Level set based on signed pressure force function and its application in liver image segmentation

The edge-based level set model gives no satisfactory results for images with weak edge,and the region-based model performs poorly for intensity inhomogeneity images.In this paper,we propose an improved region-based level set model that integrates both the gradient information and the region information.The proposed model defines a novel external energy term,which consists of gradient information and signed pressure forces function.In order to eliminate the re-initialization procedure of traditional level set model,an internal energy term is also introduced for the level set function to maintain signed distance function.Compared with traditional models,our model is more robust against images with weak edge and intensity inhomogeneity.Experiments on liver segmentation from abdominal CT images demonstrate the effectiveness and accuracy of the proposed method.     

The dynamic and stochastic instability of betas: Implications for forecasting stock returns

The purpose of this paper is to simultaneously investigate several important issues that feature the dynamic and stochastic behavior of beta coefficients for individual stocks and affect the forecasting of stock returns. The issues include randomness, nonstantionarity, and shifts in the mean and variance parameters of the beta coefficient, and are addressed within the framework of variable-mean-response (VMR) random coefficients models in which the problem of heteroscedasticity is present. Estimation is done using a four-step generalized least squares method. The hypotheses concerning randomness and nonstationarity of betas are tested. The time paths, sizes, and marginal rates of mean shifts are determined. The issue of variance shift is examined on the basis of five special tests, called T*, B, S', G and W. Then the impacts of the dynamic and stochastic instability on the estimation of betas is tested by a nonparametric procedure. Finally, the VMR models' ability of forecasting stock returns is evaluated against the standard capital asset pricing model. The empirical findings shed new light on the continuing debate as to whether the beta coefficient is random and nonstationary and have important implications for modeling and forecasting the measurement of performance and the determination of stock returns.     

图的减边控制数的一些新下界

在已有减边控制函数定义的基础上,引入了斯的控制参数--边度,并利用分类的方法对文献[7]的问题2进行了探索,得到了一般图的关于边数的减边控制数的若干下界.     

多值知识编译

定义了一类新的易处理理论:s-EPCCL理论.在此基础上,提出了一种以s-EPCCL理论为目标语言的多值知识编译方法.该方法与现有知识编译方法不同的是,它可以对多值知识库进行编译.经过多值编译后,任意查询都可以在多项式时间内得到应答.     

基于结构平衡理论及LP算法的符号网络预测

符号网络分析逐渐成为一个越来越重要的研究主题,其中最为重要的是网络中的符号推断问题.了解到在社会网络中局部路径指标(LP)表现良好.用AUC评价指标进行了实验验证,LP指标同样适用于符号网络,并且给出它的平衡理论解释.实际数据分析的结果显示,较之传统计算符号网络链路预测算法,该方法更加简单,并且能得到较好的预测效果.     

关于图的减边控制

引入了图的减边控制的概念,给出了一个图G的减边控制数γ′m(G)的两个下界,确定了完全图、圈和轮图的减边控制数,并提出了若干未解决的问题和猜想.     

整数值Z上的混合符号稀疏算子INAR(1)模型

利用混合符号稀疏算子建立整数值Z上的INAR(1)模型, 证明模型的严平稳性和遍历性, 讨论模型的转移概率、 条件期望和条件方差, 并用最大似然方法估计模型参数.     

一种测地线活动轮廓模型的预处理方法

为解决测地线活动轮廓模型在图像分割中的平滑预处理以及图像梯度的计算,并提高水平集方法实现该模型时构造符号距离函数的速度,通过对Gaussian函数以及差分运算的讨论,提出了一种基于sobel算子图像预处理方法,并通过对8-邻域点性质的分析,给出了一种基于邻域扫描的符号距离函数计算方法.实验结果表明,Sobel算子可以同时实现图像的平滑处理并计算梯度,而且降低了差分计算梯度的时间复杂度,而邻域扫描方法相比其他符号距离函数的构造方法能够提高模型算法的执行效率.     

SDG故障诊断模型的检验和验证

将定量模型检验和验证的思想应用于符号有向图（SG或SDG）定性故障诊断模型,以使其达到较高的完备性和分辨率。基于SDG HAZOP平台,提出了针对SDG定性模型检验和验证的非正式检验、静态检查、动态试验和诊断结果半定量风险分析的4个关键步骤,给出了静态检查中的操作点非正常原因可达性检验和不利后果可达性检验步骤的具体内容。以加热炉SDG故障诊断模型为例,验证了上述步骤的有效性。     

含价格折扣和模糊缺陷率的EOQ模型研究

考虑到实际库存管理中的产品缺陷和价格折扣问题,给出了含价格折扣的确定产品缺陷率和模糊产品缺陷率的EOQ库存模型．针对LR型模糊缺陷率,运用符号距离法将模糊模型转化为确定模型,进而确定了其最优订货策略。理论上揭示出：随着缺陷率趋大的模糊性增加,订货量和单位时间总成本增加;而随着缺陷率趋小的模糊性增加,订货量和单位时间总成本减少;并且当模糊缺陷率为确定数时,模糊缺陷率的EOQ模型转化为确定缺陷率的EOQ模型。仿真结果与理论分析是吻合的。