Parameter mapping and data transformation for engineering application integration

Enterprise applications may be classified into management applications used by managerial staffs for making decisions on business operations, and engineering applications used by engineers for solving multidisciplinary design problems. In the literature, enterprise application integration is extensively addressed in the context of management applications, but insufficiently discussed in the engineering disciplines. Practitioners in manufacturing industries have for a long time feel the increasing need of integrating engineering applications, in order to accelerate product development paces and improve design qualities. Integrating engineering applications used in the multiple engineering disciplines has to cope with a number of challenges. This paper focuses on one of the critical issues: parameter mapping and data transformation, which is of pivotal importance to integrating engineering applications. Design parameter mapping provides a consistent approach to data extraction, storage, display and manipulation among different data sources. Data transformation describes the operational logic of parsing input/output files, extracting and transforming data, and maintaining consistency among multiple data sources.

Nonlinear adaptive control system design and experiment for a 3-DOF model helicopter

This paper deals with model following control of a model helicopter with three degree-of-freedom. Since the decoupling matrix is singular, a nonlinear structure algorithm is used to design the controller. Furthermore, since the model dynamics are described linearly by unknown system parameters, a well-known parameter estimation technique is introduced. The integral type of estimation model is proposed here since the use of the derivative type of model cannot obtain the desired estimation result. The experimental results show the effectiveness of the proposed method. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM.To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.     

Efficient and robust shot change detection

In this article, we deal with the problem of shot change detection which is of primary importance when trying to segment and abstract video sequences. Contrary to recent experiments, our aim is to elaborate a robust but very efficient (real-time even with uncompressed data) method to deal with the remaining problems related to shot change detection: illumination changes, context and data independency, and parameter settings. To do so, we have considered some adaptive threshold and derivative measures in a hue-saturation colour space. We illustrate our robust and efficient method by some experiments on news and football broadcast video sequences.

481－2B加氢精制催化剂的生产及应用

本文介绍了４８１－２Ｂ加氢精制催化剂的生产及应用。     

双级贯流式水轮机导叶和转轮的水力设计

针对一种新型双级贯流式水轮机,推导了一级导叶、一级转轮、二级导叶和二级转轮的参数设计公式。利用数值模拟商业软件Fluent分析了水轮机在设计工况下的数值性能。数值计算时控制方程采用雷诺时均N-S离散方程,湍流模型选择标准k-ε模型,得到了水轮机的流线图、导叶和转轮叶片的压力面和吸力面的压力分布图,以及中心对称面x=0.083 5的速度矢量图,数值计算结果证明了水轮机设计的正确性。     

Parameter Estimation Algorithms for Hierarchical Distributed Systems

There has been a great deal of research activity in the area of identification of distributed parameter systems over the past two decades. An extensive treatment of off-line schemes ( e.g. , output least squares, estimation error, etc. ) together with a comprehensive survey of the literature can be found in the monograph by Banks and Kunisch [4] . In the case of on-line, or adaptive, schemes, the available literature is less extensive and more recent ( Isermann et al . [7] ). The on-line methods give estimates recursively as the measurements are obtained within the time limit imposed by the sampling period. These include recursive projection algorithm ( Baumeister et al. [5] ), recursive least squares algorithm ( Glentis et al . [6] ), on-line excitation algorithms ( Ludwig et al. [8] ), etc . In this paper an equivalent 2nd order dynamical system is formulated from a given trajectory representing the pattern to be recognised and simulated in order to estimate the parameters for hierarchical distributed systems using 1st and 2nd order dynamics. Recommendations for the best estimation strategy are given.     

Maximum likelihood based identification methods for rational models

ABSTRACT

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome this shortcoming, this paper develops two recursive algorithms for estimating the rational model parameters. These two algorithms, based on the maximum likelihood principle, have three integrated key features: (1) to establish two unbiased maximum likelihood recursive algorithms, (2) to develop a maximum likelihood recursive least squares (ML-RLS) algorithm to decrease the computational efforts, (3) to update the parameter estimates by the ML-RLS based particle swarm optimisation (ML-RLS-PSO) algorithm when the noise-to-output ratio is large. Comparative studies demonstrate that (1) the ML-RLS algorithm is only valid for rational models when the noise-to-output ratio is small, (2) the ML-RLS-PSO algorithm is effective for rational models with random noise-to-output ratio, but at the cost of heavy computational efforts. Furthermore, the simulations provide cases for potential expansion and applications of the proposed algorithms.     

随机模拟技术在储层预测中的应用

随机模拟是一种基于地质统计学的储层预测方法,将井资料和波阻抗反演结果联合进行模拟,可以提高预测的精度,适用于滚动勘探开发初期及中后期。本次应用在对储层测井响应特征和物性参数分析的基础上,通过直方、变差分析,选择合理的反演参数,进行随机模拟,进而预测储层的分布规律,实际应用表明,随机模拟方法可提高储层反演的分辨率。