对称四阶偏微分方程去噪算法

通过分析整体变分模型的去噪原理和效果,提出一个新的四阶偏微分方程去噪模型,用于克服二阶偏微分方程去噪后使图像分块的缺点,同时保持去噪后图像的高保真性,并发展一个基于四邻域系统的对称离散化算法用于求解新模型,应用中值滤波去除四阶偏微分方程去噪所引起的亮点。实验结果表明,与传统方法相比,以该算法去噪后的图像具有更好的质量和视觉效果。     

基于EAPR流程的动态局部可重构实现

介绍实现动态局部可重构的方法,以EAPR流程为例,阐述通过时/空复用技术实现动态局部自动重构的基本过程。在此基础上,使用芯片内嵌的硬核处理器Power PC405来调度和管理芯片上其他可编程逻辑资源的自重构过程。在Virtex II Pro开发板上进行验证,结果表明,使用较小容量的FPGA硬件资源,可完成超过其容量规模的系统设计。     

Regression conformal prediction with random forests

Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing state-of-the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.     

混合PLS特征提取和CVM的异常入侵检测研究

为提高异常入侵检测的效率,提出一种混合偏最小二乘特征提取和核心向量机算法的入侵检测模型。模型使用偏最小二乘算法在入侵数据集上进行主成分提取,在此基础上构建特征集,引入适用于解决大规模样本训练问题的核心向量机算法,在特征集上建立入侵检测模型,使用该模型对异常入侵行为进行检测和判断。通过基于KDD99数据集上的入侵检测实验,验证了混合模型的可行性和有效性。     

Prediction error identification of linear systems: A nonparametric Gaussian regression approach

A novel Bayesian paradigm for the identification of output error models has recently been proposed in which, in place of postulating finite-dimensional models of the system transfer function, the system impulse response is searched for within an infinite-dimensional space. In this paper, such a nonparametric approach is applied to the design of optimal predictors and discrete-time models based on prediction error minimization by interpreting the predictor impulse responses as realizations of Gaussian processes. The proposed scheme describes the predictor impulse responses as the convolution of an infinite-dimensional response with a low-dimensional parametric response that captures possible high-frequency dynamics. Overparameterization is avoided because the model involves only a few hyperparameters that are tuned via marginal likelihood maximization. Numerical experiments, with data generated by ARMAX and infinite-dimensional models, show the definite advantages of the new approach over standard parametric prediction error techniques and subspace methods both in terms of predictive capability on new data and accuracy in reconstruction of system impulse responses.     

基于偏微分方程的舌图像去噪研究

在对舌图像的去噪过程中,平滑噪声的同时容易丢失边缘和纹理等细节信息。为此,研究基于偏微分方程的舌图像去噪方法,分别采用中值滤波、高斯滤波、P-M方程、正则化P-M方程以及耦合冲击-复扩散滤波模型,对加噪舌图像进行滤波。比较结果表明,正则化P-M方程更适合舌图像的去噪处理,该方法处理速度快、去噪效果好,且能有效保护图像边缘。     

Oracle-Based Partial Evaluation

We present Oracle-Based Partial Evaluation (OBPE), a novel approach to on-line Partial Evaluation (PE) which decides the control strategy to use for each call pattern by using an oracle function which compares the results of specializing such call pattern w.r.t. a set of strategies. Our proposal is motivated by Poly-Controlled Partial Evaluation (PCPE), which allows using different control strategies for different call patterns. Given a set of control strategies, the best PCPE specialized programs outperform the specialized programs obtained by traditional PE for any of the control strategies in , especially when resource-aware specialization is performed. Unfortunately, computing all PCPE specialized programs and then choosing a posteriori the best one is too costly in practice. In contrast, in OBPE a single specialized program is computed. We have developed an empirical oracle whose parameters are approximated from a set of training data, by using constraint logic programming. Our experimental results show that the additional cost of OBPE when compared with traditional PE is a constant factor and that, at least in our experiments, OBPE obtains significantly better specializations. We argue that our proposal is relevant in practice and introduces clear improvements over standard PE. Our work is developed in the context of logic programs, though the ideas are in principle of interest to the PE of any programming language.     

