一种同步时序PLD逆向分析数据采集算法

采用逻辑分析法实现加密可编程逻辑器件(PLD)逆向分析的关键是为逻辑综合提供有效、完备的数据集,特别是对时序型PLD,在未知状态图的情况下,如何高效地采集到所有有效状态下的数据,是逆向分析研究的核心问题之一。该文在理论分析同步时序型PLD逆向分析可行性的基础上,提出一种适合多状态、复杂同步时序型PLD的高效数据采集算法,以动态建立非完全状态图为基础,求解状态驱动的最短路径,使得数据采集算法具有理想的时空开销。     

基于GPS/GIS的交通状态自动判别系统研究

针对现有的智能交通监控终端系统功能的不足,采用安装带有GPS定位装置的浮动车系统进行了浮动车样本数量确定方法的研究和道路交通状态判别算法的研究与设计,建立了城市道路交通状态自动判别仿真系统,根据道路交通状态判别算法,在ArcGIS地图平台上建立的城市交通仿真系统进行了实时道路交通状态自动判别功能的设计和仿真,并根据道路交通状态,对动态路径选择进行了设计和仿真.     

时序PLD安全缺陷检测方法研究

可编程逻辑器件(PLD)在电子设备中广泛应用,其安全缺陷检测已成为信息安全领域中一个富有挑战性的课题。通过分析PLD安全缺陷的存在形式,提出了基于状态转移图的安全缺陷检测方法。该方法统一了检测思路,采用了脱机式芯片逆向分析和在线式芯片逆向分析相结合的技术,适用于不同的PLD安全缺陷检测,同时根据存在形式提出了检测算法。最后通过模拟测试对该检测思路及算法的有效性进行了验证。     

弱连接边缘独立判别社交网络社区快速生成树推荐

针对传统社交网络社区推荐算法精度不高且计算复杂度过高的问题,提出一种弱连接边缘独立判别社交网络社区快速生成树检测算法,在提高社区推荐精度同时,降低算法计算复杂度。首先,结合社交网络社区推荐特点,设计基于边缘重量分配节点相似性的最大生成树算法,实现对社交网络社区的有效检测;其次,针对所提算法,存在弱连接边缘重复添加、删除,浪费计算资源的问题,提出弱连接边缘独立判别的快速生成树检测算法,进一步提高算法计算效率;最后,通过在标准测试数据库中的实验对比,验证了所提算法的有效性。     

Lyapunov指数在弱2FSK信号检测中的应用

针对现有基于Duffing振子的2FSK信号检测方法在混沌临界阈值确定与相图判别两方面存在精度低、效率低的问题,提出了利用Lyapunov指数法确定阈值,并定量分析和判别检测系统输出状态的方法。在分析Duffing系统中Lyapunov指数算法的基础上,进行了仿真实验与分析,结果表明基于Lyapunov指数的2FSK信号混沌振子检测方法提高了阈值设置的精度,保证了2FSK信号检测的可靠性。     

分布式控制系统中控制卡的双机热备实现

在分布式控制系统（DCS）中,为提高核心部件控制站中控制卡工作的可靠性,提出一种实现两个控制卡双机热备工作的方法。该方法采用主从式双机热备,涉及双机热备中的上电竞争、状态检测、故障切换以及数据同步操作。采用硬连接方式利用RS触发器与D触发器实现双机间的上电竞争与状态检测,以控制器局域网CAN的组播方式实现双机的输入数据同步。在提高系统可靠性的同时,简化电路设计,降低系统复杂度和硬件成本。详述了该方法实现的硬件电路和软件算法,给出利用PLD器件实现上电竞争和状态检测电路的方法,并分析了采用文中的状态检测算法进行故障检测所需要的时间。     

一种恢复状态模式的状态转换图的方法

现有研究不能从状态模式的代码中恢复状态转换图这一状态模式的核心语义信息。针对状态驱动的转换这种典型实现方式,通过分析类型传播图确定状态模式的状态变量在各次赋值前后所属的具体状态类,并由此恢复该模式的状态转换图。给出了具体算法,通过实例分析验证了方法的可行性。     

