135MW机组一次调频功能优化

介绍云浮发电厂#4机组一次调频功能的基本原理。通过试验,发现一次调频功能存在问题,经过分析,对控制逻辑和参数进行修改,解决了存在问题,并使DCS侧的一次调频功能与DEH侧的功能很好地配合,在不影响机组稳定运行下完全有能力参与一次调频。     

Adaptive target recognition

Target recognition is a multilevel process requiring a sequence of algorithms at low, intermediate and high levels. Generally, such systems are open loop with no feedback between levels and assuring their performance at the given probability of correct identification (PCI) and probability of false alarm (Pf) is a key challenge in computer vision and pattern recognition research. In this paper, a robust closed-loop system for recognition of SAR images based on reinforcement learning is presented. The parameters in model-based SAR target recognition are learned. The method meets performance specifications by using PCI and Pf as feedback for the learning system. It has been experimentally validated by learning the parameters of the recognition system for SAR imagery, successfully recognizing articulated targets, targets of different configuration and targets at different depression angles.     

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.

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。     

Application of evolutionary optimisers in data-based calibration of Activated Sludge Models

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.     

广义三次DP曲线

给出一组带有两个形状参数的三次多项式基函数,它是三次DP基函数的扩展;分析该基函数的性质;基于该组函数定义了广义三次DP曲线,它不仅具有与DP曲线类似的性质,还可通过改变参数几或产使其具有形状可调性。当这两段曲线相拼接时,在满足一定的条件下,曲线可达到G^2或C^2连续,从而为自由曲线设计提供了一种有效的方法。