Optimizing ontology alignment through Memetic Algorithm based on Partial Reference Alignment

All the state of the art approaches based on evolutionary algorithm (EA) for addressing the meta-matching problem in ontology alignment require the domain expert to provide a reference alignment (RA) between two ontologies in advance. Since the RA is very expensive to obtain especially when the scale of ontology is very large, in this paper, we propose to use the Partial Reference Alignment (PRA) built by clustering-based approach to take the place of RA in the process of using evolutionary approach. Then a problem-specific Memetic Algorithm (MA) is proposed to address the meta-matching problem by optimizing the aggregation of three different basic similarity measures (Syntactic Measure, Linguistic Measure and Taxonomy based Measure) into a single similarity metric. The experimental results have shown that using PRA constructed by our approach in most cases leads to higher quality of solution than using PRA built in randomly selecting classes from ontology and the quality of solution is very close to the approach using RA where the precision value of solution is generally high. Comparing to the state of the art ontology matching systems, our approach is able to obtain more accurate results. Moreover, our approach’s performance is better than GOAL approach based on Genetic Algorithm (GA) and RA with the average improvement up to 50.61%. Therefore, the proposed approach is both effective.     

部分计值综述

部分计值是一种程序转换技术．在给定程序部分输入的情况下，可使用该技术对程序进行例化，完成程序中尽可能多的计算，最终得到高效的剩余代码。人们已经研究了许多程序设计语言的部分计值系统，并把它们应用到编译和编译器生成、计算机图形学等领域。本文介绍了部分计值理论及其应用．讨论了Java部分计值器的研究现状．并简单描述了本课题组设计的一个Java分布式部分计值系统DJmix。     

利用部分观测跟踪被遮挡的目标

提出了一个在单个固定摄像机下进行多目标跟踪的方法.利用亮度和色度混合模型和卡尔曼滤波器来检测跟踪目标,为了利于预测和解释被遮挡的物体,建立了场景的模型.在遮挡的情况下,和传统的盲跟踪不同,本文中的目标状态是由可用的部分观测来估计的.对目标的观测取决于预测、前景观测和场景模型.这使得本文算法在定性或定量的分析下都表现出更加鲁棒的性能.     

基于非压缩后缀树的在线PPM预测模型

PPM模型适合预测用户的下一个请求,但已有的PPM模型不具备在线性,更新通过重构来实现,不能满足实时更新的要求。该文提出基于非压缩后缀树的在线PPM预测模型,采用非压缩后缀树实现增量式在线更新,提高了模型的更新速度。该模型的优点是具备在线性。     

Developing a soft sensor with online variable reselection for unobserved multi-mode operations

Soft sensors are used to predict response variables, as these variables are difficult to measure, the prediction models use data of predictors that are relatively easier to obtain. Arranging time-lagged data of predictors and applying the partial least squares (PLS) method to the dataset is a popular approach for extracting the correlation between data of the responses and predictors of the process dynamic. Because irrelevant inputs deteriorate the prediction performance of the soft sensor, the selection of variables in the PLS-based model is a critical step for developing a robust and accurate model. Furthermore, it is necessary to reselect the important predictors of a soft sensor when the operating mode is changed. However, a switch in the operating mode may not be measured, directly. In this study, two statistics are proposed to detect a change of operating mode to enable the reselection of the predictors of the soft sensor. This work involved the development of a soft sensor based on operating data from the industrial ethane removal (de-ethane) process. The changeover of crude oil types cannot be observed from the data of process variables; however, the correlation between input and output variables is significantly affected by the different types of crude oil. The result shows that the use of a soft sensor with online variable reselection is capable of maintaining the accuracy and robustness of the inferential model, effectively.     

Derived components regression using the BACON algorithm

Partial least squares and principal components regression are commonly used regularized regression methods which use derived components instead of original predictors. The components are derived from the estimated variance-covariance matrix and regression is run using the least squares. Therefore, they are not robust and a few outliers may have drastic effects on the obtained results. These regression methods are robustified by using the BACON algorithm which provides robust measures for both dispersion and regression. The proposed methods are illustrated by examples and their properties are investigated using both real data and simulation experiments.     

生理点配准的颅面局部形态关系建模

为提高对颅骨与面皮之间形态关系建模的准确度,提出一种生理点配准的颅面局部形态关系建模方法。以特征点对应关系为约束条件,基于TPS变形实现颅面初配准;根据欧氏距离与局部几何特征的加权距离确定顶点的最优生理对应关系,建立分类颅面模型库;将颅面形态关系建模问题分解为对每一面皮生理点各坐标分量建模的子问题集,运用单因变量多自变量PLSR,以面皮生理点的各坐标分量为因变量,选取显著相关的颅骨生理点相应坐标分量为自变量,建立由各面皮生理点坐标计算模型构成的局部形态关系模型。实验结果表明该方法可更准确有效地描述颅面形态变化关系。     

Consistency of functional learning methods based on derivatives

In some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather than on the original functions. As a consequence, the use of derivatives is a common practice in Functional Data Analysis, despite a lack of theoretical guarantees on the asymptotically achievable performances of a derivative based model. In this paper, we show that a smoothing spline approach can be used to preprocess multivariate observations obtained by sampling functions on a discrete and finite sampling grid in a way that leads to a consistent scheme on the original infinite dimensional functional problem. This work extends (Mas and Pumo, 2009) to nonparametric approaches and incomplete knowledge. To be more precise, the paper tackles two difficulties in a nonparametric framework: the information loss due to the use of the derivatives instead of the original functions and the information loss due to the fact that the functions are observed through a discrete sampling and are thus also unperfectly known: the use of a smoothing spline based approach solves these two problems. Finally, the proposed approach is tested on two real world datasets and the approach is experimentaly proven to be a good solution in the case of noisy functional predictors.     

