艾滋病疗法的评价及疗效的预测

研究艾滋病疗法的评价及疗效的预测问题,在美国艾滋病医疗试验机构 ACTG 公布的两组数据 ACTG320 和193A 的基础上,利用随机过程理论及数理统计分析方法进行了研究:首先将 ACTG320 样本依据仞始 CD,浓度分成三类,对每类进行插值得到每个病人固定时间间隔序列,对各时间区的浓度进行拟合,从而建立各类人群最佳终止时间的模型;其次,对 193A 利用时间序列分析、假设检验和相关性检验得出各段 CD4 平均增长率近似服从正态分布,且与年龄无明显相关性,插值拟合四种疗法 CD4 平均增长率的时间函数,依据单侧检验得到评定疗法优劣的模型;最后,引进了药物经济学中的成本效果一分析方法,利用单位效果需花费成本的时间函数,得到考虑费用条件下的最佳治疗终止时间模型.     

一类三角多项式曲线的性质及应用

在三角函数空间Φ₇=span{1,sint,cost,cos2t,sin3t,cos3t,sin4t,cos4t}和Φ₈=span{1,sint,cost,sin2t,cos2t,sin3t,cos3t,sin4t,cos4t}中构造了B-L（Bézier-Like）曲线,并给出其显式表达式。进一步讨论了该曲线的若干性质和应用,给出了不需要有理形式的心脏线、椭圆（圆）弧等的B-L曲线精确表示,椭球（球）面的B-L曲面精确表示,以及圆柱螺线的B-L曲线逼近表示。通过实例说明在造型设计方面使用简便且有效。     

新的带共享解密的多重代理签密方案

针对很多代理签名方案不能实现保密性的问题,基于椭圆曲线密码体制提出了一种新的带共享解密的多重代理签密方案。新方案具有如下优点：（1）同时实现了认证性和保密性。（2）代理签密者和接收者利用基于椭圆曲线双重离散对数证明协议自己计算代理群私钥和接收群私钥,而不需要CA为其分发。（3）代理签密和共享解密的实现分别是一个（t,n）门限和（l,m）门限秘密分享方案。（4）整个通信不需要安全信道,从而降低了通信的代价。     

单纯形搜索在遗传算法中的融合研究

构造了单纯形混合遗传算法SM-HGA⁺。分析单纯形搜索算法,提出了单纯形交叉算子和K步随机单纯形搜索算子,并将单纯形搜索算法和这两个算子分别融入到最优微群体μP_B（t）、最差微群体μP_W（t）和普通群体P_C（t）,形成SM-HGA⁺。最优微群体中的单纯搜索算法提高算法的精度;最差微群体中的单纯形交叉算子加速最差个体向优秀个体进化;普通群体中K步随机单纯性搜索提高全局搜索速度,同时在普通群体采用大交叉概率的标准遗传算法,提高全局搜索能力。遗传算法测试函数验证算法SM-HGA⁺的正确性、效率。     

一种CA密钥的多级安全保护方案

针对CA密钥的高安全性需求,提出一种多级的保护机制。使用RSA算法产生密钥,采用(t,n)秘密共享将其分发到t个签字服务器,用异构平台存储密钥份额,并使用主动式秘密技术周期性更新密钥份额,对密钥份额进行恢复和有效性验证。设计了分阶段签字方案,多级安全保护机制有效地增强了CA密钥的安全性。通过Java和Open SSL对方案进行了实现。     

K均值和中值滤波混合算法及其递归实现

针对标准中值滤波算法边缘保持能力较差的缺点,设计了K均值中值滤波算法和递归K均值中值滤波算法。使用K均值方法将中心像素点的邻域数据序列分成两类,将该像素点所属的这类数据的中值作为输出。为了加快算法的速度,提出了两个阈值T_L和T_H,保证在不影响结果精确性的同时尽量减少处理时间。传统的K均值算法耗时较高,论文的另一个改进是对K均值的优化处理,大大缩短了算法的处理时间。实验证明改进算法具有较好的噪声抑制能力和边缘保持能力。     