一种基于属性的异常点检测算法

异常数据检测是数据挖掘研究的热点之一。本文在对现有异常点检测算法分析的基础上,提出了一种基于属性的异常点检测算法。简要地介绍了异常检测的现状,对基于属性的异常检测算法进行了详细分析,包括算法设计基础、算法描述、复杂度分析等。并通过与基于距离的异常点检测算法进行实验比较,表明了算法的优越性。     

一种改进的可视化布局算法IGVA

可编程逻辑器件（PLD）存在安全缺陷,且危害性极大,将可视化技术作为PLD安全缺陷检测的辅助手段,其状态转移图布局是关键。针对状态转移图布局中存在的节点重叠、分布不均等不足,提出一种改进的可视化布局算法IGVA。该算法分阶段启发式计算引力和斥力,迭代早期通过减小节点问的引力以避免节点重叠,迭代后期通过减小边的斥力优化节点分布并减少图占用的空间。实验结果表明,IGVA可解决节点重叠的问题,达到图的布局要求。     

混沌振子弱信号能量检测方法

提出了一种Duffing弱信号能量检测方法.通过能量算法对不同状态下Duffing振子的频率分布特性进行分析,得到了一种基于瞬时频率的Duffing振子状态判别方法,并进一步给出了相变判别阈值的设定准则,且对检测算法的抗噪性和实时性进行了分析.仿真实验表明,该方法的计算量和实时性比传统相变判别算法有明显的改善,可满足强噪声背景下微弱信号的快速检测要求.     

Identification of cascaded systems with linear and quantized observations

This paper studies identification of systems that can be decomposed into cascaded subsystems. The benefits of using additional sensors for identifying subsystems are investigated in terms of identification accuracy and time complexity. Identification algorithms, input design, and time complexity are first developed for subsystems, under various sensor types and locations. Overall reduction in estimation errors and time complexity is then analyzed to understand optimal selection of sensor locations and impact of sensor types on identification accuracy and time complexity. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

应用层协议识别算法综述

能够标识出Internet上每个流所使用的应用层协议是一系列网络应用的前提和基础。然而随着网络的高速化和协议的复杂化,传统的基于端口识别应用层协议的算法已经不够准确,因此各种新的协议识别算法成为研究热点。本文介绍了协议识别问题的几个基本概念,将目前正在使用或研究的协议识别算法总结为三类并分析了各自的优缺点及关键技术和难点,最后指出了两个进一步研究的方向。     

三种高效快速的量子态滤波和估计优化算法

针对不同干扰和噪声情况下的量子状态估计和滤波问题,分别提出相应的高效量子状态密度矩阵重构凸优化算法.对于稀疏状态干扰和测量噪声同时存在的情况,提出量子状态滤波算法.对分别存在稀疏状态干扰和测量噪声的情况,提出相应两种不同的量子状态估计算法.在5量子位的状态密度矩阵估计仿真实验中分析不同采样率下的3种算法性能.实验表明,3种算法均具有较低的计算复杂度、较快的收敛速度和较低的估计误差.     

Dual-mode predictive control algorithm for constrained Hammerstein systems

In the process industry, there exist many systems which can be approximated by a Hammerstein model. Moreover, these systems are usually subjected to input magnitude constraints. In this paper, a multi-channel identification algorithm (MCIA) is proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with a singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of coloured output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, this MCIA is converted into a two stage single-channel identification algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode non-linear model predictive control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the non-linear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the traditional algorithms, the present method has a maximal stable region, a better steady-state performance and a lower computational complexity. Finally, simulation results on a heat exchanger are presented to show the efficiency of both the identification and the control algorithms.     

