In this paper, the exact analytical solution of the rectangular plate having simplysupported segments mixed with free segments of straight edges are first given by means of the method of reciprocal theorem.By comparis...In this paper, the exact analytical solution of the rectangular plate having simplysupported segments mixed with free segments of straight edges are first given by means of the method of reciprocal theorem.By comparison .we calculate the same question by finite element method.Thecomparison shows that the analytical solution is correct.展开更多
Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effec...Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.展开更多
Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmenta...Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.展开更多
Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法...在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.展开更多
文摘In this paper, the exact analytical solution of the rectangular plate having simplysupported segments mixed with free segments of straight edges are first given by means of the method of reciprocal theorem.By comparison .we calculate the same question by finite element method.Thecomparison shows that the analytical solution is correct.
基金Supported by the National Natural Science Foundation of China (No. 60475024)
文摘Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.
文摘Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
文摘在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.