基于PDEs的图像平滑数值方法

对基于PDEs的图像平滑技术进行了探讨,在对四阶模型u/t=-▽² [c（|▽²u|）▽²u] 解的分析基础上,给出一种求解该模型的数值方法,数值实验结果给出了良好的去噪效果。     

改进的门限数字签名方案

费如纯、王丽娜等提出了一个安全性基于有限域上求解离散对数的困难性和特定条件下求解二次剩余的困难性的门限数字签名方案。该方案能防止恶意参与者欺诈以阻止签名和伪造攻击。基于数论知识分析指出他们给出的这种实现方案存在不足和安全隐患：t个成员合谋无法假冒其他成员生成有效的数字签名;然后利用Williams体制对上述方案改进,从而解决了以上问题。新方案还具有成员的子密钥可以无限制的使用、验证的简单性等许多良好性质。     

基于RSA的防欺诈多秘密共享方案

针对秘密共享方案进行了分析和研究,指出基于二元单向函数和Shamir（t,n）门限方案的YCH多秘密共享方案无法有效防止欺诈,进而提出了一个基于RSA的防欺诈的多秘密共享方案。该方案在保留了YCH方案的优良特性同时,利用秘密片段和认证片段信息的模余关系来检测欺诈者,具有较强的实用性。     

确定型格值有限自动机的最小化

给出了确定型格值有限自动机的定义,并同时给出了有效终止状态和可达到状态的定义。指出了求取DLFA M=Q,Σ,δ,q₀,σ的实质是求取Q/R_k。由此以可到达状态为基础引入了等价关系R_k、S_k与商集Q/S_k,证明了R_k=R_k-1∩S_k,由此得到Q/R_k的等价类为Q/R_k-1中等价类与Q/S_k中等价类的非空交集全体。引入了H_k,并证明了可由H_k求取Q/S_k,从而得到仅利用集合运算便可求取Q/R_k的算法,最终给出了DLFA最小化算法的一个容易实现的构造型描述和相应示例。     

两类完美的门限可变多秘密共享方案

[t→t,n]门限可变方案研究如何将门限[t]改变为[t>t]以增加攻击者攻击方案的难度。基于拉格朗日插值多项式提出两类完美的门限可变多秘密共享方案：[t→t+1,n]门限可变方案[Π,Π]、[t→t+v-1,n]门限可变方案[Π,Π],并证明[Π]是[t-1,t+1,n]ramp秘密共享方案,[Π]是最优[t-1,t+v-1,n]ramp秘密共享方案,[Π,Π]是最优[t→t+v-1,n]门限可变方案。     

Optimal software release scheduling based on artificial neural networks

The determination of the optimal software release schedule plays an important role in supplying sufficiently reliable software products to actual market or users. In the existing methods, the optimal software release schedule was determined by assuming the stochastic and/or statistical model called software reliability growth model. In this paper, we propose a new method to estimate the optimal software release timing which minimizes the relevant cost criterion via artificial neural networks. Recently, artificial neural networks are actively studied with many practical applications and are applied to assess the software product reliability. First, we interpret the underlying cost minimization problem as a graphical one and show that it can be reduced to a simple time series forecasting problem. Secondly, artificial neural networks are used to estimate the fault-detection time in future. In numerical examples with actual field data, we compare the new method based on the neural networks with existing parametric methods using some software reliability growth models and illustrate its benefit in terms of predictive performance. A comprehensive bibliography on the software release problem is presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

Stochastic optimization for modeling physiological time series: application to the heart rate response to exercise

Stochastic optimization is applied to the problem of optimizing the fit of a model to the time series of raw physiological (heart rate) data. The physiological response to exercise has been recently modeled as a dynamical system. Fitting the model to a set of raw physiological time series data is, however, not a trivial task. For this reason and in order to calculate the optimal values of the parameters of the model, the present study implements the powerful stochastic optimization method ALOPEX IV, an algorithm that has been proven to be fast, effective and easy to implement. The optimal parameters of the model, calculated by the optimization method for the particular athlete, are very important as they characterize the athlete's current condition. The present study applies the ALOPEX IV stochastic optimization to the modeling of a set of heart rate time series data corresponding to different exercises of constant intensity. An analysis of the optimization algorithm, together with an analytic proof of its convergence (in the absence of noise), is also presented.     

