排队“梯度”浮点数编码遗传算法

文章主要论述了浮点数编码遗传算法优化的问题。充分利用个体及其适应度函数值的已知信息,依据一定算法得到“梯度”信息,从而加快算法的收敛速度。但如果个别适应度函数值相对较大(例如是全体适应度函数值平均值的100倍以上),则上述利用“梯度”信息的优势尽失。为了解决此问题,提出排队“梯度”浮点数编码遗传算法。最后用典型的四个常用测试函数分别对简单浮点数编码遗传算法、利用“梯度”信息的浮点数编码遗传算法和排队“梯度”浮点数编码遗传算法的性能进行了测试和比较。

A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

Mutual Information for Image Registration

Image registration is the overlaying of two images of the same scene taken at different times or by different sensors. It is one of the essential steps in information processing in remote sensing. To attain a highly accurate, reliable and low computation cost in image registration a suitable and similarity metric and reduction in search data and search space is required. In this paper, the author shows that if the right bin size is chosen, mutual information can be more robust than correlation in the registration of multi-temporal images. The author also compares the sensitivity of mutual information and correlation to Gaussian and multiplicative speckle noise. The author investigates automatic subimage selection as a reduction in search data strategy. The author proposes a measure, called alienability, which shows the ability ofa subimage to provide reliable registration. Alternate subimage selection methods such as using gradient, entropy and variance are also investigated. The author furthermore looks into a search space strategy using a gradient approach to maximize mutual information and show our first results.

Full waveform inversion based on improved MLQN method

Full waveform inversion( FWI) is a challenging data-fitting procedure between model wave field value and theoretical wave field value. The essence of FWI is an optimization problem,and therefore,it is important to study optimization method. The study is based on conventional Memoryless quasi-Newton( MLQN)method. Because the Conjugate Gradient method has ultra linear convergence,the authors propose a method by using Fletcher-Reeves( FR) conjugate gradient information to improve the search direction of the conventional MLQN method. The improved MLQN method not only includes the gradient information and model information,but also contains conjugate gradient information. And it does not increase the amount of calculation during every iterative process. Numerical experiment shows that compared with conventional MLQN method,the improved MLQN method can guarantee the computational efficiency and improve the inversion precision.

Three-dimensional conjugate gradient inversion of magnetotelluric full information data

Based on the analysis of impedance tensor data, tipper data, and the conjugate gradient algorithm, we develop a three-dimensional （3D） conjugate gradient algorithm for inverting magnetotelluric full information data determined from five electric and magnetic field components and discuss the method to use the full information data for quantitative interpretation of 3D inversion results. Results from the 3D inversion of synthetic data indicate that the results from inverting full information data which combine the impedance tensor and tipper data are better than results from inverting only the impedance tensor data （or tipper data） in improving resolution and reliability. The synthetic examples also demonstrate the validity and stability of this 3D inversion algorithm.

SPATIAL GRADIENT ANALYSIS OF URBAN GREEN SPACES COMBINED WITH LANDSCAPE METRICS IN JINAN CITY OF CHINA

Urban green spaces have been arisen growing concern responded to the social and environmental costs of urban sprawl. A wide range of planning and policies has been and/or will be designed to protect urban green spaces and optimize their spatial pattern. A better design or planning of urban green space can make a major contribution to quality of environment and urban life, and furthermore can decide whether we can have a sustainable development in the urban area. Information about the status quo of urban green spaces can help planners design more effectively. However, how to quantify and capture such information will be the essential question we face. In this paper, to quantify the urban green space, a new method comprising gradient analysis, landscape metrics and GIS was developed through a case of Jinan City. The results demonstrate: 1) the gradient analysis is a valid and reliable instrument to quantify the urban green space spatial pattern precisely; 2) using moving window, explicit landscape metrics were spatially realized. Compared with quantifying metrics in the entire landscape, it would be better to link pattern with process and establish an important basis for analyzing the ecological and socioeconomic functions of green spaces.

Aerodynamic design optimization by using a continuous adjoint method

This paper presents the fundamentals of a continuous adjoint method and the applications of this method to the aerodynamic design optimization of both external and internal flows.General formulation of the continuous adjoint equations and the corresponding boundary conditions are derived.With the adjoint method,the complete gradient information needed in the design optimization can be obtained by solving the governing flow equations and the corresponding adjoint equations only once for each cost function,regardless of the number of design parameters.An inverse design of airfoil is firstly performed to study the accuracy of the adjoint gradient and the effectiveness of the adjoint method as an inverse design method.Then the method is used to perform a series of single and multiple point design optimization problems involving the drag reduction of airfoil,wing,and wing-body configuration,and the aerodynamic performance improvement of turbine and compressor blade rows.The results demonstrate that the continuous adjoint method can efficiently and significantly improve the aerodynamic performance of the design in a shape optimization problem.