基于随机聚类采样算法的复杂网络社团探测

根据网络节点的局部拓扑信息构建稀疏相似网络。基于稀疏相似网络, 提出了一种改进后的随机聚类采样算法对网络社团进行探测。在人工和真实网络上, 将算法与未改进的随机聚类采样算法以及几种典型的社团探测算法进行了准确率和时间复杂度的比较。实验结果表明, 该方法在时间复杂度上具有明显的优势, 并且具有较好的准确率。     

Distributed averaging on digital erasure networks

Iterative distributed algorithms are studied for computing arithmetic averages over networks of agents connected through memoryless broadcast erasure channels. These algorithms do not require the agents to have any knowledge about the global network structure or size. Almost sure convergence to state agreement is proved, and the communication and computational complexities of the algorithms are analyzed. Both the number of transmissions and the number of computations performed by each agent of the network are shown to grow not faster than poly-logarithmically in the desired precision. The impact of the graph topology on the algorithms’ performance is analyzed as well. Moreover, it is shown how, in the presence of noiseless communication feedback, one can modify the algorithms, significantly improving their performance versus complexity trade-off.     

移动应用特征提取算法研究

随着移动互联网的普及与发展,新的应用不断出现,移动互联网的网络安全和流量管理变得越来越重要,其应用监管与流量控制以移动应用识别为基础.深度包检测技术是准确性最高的一种应用层协议识别方法,其需要有准确的应用协议特征,应用特征产生的效率和准确度决定了应用识别系统的优劣.因此,高效、准确的自动提取应用特征具有十分重要的意义.文章从算法复杂性及敏感性两个方面,对当前主要特征提取算法进行比较和分析;并通过进行实验研究,分别给出了不同算法的性能仿真结果,为基于载荷的移动应用特征提取算法的选取提供了一个参考,具有一定的研究和应用指导价值.     

系统辨识算法的复杂性、收敛性及计算效率研究

实践中经常会遇到大型计算问题和优化问题, 使得求解问题算法的复杂性、计算量和计算精度等成为突出问题, 特别是大规模非线性多变量系统的辨识. 对此, 提出几个有趣的研究课题: 1) 利用信息滤波技术和多新息辨识理论研究能提高辨识精度的大规模系统辨识理论与方法; 2) 利用递阶辨识原理研究维数高、变量数目多、计算量小的多变量系统递阶辨识方法; 3) 利用鞅收敛理论建立非线性多变量系统辨识方法的收敛理论; 4) 利用并行计算与递阶计算技术提高辨识算法的计算效率, 以解决一类大规模非线性多变量系统的模型化问题.

     

Defining Asymptotic Parallel Time Complexity of Data-dependent Algorithms

The scientific research community has reached a stage of maturity where its strong need for high-performance computing has diffused into also everyday life of engineering and industry algorithms. In efforts to satisfy this need, parallel computers provide an efficient and economical way to solve large-scale and/or time-constrained problems. As a consequence, the end-users of these systems have a vested interest in defining the asymptotic time complexity of parallel algorithms to predict their performance on a particular parallel computer. The asymptotic parallel time complexity of data-dependent algorithms depends on the number of processors, data size, and other parameters. Discovering the main other parameters is a challenging problem and the clue in obtaining a good estimate of performance order. Great examples of these types of applications are sorting algorithms, searching algorithms and solvers of the traveling salesman problem (TSP). This article encompasses all the knowledge discovery aspects to the problem of defining the asymptotic parallel time complexity of data-dependent algorithms. The knowledge discovery methodology begins by designing a considerable number of experiments and measuring their execution times. Then, an interactive and iterative process explores data in search of patterns and/or relationships detecting some parameters that affect performance. Knowing the key parameters which characterise time complexity, it becomes possible to hypothesise to restart the process and to produce a subsequent improved time complexity model. Finally, the methodology predicts the performance order for new data sets on a particular parallel computer by replacing a numerical identification. As a case of study, a global pruning traveling salesman problem implementation (GP-TSP) has been chosen to analyze the influence of indeterminism in performance prediction of data-dependent parallel algorithms, and also to show the usefulness of the defined knowledge discovery methodology. The subsequent hypotheses generated to define the asymptotic parallel time complexity of the TSP were corroborated one by one. The experimental results confirm the expected capability of the proposed methodology; the predictions of performance time order were rather good comparing with real execution time (in the order of 85%).