基于偏微分方程的误差分散逆半调改进算法

结合误差分散半调噪声特征,提出一种基于偏微分方程的逆半调改进算法。通过研究偏微分方程的去噪原理,以3次B样条函数作为扩散函数,采用迭代求解偏微分方程的方法估计逆半调图像,计算每次迭代前后图像梯度模值的增量以确定平滑度的调节参数,解决偏微分方程在应用中的参数选择自适应问题。实验结果表明,该算法在图像整体平滑度和细节保持能力上都具有较好的效果。     

具有公平性约束的CTL部分状态空间模型检测

检测部分状态空间是近年来出现的有效解决状态爆炸的模型检测技术，部分Kripke 结构是描述部分状态空间的形式框架。文章主要讨论一类具有公平性约束条件的CTL（计算树逻辑）模型检测问题。定义了部分公平Kripke结构和公平序，分别来表征部分公平状态空间和它们之间的序关系。并给出相应的3值CTL语意和相关定理来说明部分状态空间模型检测技术同样适用于具有公平性约束条件的CTL模型检测问题。     

基于改进的非参数方法的软件失效预测模型

基于主成分分析(PCA)和改进的N-W非参数估计法(INW)提出了一种新的软件失效预测模型。首先,通过对非参数估计的训练样本集进行主成分分析来减少非参数回归估计和预测的输入因子数,再利用PCA计算的方差贡献率作为非参数方法中带宽矩阵的权重,消除各输入因子对结果的作用程度不同所造成的影响,进而建立软件失效预测模型。最后基于一组真实软件失效数据集Eclipse JDT进行实例分析。结果表明,基于改进的非参数方法的软件失效预测模型在预测的精度和稳定性上得到了进一步提高。在后10步的预测范围内,预测值的平均误差为16.2575,均方百分比误差为0.0726。     

一个基于偏序事件结构的Web服务模型及其形式化组装

随着Internet的发展,服务化和Web化趋势使得一个基于Web的分布式软件服务计算环境正在形成;同时随着大规模应用需求的不断涌现,单个的Web服务往往不能很好地满足一些复杂的应用。因此Web服务之间的集成组装就显得尤为重要。分析Web服务的交互和协同行为可以发现,分布性和并发性是基于软件服务分布计算系统的本质特征。这就往往使得组装的正确性难以得到保证,从而需要提供一定的手段加以支持;同时,基于软件服务分布计算系统的效率问题也值得关注。我们认为,利用偏序关系对这些问题加以处理是一种理想和有效的手段;而同时我们发现,在服务的组装中,服务与服务之间存在着一种自然的偏序关系。本文基于此,从形式化的角度研究了软件服务及其组装,提出了一种基于偏序事件多集的Web服务的形式化模型。本文从服务内部事件而不仅仅是服务的角度来考察并发问题,这使服务内和服务问的更多的事件可以并行执行,并使得组装后的系统可以更高效地进行实施,从而使得Web服务的并发拉度变细;在此基础上给出了一种服务组装语言和规则,以确保在组装过程中出现的局部变化不影响整体的Web服务的正确组装,从而能够从形式化的角度来规范Web服务的集成组装,使其正确性和效率能够得到保证。     

基于方向场的指纹图像偏微分方程修补模型

提出一个用于指纹图像修复的新的偏微分方程(PDE)模型,该模型对指纹图像的缺损区域能进行有效的修补。通过分析比较现有的技术方案对指纹图像修补的不足:一般常见的图像修复模型由于缺乏方向场的几何信息,对于指纹图像,这些模型不能给出满意的修补结果;或者虽然引入了方向场的几何信息,但在具体修补时会出现将不同的脊线连到一起不同的错误结果。该模型采用方向场作为扩散方向,在扩散过程中灰度信息沿着表征脊线方向的局部固定方向传播到待修复区域中, 改进了一般PDE模型不能用于修复指纹图像的不足。数值实验结果表明在修复指纹图像时提出的模型优于一般的模型