使用三个数域的数域筛算法

大整数分解难题是RSA密码的数学安全基础.目前数域筛算法是分解365比特以上大整数的最有效方法,然而它的时间复杂度仍然是亚指数的.对于目前普遍使用的1024比特以上大整数,数域筛算法还不能分解,所以研究数域筛算法具有重要的意义.现有的一般数域筛算法普遍使用两个数域,对多个数域的研究极少.一般数域筛算法经过修改可以使用三个数域,即两个代数数域和一个有理数域.分析表明:修改后的数域筛算法与原来的一般数域筛算法在时间复杂度上处于同一量级.但修改后的数域筛算法有更多地方可以合并计算,所以计算速度更快了.通过两个实验也验证了这一结论.     

自适应LMS和相关算法在GMI磁传感器信号检测中的应用

在GMI磁传感器的研制中,微弱磁场信号经常淹没于电路固有噪声中。当非晶丝GMI(GiantMagneto-Impedance)磁传感器的输出信噪比小于0dB时,常规的峰值检波方法无法检出传感器信号。针对此问题,提出了一种新的微弱信号(信噪比小于0dB)检测方法,利用LMS自适应滤波算法提取非晶丝GMI磁传感器输出信号的特征参数,将该特征参数与理想参数进行相关运算,并根据相关值的大小来确定信号大小,从而实现对微弱磁信号的测量。仿真结果表明,采用LMS自适应滤波算法和相关分析相结合的方法,对于传感器输出信噪比等于-10dB的微弱信号,根据相关值与外磁场大小的对应关系,仍然可以确定外磁场大小。     

高超声速流动计算中LU-SGS隐式算法的应用研究

在高超声速条件下,对原始LU-SGS格式及其改进方法的收敛速度做了深入地比较分析,目的是进一步更好地将LU-SGS算法用于工程上复杂外形的计算模拟当中。二维圆柱,三维钝锥及空天飞机算例的结果表明：(i)对于高超声速粘性流动的计算,粘性项应进行隐式处理;(ii)BLU-SGS方法给出的内迭代方式的收敛性优于DP-LUR方法所给出的内迭代方式;(iii)LU-SGS算法中雅克比系数矩阵的计算方式对计算量及收敛性影响较大,若采用精确的矩阵形式则在流动无分离情况下能取得快速收敛的效果,而在含有流动分离的情况因受稳定性的影响精确的矩阵形式的收敛表现不及对角近似形式。     

基于NFxPEM算法的调制气流声源的非线性补偿研究

针对调制气流声源存在较强的谐波畸变,将声源系统等效为Hammerstein非线性模型,利用该模型下的预失真技术对声源进行非线性补偿研究。根据辨识的Hammerstein模型中静态非线性部分带有直流分量的特点,给出了考虑直流分量补偿的预失真算法,并用数值仿真验证了算法的准确性和直流分量补偿的必要性。在非线性补偿实验中,根据单频信号辨识得到Hammerstein模型参数,采用NFxPEM算法求得对应的预失真Wiener模型参数和预失真波形。实验结果表明,与直接发射相比,补偿发射后声波的功率谱中谐波能量有所下降,而基频能量有小幅度的上升,说明了研究思路的正确性。     

舰船水消防系统战斗损伤的智能重构决策与应用

针对当前舰船水消防系统监控点信息孤立、智能化决策程度不高的问题,构建了基于节点的水消防系统功能逻辑模型,提出了基于战斗损伤的水消防系统破损隔离重构使用智能决策算法,并据此开发了水消防智能监控系统。案例对比计算和系统测试检验的结果表明,该智能决策算法以及智能监控系统可以有效提升分析决策的速度和精度,显著提高指挥控制的效率,为水消防系统的战斗破损使用提供有效的支撑。     

受互联网思路启发的电力系统重要节点评估算法

对电力系统中重要节点进行有效区分,有助于在资源有限的条件下对重要节点施加额外保护或改变拓扑结构,从而提高系统鲁棒性、降低事故发生的概率。受网页排序算法启发,提出电气链接结构分析的随机方法(electrical stochastic approach for link structure analysis, E-SALSA)用于电力系统重要节点评估。该算法综合考虑了电力系统拓扑结构、潮流等因素对节点的影响,能够有效反映电力系统的真实情况,并且其特点更符合电力系统背景。在IEEE300节点电力系统中,使用失负荷规模和最大子群规模两个指标对E-SALSA算法与电气介数算法、基于共同引用的超链接引导的主题搜索(model based on co-citation hypertext induced topic search, MBCC-HITS)算法进行了对比分析。结果证明E-SALSA算法相比电气介数算法在两个指标上都具有优势,相比MBCC-HITS算法能够更综合考虑各方面因素对节点的影响,进而证明了E-SALSA算法的合理性、有效性。     

Two-agent scheduling with linear resource-dependent processing times

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless ; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.     

无源RFID系统中推断式二进制搜索算法

在现有的RFID标准中,一般标签ID都由几个不同含义区间组成。针对这种特点,提出推断式二进制防碰撞算法：在识别每个区间段的ID时,利用某个特定读写器中的前缀库推断该区间段各个位的取值,以便减少待识别标签ID的位数。同时,通过调整参数u,可以对系统可靠性进行控制。仿真实验表明,在没有新前缀出现的情况下,推断式二进制防碰撞算法能够将QT算法的识别速度提高3倍、标签平均响应次数降低3／4。     

基于自适应遗传算法的配送型多级库存优化研究

研究了由1个战役仓库、多个战术仓库组成的配送型系统的优化问题,建立了战术仓库使用经济订货批量策略和战役仓库采用4种不同订货策略时的模型,并采用自适应遗传算法实现了模型的求解,实例验证了模型及算法的有效性。     

A self‐adapting genetic algorithm for project scheduling under resource constraints

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029