大规模网络中基于移动代理的拓扑发现机制

针对基于移动代理的大规模网络拓扑发现,提出了一种基于移动代理洪泛的自启发式深度优先染色生成树算法。将一个大规模网络划分为若干个子网,在各子网内部利用移动代理洪泛策略并行地获取各子网拓扑信息。利用移动代理将每个子网拓扑信息进行汇总获得整个网络的拓扑。算法的复杂度分析和仿真结果表明本算法可在一定程度上减少网络流量和拓扑周期。     

基于抗差最小二乘配置的海底地形生成研究

最小二乘配置算法能同时顾及测深数据的系统性和随机性影响,提高海底地形的生成精度。根据该算法建立了海洋测深多波束数据的函数模型和随机模型,并构建海底地形。为避免测深数据的异常值影响,进一步推导了基于抗差估计的协方差函数求解公式,提出抗差最小二乘配置算法生成海底地形。利用多波束数据生成海底,结果显示抗差最小二乘配置算法能在构建海底地形的同时准确剔除测深异常。将构建的地形与双线性多项式内插生成的海底进行比较,进一步说明了该算法具有较高的海底地形生成精度。

Abstract：

Seabed terrain generating precision can be improved by least-squares collocation algorithm which takes the systematic and stochastic effects in the bathymetry data. The functional model and stochastic model of the algorithm were created by multibeam bathymetry data,and the method of creating seabed terrain by Least-squares Collocation was researched. To avoid the effect of outliers in bathymetry data,covariance function was calculated by robust estimation,and the Robust Least-squares Collocation algorithm for terrain generation was proposed. It was applied to the real bathymetry data set,and the results indicate that the outliers are detected by the algorithm while the seabed terrain is generated. In the remainder,the terrain grids was compared with which created by bilinear polynomial interpolation algorithm,and it is proved that the Robust Least-squares Collocation algorithm can get higher precision of seabed terrain generation while detecting outliers.     

基于纹理传输的烙画风格图像仿真

针对基于图像生成的烙画艺术问题,以纹理合成与传输算法为基础,提出了一种基于纹理传输的烙画风格图像仿真算法。利用YCbCr颜色空间中亮度直方图的相似性匹配,实现了纹理传输过程中误差优化,避免了局部纹理块的拷贝错位,最后通过对传输结果图像与背景图像的加权控制完成图像融合。仿真实验表明,该方法生成的仿真图像接近真实烙画视觉效果。

Abstract：

Aiming at solving the problem with the image-based pyrography generation,a novel style of pyrography painting simulation algorithm was proposed based on texture synthesis and transfer.It is suggested that the brightness histogram matching in YCbCr color space be adopted to optimize errors during the image processing,the texture block copy be reoriented correctly,and ultimately the image fusion be achieved through weighted control.Experimental results show that a very similar-to-real pyrography simulation effect can be achieved through this approach.     

灰色正交化方法在用电量预测中的仿真研究

根据灰色正交化方法和马尔可夫链原理,应用Gauss-Chebyshev正交化思想预测时序数据的总体趋势。预测的精度是时变的,而马尔可夫链原理在处理时变的系统过程时具有较好的优势,选用该方法能更好的解决预测结果的不稳定性。基于此,提出一种用于用电量数据预测的灰色马尔可夫正交化模型,适用于中短期、数据需求量少且数据振幅较大的动态过程预测。最后用提出的方法对江苏省2007年工业用电量进行预测,其结果表明了所提方法的有效性。

Abstract：

The general trend of time series data was predicted with Gauss-Chebyshev orthogonalization theory according to the grey orthogonalization method and the Markov Chain theory.The prediction accuracy is time-varying.However,this approach will better solve the problem of unstable prediction result since Markov chain theory has greater advantages in handling time-varying system process.Based on this,the Markov grey orthogonalization model prediction was proposed for electricity consumption.It is suitable for dynamic process prediction in medium and short term with less data demand and large data fluctuations.Finally,the proposed approach was used to forecast the industrial electricity consumption of Jiangsu Province in 2007,and the results show the effectiveness of this approach.     

电力关键基础设施网络仿真模型研究

电力网络模型是关键基础设施模型中的重要组成部分。分析了现实电力网络的拓扑结构和运行原理,根据复杂网络的基本理论,采用基于Agent的建模方法设计实现了关键基础设施中电力网络模型。通过仿真实验,较好的模拟了电力系统输送电能的动态过程和因偶发故障而造成的大规模停电现象,对复杂电力网络的动力学特性进行了初步探索。

Abstract：

The model of electric power network is a major part of critical infrastructure model. The topology of network structure and the principle of net function were analyzed, according to the theory of complex network, applying the agent-based modeling to design and realize the model of electric power network. Through the simulation experiment, it was demonstrated to be better ways to simulate the dynamic progress of electric transmission and the cascading failure phenomenon caused by incidental trouble. The simulation model offers an effective approach to analysis electric power network’s role in critical infrastructure, and makes a step to exploration for complex electric power network’s dynamic character.     

