provide fast convergence. The parameter identification results illustrate that the proposed parameter inversion procedure has not only higher computing efficiency but also better identification accuracy. The results from the model are compared with simulated observations. The models are found to have good predictive ability and are expected to be very useful for estimating model parameters for soils in EPB shield tunneling.     

Volcanic source inversion using a genetic algorithm and an elastic-gravitational layered earth model for magmatic intrusions

Here we present an inversion methodology using the combination of a genetic algorithm (GA) inversion program, and an elastic-gravitational earth model to determine the parameters of a volcanic intrusion. Results from the integration of the elastic-gravitational model, a suite of FORTRAN 77 programs developed to compute the displacements due to volcanic loading, with the GA inversion code, written in the C programming language, are presented. These codes allow for the calculation of displacements (horizontal and vertical), tilt, vertical strain and potential and gravity changes on the surface of an elastic-gravitational layered Earth model due to the magmatic intrusion. We detail the appropriate methodology for examining the sensitivity of the model to variation in the constituent parameters using the GA, and present, for the first time, a Monte Carlo technique for evaluating the propagation of error through the GA inversion process. One application example is given at Mayon volcano, Philippines, for the inversion program, the sensitivity analysis, and the error evaluation. The integration of the GA with the complex elastic-gravitational model is a blueprint for an efficient nonlinear inversion methodology and its implementation into an effective tool for the evaluation of parameter sensitivity. Finally, the extension of this inversion algorithm and the error assessment methodology has important implications to the modeling and data assimilation of a number of other nonlinear applications in the field of geosciences.     

Online learning with hidden markov models

We present an online version of the expectation-maximization (EM) algorithm for hidden Markov models (HMMs). The sufficient statistics required for parameters estimation is computed recursively with time, that is, in an online way instead of using the batch forward-backward procedure. This computational scheme is generalized to the case where the model parameters can change with time by introducing a discount factor into the recurrence relations. The resulting algorithm is equivalent to the batch EM algorithm, for appropriate discount factor and scheduling of parameters update. On the other hand, the online algorithm is able to deal with dynamic environments, i.e., when the statistics of the observed data is changing with time. The implications of the online algorithm for probabilistic modeling in neuroscience are briefly discussed.     

Model-based quality monitoring of batch and semi-batch processes

In this paper, a model-based inferential quality monitoring approach for a class of batch systems is investigated. Given the appropriate model form, the batch quality monitoring problem can be reduced to the problem of state estimation for batch and semi-batch processes. Because feed upsets are often a major source of disturbance in this type of system, it is shown that estimating the initial conditions can lead to improved state estimates throughout the batch as well as improved monitoring and control of end-use quality in many cases. The approach taken in this paper is to reduce the effects of the initial uncertainty resulting from feed disturbances by using algorithms designed to perform on-line smoothing of the initial conditions. First, an Extended Kalman Filter-based fixed-point smoothing algorithm is presented and compared to a popular approach to estimating the initial conditions. Subsequently, a nonlinear optimization-based approach is introduced and analyzed. A sub-optimal on-line approximation to the optimization problem is developed and shown to be directly related to the Extended Kalman Filter-based results. Finally, some practical implementation aspects are discussed, along with simulation results from an industrially relevant example application.     

多核环境下边缘提取并行算法研究

随着处理器由高主频的单核处理器逐步转向片上多核处理器(CMP),计算机并行处理能力不断提升。通过分析GIS串行算法面临的性能瓶颈,利用CMP的优势,采用线程级并行处理栅格数据。针对边缘提取算法,深入分析和比较了MPI、OpenMP等当前主流的并行编程模式,提出了并行性能估计模型。基于OpenMP编程模型分析线程数、调度方式和分块大小对算法并行性能的影响,实现边缘提取最优并行。实验证明,性能评估模型能够准确预测CMP环境下的并行性能,基于OpenMP实现的边缘提取并行算法能够提高图像边缘提取效率。     

Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution

This paper considers the application of the adaptive neuro-fuzzy inference system (ANFIS) instead of the highly nonlinear model of a reactive batch distillation column for optimization. The architecture has been developed for fuzzy modeling that learns information from a data set, in order to compute the membership function and rule base in accordance with the given input–output data. In this work, the differential evolution algorithm has been employed for optimization of operation policy of reactive batch distillation for producing ethyl acetate. In optimization, minimal batch time and high purity of product are considered, and reflux ratio and final batch time are taken as decision parameters. The results show that the reduced model (ANFIS) is able to properly create a robust model of the reactive batch distillation, and CPU use is reduced to 1/18,000 of that of a real mathematical model. The highest yield and mole fraction of ethyl acetate were achieved through the use of the obtained optimization policy.     