Optimal adaptable design for creating the changeable product based on changeable requirements considering the whole product life-cycle

Compared with the traditional design to create multiple products for satisfying different requirements in different life-cycle time periods, adaptable design aims at creating a single product to satisfy these different requirements through adaptation of this product in its different life-cycle time periods. In this research, a method to identify the optimal adaptable product based on changeable requirements is introduced. The changeable requirements are described as functions of the life-cycle time parameter. The adaptable product, modeled by its configurations and parameters, is changed in different life-cycle time periods to satisfy the changeable requirements. An adaptable product is evaluated considering different time periods in its whole life-cycle. Among all possible design candidates, the optimal design is identified through optimization. A case study is conducted to show the effectiveness of the introduced method.     

可变服务率模式下基于需求驱动的传送带给料加工站系统的优化控制

本文主要研究可变服务率模式下基于需求驱动的传送带给料加工站(CSPS)系统的优化控制问题,主要目标是对系统的随机优化控制问题进行建模和提供解决方案.论文以缓冲库和成品库剩余容量为联合状态,以站点前视距离和工件服务率为控制变量,将其最优控制问题描述为半马尔科夫决策过程(SMDP)模型.该模型为利用策略迭代等方法求解系统在平均准则或折扣准则下的最优控制策略提供了理论基础,特别地,据此可引入基于模拟退火思想的Q学习算法等优化方法来寻求近似解,以克服理论求解过程中的维数灾和建模难等困难.仿真结果说明了本文建立的数学模型及给出的优化方法的有效性.     

A new approach based on artificial neural networks for high order multivariate fuzzy time series

Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691–700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468–477].     

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

There are two popular types of forecasting algorithms for fuzzy time series (FTS). One is based on intervals of universal sets of independent variables and the other is based on fuzzy clustering algorithms. Clustering based FTS algorithms are preferred since role and optimal length of intervals are not clearly understood. Therefore data of each variable are individually clustered which requires higher computational time. Fuzzy Logical Relationships (FLRs) are used in existing FTS algorithms to relate input and output data. High number of clusters and FLRs are required to establish precise input/output relations which incur high computational time. This article presents a forecasting algorithm based on fuzzy clustering (CFTS) which clusters vectors of input data instead of clustering data of each variable separately and uses linear combinations of the input variables instead of the FLRs. The cluster centers handle fuzziness and ambiguity of the data and the linear parts allow the algorithm to learn more from the available information. It is shown that CFTS outperforms existing FTS algorithms with considerably lower testing error and running time.     

Optimal Estimation Equations for the State Vector of a Stochastic Bilinear System: Its Bilinear Approximation

A finite-dimensional approximation for the optimal filtrating equations of the class of Markov diffusion processes described by a bilinear stochastic system is derived. The solution of the stochastic system is expressed in terms of the Peano series and its formal algebraic representation. A finite system of equations for the approximate filter is derived as the optimal Stratonovich–Kushner filter for a system with finite Peano series.     

基于模糊综合支持向量机的特高压变电站二次设备状态评估

针对超高压变电站二次设备状态检修科学依据不足的问题,给出了一种基于模糊综合支持向量机的二次设备状态评估模型. 对综合自动化系统上传的在线告警信息和检修报告中的离线信息进行了分析,采用模糊综合分析实现对各种评估因素的有效利用和信息提取,在此基础上运用改进型SVM 对二次设备状态进行评估.在SVM 评估过程中,对三种不同核函数进行了实验分析,选取RBF 作为模型的核函数. 实验结果证明,模糊综合支持向量机评估模型的提高了二次设备状态评估正确率.     

广义离散随机线性系统自校正最优预报器

运用现代时间序列分析[1]的方法研究广义离散随机线性系统最优及自适应状态估计. 将状态估计转化为输出预报和白噪声估计,从而提出了系统的最优预报器,并且证明最优预 报器对于初始值的选取渐近稳定.在噪声统计未知时提出了自校正预报器.仿真例子说明了 其有效性.