基于博弈论组网多机动目标协同跟踪技术研究

在卫星多目标跟踪的背景下,针对传感器网络的协同问题,首次建立了完整的基于移动动态联盟的多目标协同跟踪模型,并针对协同过程中的移动动态联盟形成问题,提出了基于博弈论的联盟形成算法。该协同模型在跟踪多目标时利用移动动态联盟来交换并协调节点间的信息,使节点的行为一致,具有分布式和自组织的特点。仿真实验验证了该协同模型应用在传感器网络多目标跟踪中的有效性。

Abstract：

As to the collaborative technique in sensor network,an integrated multi-target collaborative tracking model was constructed based on mobile dynamic coalition under the background of multi-satellite tracking.Then as to the mobile dynamic coalition formation,a coalition formation method was proposed based on game theory.Using mobile dynamic coalition,this collaborative model exchanged and coordinated the information of nodes to accord their action.So it is distributive and self-organizing.Simulation results validate the effectiveness of collaborative model in sensor network tracking multi-target.     

基于模板匹配的人体运动捕捉数据处理方法

在光学式运动捕捉系统中,标记点往往会出现近似或对称的分布结构,这使得在对标记点进行识别时容易引起混淆。基于模板匹配的人体运动捕捉数据处理方法根据人体局部刚性结构把人体模板分割为多个子模板,使用空间仿射变换从运动捕捉数据中搜索与相应子模板误差最小的匹配点集作为最优匹配,并通过标记点跟踪实现对散乱数据的识别,解决了因标记点分布结构近似或对称而引起的混淆。对标记点缺失情况进行了重构。当出现错误匹配时系统能够自动检测并更正,整个过程无需人工干预。实验结果表明该方法能够正确识别标记点,标记点缺失情况重构效果比较理想,能够满足实时处理要求,证明了该方法的有效性。

Abstract：

In optical motion capture system,markers may be easily confused because of the approximate and symmetrical distributing structure.An approach based on template matching was proposed for the processing of human motion data.The human template was segmented to several rigid sub-templates by the local rigid structure.Matching point-set with least error was searched for each sub-template from the human motion capture data by affine transformation,and the scattered data was identified by point tracking.The ambiguity of identification was notability reduced.The missing data is reconstructed and when error matching is encountered the system is able to check and rectify them automatically.Manual intervention isn＇t necessary through the whole process.Experiment results indicate that this approach is able to identify the markers correctly,the reconstruction of the missing data is ideal and the processing is in real time.All these prove that the approach in this article is available.     

无线传感器网络的自适应分簇组网算法研究

针对经典算法LEACH和HEED的不足,提出了一种能自适应分簇组网的优化算法。构建了节点信息权重模型,并借鉴邻节点信息交换的思想,在成簇过程中与探测范围内的节点交换权重信息,自适应完成分布式网络的簇首选举,并根据最小距离原则成簇。理论分析和仿真实验表明,该算法比LEACH和HEED算法选取的簇首及形成的簇结构更加合理,同时更有效地降低与均衡了网络的能耗,提高了传感器网络的生命周期。

Abstract：

A self-adaptive and optimized clustering algorithm was put forward according to the shortage of LEACH and HEED. The Heavy-weight model about nodes＇ messages was created,and the idea that neighbor nodes exchanged messages each other was used for reference. The nodes which were able to communicate with each other exchanged the Heavy-weight during making clusters,elect self-adaptively the cluster head in distributing networks,and made some clusters based on the minimum distance principle. The theoretic analysis and simulation results prove that the elected cluster head and cluster structure are more reasonable,the energy expenditure in networks is less,the longevity of networks is longer by the optimized algorithm compared to LEACH and HEED.     

基于性能预测的遗传强化学习动态调度方法

针对作业车间动态调度问题,在模式驱动调度的框架下,提出遗传强化学习动态调度方法。首先,采用优先规则编码的染色体表达问题的解,将染色体分割成基因模式作为分阶段调度算法的状态模式;其次,设计性能预测变量,构建启发式立即回报函数,引导和加快遗传强化学习算法的搜索进程;再次,设置遗传算子、强化学习及其相关参数以实现搜索过程＂开采＂与＂探索＂之间的平衡;最后,仿真实验结果验证了遗传强化学习调度方法的有效性。

Abstract：

In the framework of pattern driven scheduling,a genetic reinforcement learning （GRL） approach to schedule the job in the dynamical job-shop was proposed.First,the chromosome was coded by preference rules-based representation for the problem.The chromosome was divided into gene schema as state patterns for the multi-phase scheduling system.Secondly,a performance predictive variable to construct instant reward function was designed which was used to guide the learning system to progress rapidly.Thirdly,genetic operators,RL and controlling parameters carried out the search strategy for the balance of ＂exploration＂ and ＂exploitation＂.Finally,the simulation results verify the efficiency of GRL scheduling approach.     

