用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。     

优化发电能源结构的初探

从我国电力工业发展的角度，浅析了国内外发电能源结构及其变化趋势，作者认为优化我国发电能源结构、采取必要的技术措施，是实现我国电力 工业可持续发的重要途径。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于可靠度理论的黄土高边坡优化设计

对黄土高边坡进行可靠性分析，应用数学原理和优化原理，建立了黄土高边坡的优化模型，对铜黄一级公路黄土高边坡进行分析验证。结果表明，该方法计算的结果与实际较接近．应用中易操作。     

LAMOST总控系统程序设计

国家重大科学工程项目LAMOST总控系统的程序设计是一个庞大的软件工程．其总控系统下共包括8个既相互独立又互有关联的子系统．如何通过良好的软件设计方法来处理好这8个子系统之间的关系使其协调运转，以及如何确保软件开发的可维护性、可持续性是研究重点．     

形状误差圆柱度的优化计算

建立了简明的圆柱度数学模型，并采用优化方法进行求解，为圆柱度误差值的评定提供了较理想的计算方法。     

Linux 防火墙的研究与设计

阐述了Linux2．4内核的防火墙Linux的原理，并分析了netfilter的设计与实现，叙述了Linux的动态地址转换，介绍了在Linux下基于netfilter架构如何开发自己的防火墙并给出具体实例。     

Combined mine tremors source location and error evaluation in the Lubin Copper Mine (Poland)

A modified method of mine tremors location used in Lubin Copper Mine is presented in the paper. In mines where an intensive exploration is carried out a high accuracy source location technique is usually required. The effect of the flatness of the geophones array, complex geological structure of the rock mass and intense exploitation make the location results ambiguous in such mines. In the present paper an effective method of source location and location's error evaluations are presented, combining data from two different arrays of geophones. The first consists of uniaxial geophones spaced in the whole mine area. The second is installed in one of the mining panels and consists of triaxial geophones. The usage of the data obtained from triaxial geophones allows to increase the hypocenter vertical coordinate precision. The presented two-step location procedure combines standard location methods: P-waves directions and P-waves arrival times. Using computer simulations the efficiency of the created algorithm was tested. The designed algorithm is fully non-linear and was tested on the multilayered rock mass model of the Lubin Copper Mine, showing a computational better efficiency than the traditional P-wave arrival times location algorithm. In this paper we present the complete procedure that effectively solves the non-linear location problems, i.e. the mine tremor location and measurement of the error propagation.     

The spatial implications of the functional proximity deriving from air passenger flows between European metropolitan urban regions

Until recently the traditional spatial configuration of the European geography was based upon the core-periphery model. The ‘pentagon’, broadly defined as lying between London, Paris, Milan, Munich and Hamburg, was seen as the core area characterised by having the highest concentration of economic development in the European Union (EU), with the remainder of the European territory viewed as peripheral, albeit to varying degrees. In a number of cases such peripheral areas equated with clear regional disparities. The elaboration of the European Spatial Development Perspective (ESDP) (CEC, European spatial development perspective, towards balanced and sustainable development in the territory of the European Union, 1999) challenged this core-periphery model. European spatial planning policies, aimed at encouraging social and economic, and with ever increasing importance, territorial cohesion, seek amongst other aspects to encourage the development of a balanced and polycentric urban system. This paper adopts a network analysis approach to the analysis of air passenger flows between some 28 principal European metropolitan urban regions. The evaluation of these flows contributes to an enhanced comprehension of the spatial dynamics of the European metropolitan territory which goes beyond that deriving from the more standard analyses of the individual components of the urban system. Several indicators are used, deriving from gravitational modelling techniques, to analyse the complexity of the air passenger flows. A multidimensional scaling (MDS) technique is introduced in order to interpret and visualise the resulting spatial configuration and positioning of the different metropolitan centres within the conceptual European ‘space of air passenger flows’, thereby contrasting with the more traditional map-based geographical image of Europe, based upon Cartesian coordinates.

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.