基于模糊支持向量的多标签分类方法

单实例多标签分类是指一个样本拥有多个标签的分类问题,对此提出了一种基于半模糊核聚类和模糊支持向量机的多标签分类算法.该算法采用一对一分解策略将多类多标签数据集分解为多个两类双标签数据子集,在每个子集上训练两类双标签模糊支持向量机.为提高分类器的性能引入了半模糊核聚类技术.实验结果表明,与现有的一些算法相比新算法具有其优...     

Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.     

特征尺度自动选择边缘分割方法

针对复杂边缘错综交织而不易分割的问题，提出了特征尺度自动选择边缘分割方法.该方法通过定义相邻两层离散尺度空间中边缘点最大偏移量，以及分析极值路径的4种演化方式，得到具有实际意义的边缘点特征尺度.根据边缘线尺度直方图和尺度空间边缘连通性，对交织的边缘进行剥离和合并，得到分割的结果.通过在多幅测试图片上的实验表明，根据尺度范围获得的边缘多尺度表达层次清晰分明，并且按照显著性提取的显著边缘较少出现细碎旁枝与实际显著边缘能较好地吻合，证明提出的边缘分割方法切实有效.

     

HIV模型的统计诊断

描述HIV的数学模型是一组非线性常微分方程,其中包括CD4+T细胞再感染率和死亡率,HIV病毒死亡率等多个重要未知参数,准确估计这些参数有助于正确了解患者病情发展,以采用个人化治疗方案.针对此模型已提出多个参数估计方法,包括基于数值解的非线性最小二乘,截面似然及两步估计.另一方面,由于客观因素影响,各观测数据对于参数估计的影响大小不同,找出对参数估计影响大的观测值,进一步分析其原因具有重要意义.基于HIV模型的两步估计,构造了用于影响分析的非参数Cook型统计量,并给出其大样本分布.通过模拟数据和临床数据分析发现:(1)所构造统计量可以检测中度以上偏移.(2)相对于内点,边界点对HIV模型的参数估计影响更大.基于以上结论,建议对HIV模型进行参数估计时,对边界点应加以格外关注.提出的方法也可以推广到其他线性常微分方程模型的统计诊断问题中.     

一种消除锯齿的图像放大算法

为了最大程度消除图像放大后出现的锯齿现象,提出了一种新的二次导数连续的多结点立方卷积插值函数,通过增加结点使得插值曲线更加的平滑;同时对插值函数引入逆梯度约束条件,然后根据图像放大的倍数定义一个适当的模板对图像进行卷积操作,自适应的消除图像放大后出现的锯齿现象。实验结果表明,该算法在对图像执行高倍放大时,几乎看不到锯齿,同时图像的清晰度也保持的较好。     

杜哈梅尔积分的全解析算法

从杜哈梅尔积分的严格定义式出发，利用其解析式中存在的广义函数，提出杜哈梅尔积分的全解析算法。给出了用于解析计算的通用解析表达式。算例表明，本算法简洁，特别适于当系统的输入ｘ（ｔ）或其阶跃响应ｓ（ｔ）为具有不连续点时的复杂函数情况。     

一类卷积型方程

利用高阶奇异积分的Ｆｏｕｒｉｅｒ变换和高阶奇异积分方程研究一类带幂权核的卷积型方程，讨论了它的可解性条件、解的数目及解法。     

再生核空间W_2~1(R)中的分段投影插值

本文在具有再生核的空间W_2~1(R)中讨论分段插值问题,给出W_2~1(R)的再生核的解析式,并构造一个分段插值公式。     

正定Lipschitz核的本征值

设 G 是 R~d 中闭单位正方体,正定对称核 K(x,y)在 G×G 上满足α阶的 Lipschitz条件。本文证明,由 K(x,y)生成的积分算子 K 的本征值渐近为 O(1/(n~(1+α/d)))。     

二阶卷积的构造

文中给出了二阶卷积算子的定义和性质并举例说明。