传感器网络中一种能量高效的数据收集算法

为了在无线传感器网络中降低能耗和最大化网络生存期,提出一种能量高效的数据收集算法(energy efficient data gathering algorithm, EEDGA)。该算法利用移动代理模型在网络中进行数据收集。首先,EEDGA根据监测精度的要求控制活动节点的数量;然后,通过求最小支配集得到具体的工作节点;最后,利用蚁群算法规划移动代理迁移的最优路线,移动代理以渐进方式收集活动节点的监测数据。仿真结果表明,与典型算法相比,该算法具有更低的能耗和更长的网络生存期。     

基于System C的星载计算机系统设计

分析了小卫星星载计算机系统的公共需求及其应用的特殊性,给出了典型小卫星星载计算机系统的通用体系结构;提出了基于System C的星载计算机系统的设计方法。通过与传统的星载计算机系统设计方法的对比,提出的基于System C的设计方法可使星载计算机系统的研发周期缩短,设计质量获得较大的提升。同时结合实际工程经验,给出了星载计算机系统软硬件协同设计的基本步骤。     

基于插值恢复多通道信号的单通道测向方法

为克服多通道系统硬件量大、造价高及多通道间存在不一致性时性能恶化等不足,提出一种基于插值恢复多通道信号的单通道接收阵列测向方法。此方法采用一个接收通道,各阵元以时分方式工作,采用插值的方法恢复出各路信号,然后运用阵列信号处理算法进行测向。给出了应用该方法的具体步骤,通过仿真实验说明该方法的有效性。     

一种基于需求特性分类的备件库存管理方法及其实证研究

提出了一种基于分类的备件库存管理方法,该方法先从备件需求预测的角度对备件进行分类,然后对每一类备件的需求进行预测并给出了其适当的库存决策方法.基于分类的预测方法可以解决备件种类过多、部分备件需求的历史数据不足问题并能够为每类备件选择预测精度较高的预测方法;基于分类的库存决策模型适用于在缺少单位备件缺货成本参数条件下的订购点与订购批量决策.以石化企业的阀门为典型背景,论文说明了所提出方法的适用性和优越性.     

Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.     

基于横切关注点恢复的软件逆向工程研究

为解决软件遗产系统中横切特性的理解和进化问题,提出了面向侧面(aspect-oriented)的逆向工程框架模型,并以此为基础给出了一种用例驱动的形式化概念分析方法,对需求分析模型层面上的系统横切特性进行逆向恢复。通过对目标系统执行信息进行概念格分析,该方法能够识别和抽取横切用例模型的系统方法调用集合,并利用统一建模语言中用例图的版型(stereotype)机制对它们进行模型重构和呈现。与仅为了代码重构而进行的侧面挖掘(aspect mining)技术相比,该方法更适合在较高的系统抽象层面上进行程序理解,作为一种采用增量式结果产生策略的半自动化方法,它的实施流程也具有更好的可控性和对逆向工程需求变化的适应性。     

一种基于D-S理论的自评系统ER改进方法

针对质量奖自评系统中评分方法的主观性问题，本文基于D—S理论，提出了一种新的证据推理方法，并证明该方法完全满足Yang等人提出的任何证据合成过程必须满足的四个公理。通过自评实例运算，进一步验证了该方法的有效性和合理性。     

基于均匀场理论的CDMA多用户检测的方法

基于均匀场理论提出了实现码分多址(CDMA)最佳多用户检测的一种神经网络方法。从理论上证明了该算法的稳定性,理论分析和计算机仿真表明该算法能有效地抑制噪声干扰、能有效地克服远近效应,且能实时求解,有利于VLSI实现。     

基于快速逆QR分解的自适应波束形成方法

在实时自适应阵处理中 ,基于逆QR分解 (IQRD)的方法在求自适应权值时由于避免了三角方程回代运算而受到了很大的重视。但是和基本的QR分解 (QRD)一样 ,IQRD方法也存在着平方根运算。平方根的运算量相当大 ,因此成为并行实时处理的速度瓶颈。提出了一种快速的IQRD方法 ,该方法有效地消除了平方根运算 ,从而使基于IQRD的自适应波束形成法便于实时实现 ,更适合于实际应用。     

基于证券价格时间序列的协整优化指数跟踪方法研究

通过考虑不允许卖空约束和目标指数进行调整的现实情况,研究了基于证券价格时间序列的协整优化指数跟踪方法对目标指数进行直接跟踪的效果以及用简单平均法引入的非样本信息在指数跟踪中的作用.实证结果表明,相比于最小化跟踪误差优化指数跟踪方法,协整优化指数跟踪方法是一种非常好的指数跟踪方法,更多的信息可以进一步改进指数跟踪效果.     

An approach towards holism in science and engineering

This paper posits the desirability of a shift towards a holistic approach over reductionist approaches in the understanding of complex phenomena encountered in science and engineering. An argument based on set theory is used to analyze three examples that illustrate the shortcomings of the reductionist approach. Using these cases as motivational points, a holistic approach to understand complex phenomena is proposed, whereby the human brain acts as a template to do so. Recognizing the need to maintain the transparency of the analysis provided by reductionism, a promising computational approach is offered by which the brain is used as a template for understanding complex phenomena. Some of the details of implementing this approach are also addressed.