Dongmei Fu

The method for material corrosion modelling and feature selection with SVM-RFE

Material corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the modeling and feature selection of Material corrosion data. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing. By combining RFE and SVM, a novel feature selection method SVM-RFE is introduced. Then integrating this feature selection method and SVM modeling method, a special modeling framework is built. According to the experiments, the priority of this method is established not only on algorithm efficiency but also on predicting precision.

An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling

As for massive multidimensional data are being generated in a wide range of emerging applications, this paper introduces two new methods of dimension reduction to conduct small-sample size and high-dimensional data processing and modeling. Through combining the support vector machine (SVM) and recursive feature elimination (RFE), SVM-RFE algorithm is proposed to select features, and further, adding the higher order singular value decomposition (HOSVD) to the feature extraction which involves successfully organizing the data into high order tensor pattern. The validation of simulation experiment data shows that the proposed novel feature selection and feature extraction methods can be effectively applied to the research work for analyzing and modeling the data of atmospheric corrosion. The feature selection method pledges that the remaining feature subset is optimal; feature extraction method reserves the original structure, discriminate information, and the integrity of data, etc. Finally, this paper proposes a complete data dimensionality reduction solution that can effectively solve the high-dimensional small sample data problem, and code programming for this solution has been implemented.

A novel immune image template set for fuzzy image segmentation and its application research

Image segmentation is one of the classic problems in the computer vision field. Although a lot of successful operators and algorithms have been proposed, fuzzy image segmentation does not always achieve satisfactory results. This paper is inspired by Positive Selection Algorithm and Negative Selection Algorithm and, is based on the mechanism and process where T-cell is activated by the MHC molecule. A new positive selection algorithm is introduced which establishes so-called templates set for immune detection. This algorithm is based on processing of image information represented as a gray value statistic rather than arithmetic gradient formulation. It is comprised of a template set not just a single template. Therefore it gives good results for different images. The presented algorithm is used for image segmentation into objects, background and fuzzy edge in fuzzy infrared images.

Application of artificial immune algorithm in image segmentation based on immune field

Image segmentation is one of the classic problems in image processing and computer vision field. Existed algorithms do not always reach a satisfactory purpose in fuzzy image segmentation. This paper is inspired by new development of medical immunology and proposes an artificial immune algorithm based on immune field. First, the article gives the concept of innate immune field, adaptive field and the immune field by learning from the operating mechanism between innate immune system and adaptive immune system. Second, the paper builds an artificial immune network of combination between innate immune and adaptive immune to divide the antigen feature space. This novel artificial immune algorithm is used for segmenting of object, background and thermal diffusion region in sheltered infrared image. Experimental results show that the method we proposed can solve the problem of incomplete target and edge distortion, and have a comparatively satisfying result with comparison to some segmentation methods, such as immune template, Prewitt operator, Sobel operator and negative selection.

Automatic measurement of intima media thickness with preselection of the most suitable places

This paper describes a novel method for a robust segmentation of layers situated on the far wall of the common carotid artery. Thickness of innermost two layers, called intima-media thickness (IMT), is highly important marker predicting a risk of cardiovascular events. The novelty of the proposed methods resides in an automatic initial determination of suitable places along the artery, where the layers are the most visible and easy to segment. Then, the IMT is measured only in these places and not along whole artery as is performed in almost all state of the art methods. This enables to measure the IMT even in images where the layers are visible only in a part of image. This is one of the main benefits of the proposed method as well as its low computational burdens and low demands on precision of its initialization.

The study on infrared image mosaic application using immune memory clonal selection algorithm

Infrared imaging technology is widely used in industrial and military fields. When a large target need to be photographed but one picture could not accommodate; then several different areas of this target must be photographed firstly, afterwards these split areas need to make image mosaic. To do this, this paper proposes an image mosaic algorithm which is called immune memory clonal selection algorithm. The algorithm determines the matched positions of infrared images, after finding the feature points of infrared images by using Susan algorithms. Simulations of the proposed algorithm show that the method is effective. The algorithm is applicable not only in infrared image mosaic, but also in visible image mosaic with complex background.