模型速度建场方法的应用研究

以往的地震速度分析大致分为两个彼此独立的部分:第一部分是静校、相关处理和速度谱,均以提高成像质量为目的;第二部分是解释、速度分析和成图,均以获取地质成果为目的。本文介绍的模型速度建场法则是把上述两部分结合起来进行一体化综合分析,采用三维静校空间场、射线追踪和模型迭代等方法,再结合三维速度场最终完成构造图,速度精度显著提高。本方法在准噶尔盆地盆5井地区应用效果很好。

结构模型缩聚的改进IRS方法

模型缩聚法在结构的静力和动力特性分析中有着广泛的应用。应用模型缩聚法,可以有效降低结构的有限元计算规模,节省计算时间和成本,并能获得和实验测量自由度相匹配的有限元模型。本文在改进模型缩聚方法 (improved reduced system, IRS)的基础上,提出一种考虑二阶惯性量的改进IRS方法,有效改进了IRS方法的计算精度,和模型缩聚迭代法(iterated IRS, IIRS)相比,此方法计算量更小且计算精度更高。以桁架结构和框架结构为例对所提二阶IRS方法进行了验证,并将计算结果与精确值、Guyan缩聚解、IRS缩聚解和IIRS缩聚解进行了比较,结果表明了所提方法计算精度最好,具有良好的工程应用前景。

Bi-level model for shared parking decision-making based on parking lot assignment simulation

In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.

Dissipative Travelling Wave Solution for El Nio Tropic Sea-air Coupled Oscillator

El Nifio and Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific sea-air interactions. An asymptotic method of solving equations for the ENSO model is proposed. Based on a class of oscillator of ENSO model and by employing a simple and valid method of the variational iteration, the coupled system for a sea-air oscillator model of interdecadal climate fluctuations is studied. Firstly, by introducing a set of functionals and computing the variationals, the Lagrange multipliers are obtained. And then, the generalized variational iteration expressions are constructed. Finally, by selecting appropriate initial iteration, and from the iterations expressions, the approximations of solution for the sea-air oscillator ENSO model are solved successively. The approximate dissipative travelling wave solution of equations for corresponding ENSO model is studied. It is proved from the results that the method of the variational iteration can be used for analyzing the sea surface temperature anomaly in the equatorial Pacific of the sea-air oscillator for ENSO model.

Multi-loop Constrained Iterative Model Predictive Control Using ARX -PLS Decoupling Structure

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

Motion estimation based feature selection for visual SLAM

Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.

Soft measurement for component content based on adaptive model of Pr/Nd color features

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

Revisiting the interacting model of new agegraphic dark energy

In this paper, a new version of the interacting model of new agegraphic dark energy （INADE） is proposed and analyzed in detail. The interaction between dark energy and dark matter is reconsidered. The interaction term Q = bh0ραdcρ1-α dm is adopted, which abandons the Hubble expansion rate H and involves both Pae and Pare. Moreover, the new initial condition for the agegraphie dark energy is used, which solves the problem of accommodating baryon matter and radiation in the model. The solution of the model can be given using an iterative algorithm. A concrete example for the calculation of the model is given. Furthermore, the model is constrained by using the combined Planck data （Planck＋BAO＋SNIa＋H0） and the combined WMAP-9 data （WMAP＋BAO＋SNIa＋H0）. Three typical cases are considered： （A） Q = bHoPde, （B） Q = bHo√ρdeρdm,and （C） Q = bHopdm, which correspond to α = 1, 1/2, and 0, respectively. The departures of the models from the ACDM model are measured by the ABIC and AAIC values. It is shown that the INADE model is better than the NADE model in the fit, and the 1NADE（A） model is the best in fitting data among the three cases.

A learning method for energy optimization of the plug-in hybrid electric bus

The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.

Solution for a nonlocal elastic bar in tension

In this paper we use an alternative method to study analytically and numerically for a nonlocal elastic bar in tension.The equilibrium equation of the model is a Fredholm integral equation of the second kind.With the aid of an efficient iterative method,we are able to get the approximate analytical solution.For the purpose of comparisons,numerical solutions are also obtained for two types of nonlocal kernels,which show the validity of the analytical solution.The effects of some related parameters are also investigated.

Identification of the Exchange Coefficient from Indirect Data for a Coupled Continuum Pipe-Flow Model

Calibration and identification of the exchange effect between the karst aquifers and the underlying conduit network are important issues in order to gain a better understanding of these hydraulic systems. Based on a coupled continuum pipe-flow(CCPF for short) model describing flows in karst aquifers, this paper is devoted to the identification of an exchange rate function, which models the hydraulic interaction between the fissured volume(matrix) and the conduit, from the Neumann boundary data, i.e., matrix/conduit seepage velocity. The authors formulate this parameter identification problem as a nonlinear operator equation and prove the compactness of the forward mapping. The stable approximate solution is obtained by two classic iterative regularization methods, namely,the Landweber iteration and Levenberg-Marquardt method. Numerical examples on noisefree and noisy data shed light on the appropriateness of the proposed approaches.