Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm (FPSOGSA) and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles. The proposed method is used to calculate the control profiles to achieve the two objectives, namely a smoother trajectory and enforcement of the path constraints with terminal accuracy. The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide (QEG) scheme. The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme. The maximum heating rate path constraint is enforced through the angle of attack parameterization. The Common Aero Vehicle (CAV) flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization (PSO) method and the standard Gravitational Search Algorithm (GSA). The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.     

一种颤振分析新方法

本文提出一种颤振分析新方法－正交搜索法，其本思想是依据解析函数的保角映，沿复变量实部和虚部交替搜索，逐步达到所求方程的根。完全非定常气动力由格林九方法直接计算，颤振分析结果准确。该算法可在计算机上执行，有一定推广价值。     

Evaluation method for helicopter maritime search and rescue response plan with uncertainty

Helicopter plays an increasingly significant role in Maritime Search and Rescue (MSAR), and it will perform MSAR mission based on response plans when an accident occurs. Thus the rationality of response plan determines the success of MSAR mission to a large extent. However, with the impact of many uncertainty factors, it is difficult to evaluate response plans comprehensively before performing them. Aiming at these problems, an evaluation framework of helicopter MSAR response plan named UMAD is proposed in this paper, which reveals the influence mechanism of uncertainty factors based on Multi-Agent method and analyzes the mission flow based on Discrete Event System (DEVS) method. Furthermore, the evaluation criterion and indicators of response plan are extracted from the aspects of safety and effectiveness. Meanwhile, the Monte Carlo method is adapted to calculate the probability distribution and robustness of response plan comprehensive result. Finally, in order to illustrate the validity of this method, it is discussed and verified by an application example of evaluating multiple response plans to the same MSAR scenario. The results show that this method can analyze the influence of uncertainty more systematically and optimize response plans more comprehensively.     

基于眼动数据的网络搜索行为预测方法

预测用户的网络搜索行为对改进搜索引擎和提升用户体验十分重要.现有大多数方法是基于用户的交互数据,如查询、点击和鼠标移动等.提出一种基于眼动数据的用户网络搜索行为预测方法.通过眼动实验,采集用户在网络搜索任务时的眼睛运动数据,将这些数据转化成两种数据格式:直方图和序列.直方图数据描述用户注意力的分布情况,序列数据呈现用户的扫视路径.使用4种学习算法对用户决策或用户意图进行预测,同时研究两种数据格式的性能.结果显示,两种数据格式均适合于预测用户决策,而序列数据更适合于预测用户意图.该结果表明,利用眼动数据能够有效预测网络搜索行为.      

空投鱼雷蛇形搜索攻击有利提前角模型研究

提出了一种用等效扇面方法对蛇形搜索航行鱼雷攻击有利提前角的解算方法,并通过仿真对模型进行了修正。仿真结果表明,经修正后的鱼雷可达到最大命中概率。     

面向空中目标威胁评估的多传感器管理方法

为了降低在空中目标威胁评估任务中由于威胁评估结果的不准确性和传感器辐射所带来的潜在损失,提出了一种基于风险的多传感器管理方法。首先,基于部分可观马尔可夫决策过程建立了传感器管理模型;然后,给出了基于信息状态的威胁评估风险和传感器辐射风险的预测方法以量化潜在损失;接着,为获得更优的作战收益,以多步风险预测值为决策依据,以两种风险的加权和最小为优化目标建立了长期目标函数;最后,在求解目标函数时,将传感器管理问题转化为决策树搜索,设计了一种基于分支定界的标准代价搜索算法以快速获得高质量的管理方案。仿真实验表明,所提算法能够在搜索到高质量解的同时大幅减少计算时间和内存消耗;所提方法能够对风险进行准确预测,且相比于经典的传感器管理方法,所提方法具有更好的风险控制效果。     

自组织时分多址甚高频数据链的延时分析

时隙预约方法对自组织时分多址系统的延时性能有很大影响.针对动态时隙分配方案中典型的回溯搜索算法,分析了时隙空闲概率及预约冲突概率对报文延时的影响,并采用概率周期分析方法,通过引入时隙处于空闲和忙状态的概率,详细分析了报文延时与用户数和发报率的关系.最后,提出了一种新的时隙选择算法——序贯搜索算法.仿真结果表明,该算法的延时性能比回溯搜索算法优越.      

Risk Theta*:一种基于地形危险度的任意航向路径规划算法

提出了一种基于地形危险度的任意航向路径规划算法——Risk Theta*。首先以星球表面地形特征统计分析为基础提出了地形危险度指标,并建立地形危险度地图。在此基础上应用Basic Theta*搜索,以危险度最低为方向搜索最优路径。仿真实验证明,该算法能够在栅格地图上找到比A*和Basic Theta*算法危险度低得多、长度相当的任意航向路径,既显著提高了巡视器的安全性,又满足了星球巡视探测对任意航向行驶的迫切需求,因此具有较强的实用性。     

全局最优导向模糊布谷鸟搜索算法及应用

针对标准布谷鸟搜索算法探索能力强而开发能力较弱、收敛速度慢及计算精度较差等问题,提出了具有全局最优导向的模糊布谷鸟搜索算法。在鸟窝更新公式中引入全局最优导向策略,在产生新的鸟窝位置时利用到当前最优鸟窝位置信息,以保持鸟窝的多样性并提高算法的开发能力。另外,采用模糊逻辑规则对布谷鸟算法中的搜索步长和外来鸟蛋被发现概率这2个重要参数进行自适应调整,以提高算法的全局收敛性能和求解精度。通过2个经典结构可靠性分析极限状态方程测试该算法的性能,并将其应用于某飞机舱门锁定机构可靠性分析中。实验结果表明,与粒子群算法、标准布谷鸟搜索算法和改进布谷鸟搜索算法相比,所提出的全局最优导向模糊布谷鸟搜索算法在进行可靠性分析中,能够有效地提高解的精度并增加收敛速度,寻优效果更优。