ID 原文 译文
48166 本文主要研究基于屏蔽数据下叠加模型中参数的极大似然估计,评估软件系统的可靠性。 This paper mainly studies based on block data under the maximum likelihood estimation of parameters in superposition model, evaluating the reliability of the software system.
48167 最后通过一组模拟数据,说明极大似然估计效果良好。 Finally through a set of simulated data, the maximum likelihood estimation effect is good.
48168 针对小型无人机航迹规划中难以满足自身性能约束和实时性要求的问题,将飞行环境中的威胁分为可穿越威胁和不可穿越威胁;基于序列规划思想,采用粒子群优化算法规划基准航迹,采用改进的稀疏A*算法进行在线航迹规划。 For small unmanned aerial vehicle (uav) path planning is difficult to meet their performance constraints and the problem of real-time requirements, threat of flight environment can be divided into can and impenetrable threats to cross;Based on sequence planning idea, USES the particle swarm optimization algorithm is used to plan reference track, with the improved sparse A * algorithm for on-line path planning.
48169 为提高在线航迹规划效率,将三维航迹规划转化为二维水平面规划和高度规划,仿真实验验证了该算法能够生成三维可行航迹,且规划时间显著减少。 To improve the efficiency of path planning, the three-dimensional path planning into a two-dimensional horizontal planning and height, the simulation experiment shows that this algorithm can generate three-dimensional feasible path, and significantly reduce the planning time.
48170 基于排队论,得出了信息熵形式的防空阵地网对要地保护能力的数学描述,并将防空阵地网对要地保护能力、防空武器系统部署地点地形条件和防线划分等因素作为优化目标, Based on queuing theory and information entropy is obtained in the form of air defense position the mathematical description of reclaiming land protection ability, and the air defense positions for reclaiming land protection ability, the air defense weapon system deployment location factors such as terrain conditions and defense division as the optimization goal,
48171 防空武器系统最小部署间距为约束条件,建立了多型防空武器扇形优化部署多目标优化模型。 air defense weapon system deployment minimum spacing as constraint conditions, set up many type air defense weapon fan optimization deployment of multi-objective optimization model.
48172 针对目前算法在解决高维多目标优化存在的问题,基于改进的强度帕累托进化算法(strength Pareto evolutionary algorithm,SPEA2),提出了将多个目标函数分成若干组,分别寻优,再综合求取全体目标函数非支配集的分组优化算法。 In view of the present algorithm in solving the problems of high-dimensional multi-objective optimization, based on the improved strength Pareto evolutionary algorithm (strength Pareto evolutionary algorithm, SPEA2), puts forward the multiple objective function can be divided into several groups, respectively, optimization, and integrated to calculate all the objective function of dominating sets grouping optimization algorithm.
48173 仿真实验证明,该优化模型能够按现代防空作战特点进行防空武器系统的防线部署,规避不良地形,形成严密的防空覆盖面,且分组优化算法在性能上优于当前高维多目标优化降维算法。 Simulation experiments show that the optimization model can according to the characteristics of modern air defense combat air defense weapon system of defense deployment, avoid bad terrain, forming tight coverage for civil air defense, and grouping optimization algorithm on the performance is better than the current high-dimensional multi-objective optimization dimension reduction algorithm.
48174 针对非合作信号处理中的线性分组码盲识别问题,提出了一种基于有限域傅里叶变换(Galois field Fourier transform,GFFT)的检测识别方法。 For non-cooperative signal processing problem of linear block codes blind identification is proposed based on a finite domain Fourier transform (Galois field Fourier transform, GFFT) testing identification method.
48175 该方法对接收码序列按不同长度进行分段,对分段码字进行有限域上的傅里叶变换并计算其频谱的累积量。 The method of receiving code sequence segmentation, according to different length of segment code word for the Fourier transform on the finite field and calculate the frequency spectrum of cumulant.