Reliable Minimax Parameter Estimation

This paper deals with the minimax parameter estimation of nonlinear parametric models from experimental data. Taking advantage of the special structure of the minimax problem, a new efficient and reliable algorithm based on interval constraint propagation is proposed. As an illustration, the ill-conditioned problem of estimating the parameters of a two-exponential model is considered.     

Implicit rule-based fuzzy-neural networks using the identification algorithm of GA hybrid scheme based on information granulation

This paper proposes an identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN). The design of the model dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithm. The FNN modeling and identification environment realizes parameter estimation through a synergistic usage of clustering techniques, genetic optimization and a complex search method. An HCM (Hard C-Means) clustering algorithm helps determine an initial location (parameters) of the membership functions of the information granules to be used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the optimization algorithm of a GA hybrid scheme. The proposed GA hybrid scheme combines GA with the improved complex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) is used in the model design in order to achieve a sound balance between its approximation and generalization abilities. The proposed type of the model is experimented with several time series data (gas furnace, sewage treatment process, and NOx emission process data of gas turbine power plant).     

Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization

This paper proposes a methodology for automatically extracting T–S fuzzy models from data using particle swarm optimization (PSO). In the proposed method, the structures and parameters of the fuzzy models are encoded into a particle and evolve together so that the optimal structure and parameters can be achieved simultaneously. An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO. CRPSO employs several sub-swarms to search the space and the useful information is exchanged among them during the iteration process. Simulation results indicate that CRPSO outperforms the standard PSO algorithm, genetic algorithm (GA) and differential evolution (DE) on the functions optimization and benchmark modeling problems. Moreover, the proposed CRPSO-based method can extract accurate T–S fuzzy model with appropriate number of rules.     

Hybrid simplex genetic algorithm for blind equalization using RBF networks

The purpose of this paper is to derive a hybrid simplex genetic algorithm for nonlinear channel blind equalization using RBF networks. Most of the algorithms for blind equalization are focused on linear channel models because of their simplicity. However, most practical channels are better approximated by nonlinear models. In order to find an effective method for nonlinear channel blind equalization, here, the equalizer based on RBF networks which is constructed from channel output states instead of the channel parameters is considered. Using the Bayesian likelihood cost function defined as the accumulation of the natural logarithm of the Bayesian decision variable, the problem becomes to maximize the Bayesian likelihood cost function with the dataset which composes the RBF equalizer’s center. For this high dimensional complex optimal problem, the proposed hybrid simplex genetic algorithm solves it by incorporating the simplex operator with GA, and obtains a good convergence characteristic and satisfied equalization result.     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

Robust identification for multi-section freeway traffic models

1IntroductionIt is important to estimate the densityandspeed oftrafficfor the safetyandtraffic control .For decades ,manyresearchwork have been done to estimate traffic density, trafficvolume ,average speed,and other parameters[1,2] .Theproblemof estimating dynamic traffic has been involved inparts of those research work[1 ~4] .By means of O_Dmatrix,some researchers have also made a series of studiesof traffic prediction and traffic layout estimation[5] .However , most of the research work m…     

Robust identification for multi_section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper.To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed.To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently.In the new model identification criterion,numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices.By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed.Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.     

Applications of hybrid optimization techniques for model updating of rotor shafts

This paper presents an approach by combining the genetic algorithm (GA) with simulated annealing (SA) algorithm for enhancing finite element (FE) model updating. The proposed algorithm has been applied to two typical rotor shafts to test the superiority of the technique. It also gives a detailed comparison of the natural frequencies and frequency response functions (FRFs) obtained from experimental modal testing, the initial FE model and FE models updated by GA, SA, and combination of GA and SA (GA–SA). The results concluded that the GA, SA, and GA–SA are powerful optimization techniques which can be successfully applied to FE model updating, but the appropriate choice of the updating parameters and objective function is of great importance in the iterative process. Generally, the natural frequencies and FRFs obtained from FE model updated by GA–SA show the best agreement with experiments than those obtained from the initial FE model and FE models updated by GA and SA independently.     

Macro-grammatical evolution for nonlinear time series modeling��a case study of reservoir inflow forecasting

Streamflow forecasting is significantly important for planning and operating water resource systems. However, streamflow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Utilizing GA with GE optimizes an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimization, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardization is beneficial to model for seasonal time series.     

基于GEP和GA技术的非线性系统辨识研究

给出了利用基因表达式编程（GEP）进行非线性系统辨识的方法,弥补了传统辨识方法需要过多预知信息的不足,有着比遗传编程（GP）更简洁有效的系统模型结构表达方式.利用改进的遗传算法（GA）并行地进行模型参数进化,可以在有限的给定数据内得到合适的模型.关于模型适应度的定义,综合考虑了精确性和复杂性因素,能够获取一种比较折中的辨识结果.仿真结果表明,这种方式可以快速、准确地获取非线性模型.