基于自适应径向基网络的舰船RCS统计特征识别方法

文章采用舰船RCS频域起伏序列的均值、标准差为识别特征向量,利用提出的基于样本密度的自适应径向基网络,进行舰船分类识别研究。自适应径向基网络采用改进的自适应PSO方法估计样本密度最优邻域半径,实现径向基网络中心的自适应选择。改进的自适应PSO方法采用能反映样本聚类特点的BWP指标为适应度评价函数,采用快慢结合的高斯自适应惯性权重调节策略,提高了最优样本密度邻域半径的搜索速度和精度。实验结果表明,自适应径向基网络能自适应获得径向基网络最优识别率对应的RBF中心及其位置分布,减少了对建模人员经验的依赖,提高了反舰导弹对舰船类型的识别分类能力。     

可拓模式识别算法中经典域的确定方法

可拓模式识别算法是根据各个关联度的对比来识别最终的模式,其中经典域区间的确定有着重要的作用。传统的经典域确定方法是基于数理统计学的,不适用于小样本数据,数据不完全以及数据分布未知等复杂情况。引入Bootstrap算法,并和传统的方法对比,更精确地确定了经典域区间。     

Plastic wrinkling prediction in thin-walled part forming process:A review

The precision forming of thin-walled components has been urgently needed in aviation and aerospace field. However, the wrinkling induced by the compressive instability is one of the major defects in thin-walled part forming. The initiation and growth of the wrinkles are interac-tively affected by many factors such as stress states, mechanical properties of the material, geometry of the workpiece and boundary conditions. Especially when the forming process involves compli-cated boundary conditions such as multi-dies constrains, the perturbation of clearances between workpiece and dies and the contact conditions changing in time and space, etc., the predication of the wrinkling is further complicated. In this paper, the current prediction methods were summa-rized including the static equilibrium method, the energy method, the initial imperfection method, the eigenvalue buckling analysis method, the static-implicit finite element method and the dynamic-explicit finite element method. Then, a systematical comparison and summary of these methods in terms of their advantages and limitations are presented. By using a combination of explicit FE method, initial imperfection and energy conservation, a hybrid method is recommended to predict plastic wrinkling in thin-walled part forming. Finally, considering the urgent requirements of com-plex thin-walled structures’ part in aviation and aerospace field, the trends and challenges in wrin-kling prediction under complicated boundary conditions are presented.     

DME Interference mitigation for L-DACS1 based on system identification and sparse representation

L-band digital aeronautical communication system 1 (L-DACS1) is a promising candi-date data-link for future air-ground communication, but it is severely interfered by the pulse pairs (PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection (DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs (DPPs) which are caused by mul-tiple filters in the receiver. Secondly, a finite impulse response (FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be sub-tracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.     

不同迎角下脊形前体绕流数值模拟研究

脊形前体有较强的背风涡流场,不同的前体形状对前体涡流场和气动力有很大的影响。本文针对脊形前体飞行器大迎角湍流大分离流动计算的困难,采用IDDES混合湍流模型,以及与之匹配的非定常算法,研究了不同来流迎角下脊形前体的气动特性,以及背风涡非定常演化、破裂的细致流动结构。选取了不同脊形角,以及不同上、下高宽比的脊形前体进行计算。计算结果表明,在迎角较小时,随着迎角的增大,前体主涡会逐渐增强,在迎角较大时,前体主涡破裂;在相同迎角下,脊形角较小时,前体涡较强,涡升力也更大;对于相同脊形角的前体,当上半截面高宽比较小时,前体主涡强度较大,前体涡破裂临界迎角较小,即会提前破裂。     

存在干扰下的无人机中继站波束成形算法

无人机(UAV)通信具有部署快、使用灵活、通信质量好等优点,在无线通信领域具有广阔的应用前景。采用UAV作为中继站构成的增强型卫星通信网络经常受到各种干扰的影响,导致其性能急剧下降。以信干噪比(SINR)最大化为准则,UAV中继站发射功率满足要求为约束条件的优化问题的基础上,提出了一种基于最大瑞利熵的波束成形(BF)算法,得出最优权矢量。为了降低BF算法的实现复杂度,进一步提出了一种基于迫零(ZF)的次优BF算法。计算机仿真验证了这2种BF算法的有效性。     

基于离散粒子群算法的航空发动机总装工艺优化方法

为了提高航空发动机总装工作的效率与质量,加强对总装工序和总装机件的管控,量化不同总装人员工作任务量,提高发动机总装的一致性和可追溯性,提出了1种发动机总装分区优化方法。以改进的2进制粒子群算法为基础,平衡了发动机总装各区域的工作内容,最小化各总装区域的工作差异,增强了每个区域装配机件的关联度,提高了总装工作的并行度,从而提高了装配质量。以某型发动机总装区域划分工作为实例,通过上述算法进行了总装区域划分,量化了所有装配区域每一工序的具体装配内容,减小了不同装配区域间的机件差异,杜绝了缺件、剩件等装配问题。     

面向一体化计算的整体式液体冲压发动机建模及性能评估

为发展冲压发动机性能工程预估方法,建立了面向一体化计算的整体式液体冲压发动机性能计算模型,并使其兼容基团贡献算法,提高了拓展性。利用模型分析了冲压发动机在攻角0°～6°、高度0～18km、马赫数2.0～3.5、余气系数1.0～2.9范围工况多维度连续变化下,比冲、推力系数和燃油质量流量的速度-高度特性、高度-节流特性、节流-速度特性以及攻角特性。研究结果表明:性能计算模型可在一体化计算条件下、基团贡献算法允许范围内,不依赖试验数据对冲压发动机性能预估,计算结果与技术参考值相比相对误差均小于14%。推力系数和比冲具有基本一致的速度-高度特性和相似的高度-节流特性,受燃气组分影响,推力系数和比冲的节流-速度特性差异明显。燃油质量流量的变化规律不同于比冲和推力系数,在进入平流层后呈现折缓趋势,而呈现连续性,速度越大、高度越低、余气系数越小,燃油质量流量越高,反之则越小。引入攻角以后,攻角越大,比冲和推力系数越低,进气道起动马赫数越高;攻角-起动马赫数曲线小范围内近似线性,攻角超过5.6°非线性加剧。     

风扇/压气机周向模态识别中传感器角度偏差的影响

基于任意角度压缩感知(CS)方法分析了传感器安装角度偏差对风扇/压气机周向模态识别重构的影响,设计了一套自适应角度优化程序修正重构误差。利用数值试验探究了传感器角度偏差和数量对周向模态重构结果的影响,研究表明:当角度偏差等级为2.5%时,平均重构误差达到10%以上,若保证重构误差基本不变,将传感器数量从7个增加至25个,仅可以将角度偏差等级放宽至4%。而采用小生境微种群遗传算法进行自适应角度优化,在20 dB信噪比下,通过自适应角度优化可将角度偏差等级从2.5%放宽至10%,降低了传感器安装的精度要求。成功优化了一款冷却风扇在前三阶叶片通过频率下的主要周向声模态重构幅值。自适应角度优化算法有效提升了基于压缩感知的风扇/压气机周向模态重构可靠性。     

数字水印防伪技术的研究和应用

本文采用数字水印技术,研究和开发了一种新的证件制作与检测方法技术体系,防伪性能高、识别准确快速。该项技术可广泛应用于证件和产品的防伪,具有极高的理论价值和实用价值。