Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

Stall flutter prediction based on multi-layer GRU neural network

The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model (ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit (GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation (LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.     

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T) has changed the way that network services are developed, deployed, and operated. Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and t...     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

深空网伪码测距技术研究

介绍了NASA深空网所采用的伪码测距的基本原理,讨论了深空航天器应答机相干转发或再生所采用伪码序列的两种形式,对再生测距系统中的具体序列产生和相关、以及用中国剩余定理解算距离的实现方法和技术途径进行了分析,并计算分析了测距精度、捕获时间等系统关键指标,以对我国深空网的建设提供技术理论参考.     

LDPC编码系统的码辅助载波相位同步算法

深空通信具有传输距离远、信号衰减严重的特点,针对低信噪比条件下深空通信系统中LDPC编码系统载波同步实现困难的问题,提出一种新的具有较低复杂度的码辅助载波同步算法。分析了载波同步误差对译码性能的影响,基于最大似然准则推导了载波相位的迭代同步算法,算法利用LDPC译码器输出的软判决信息来辅助载波相位的估计,将译码器与载波同步器进行联合迭代,从而得到接近最大似然估计性能的载波相位估计,同时对载波相位进行迭代补偿。仿真结果表明,在低信噪比条件下,算法能够有效纠正载波偏差,并能够以较低的系统复杂度获得近似理想同步的LDPC码译码性能。     

深空探测器被动式高精度多普勒测量方法与应用

针对深空探测器的单程多普勒测量需求,研究了被动式高精度多普勒测量方法及其实现技术。该方法基于探测器测控信标残留载波等点频信号和VLBI测站高精度氢原子钟频标,构造出与实际接收信号频率接近的参考信号;再通过本地相关处理,完成高精度开环多普勒测量。其特色在于完全不需要精确的先验轨道模型。所开发的专用被动式多普勒测量设备,在国内第一次成功用于欧空局环火星探测器“MEX”的多普勒测定轨试验。探测器X频段信号5s积分的单程多普勒测量精度达到 0.2mm/s ,与欧空局测量水平相当。采用该多普勒测量数据的MEX定轨结果与欧空局精密轨道在数百米至千米量级一致。     

深空通信中高性能低复杂度的QC-LDPC码构造方法

LDPC码字具有优异的性能, 在空间通信中得到广泛应用. 为进一步降低LDPC码构造及编码的复杂度, 给出了一种高性能、低复杂度的QC-LDPC码构造方法. 设计了扩展近似下三角阵(extern Approximate Lower Triangular, eALT)结构的全局矩阵, 通过增加双对角阵结构全局矩阵的列重, 降低差错平底(error floor). 为降低传统循环移位系数选择的复杂度, 提出了一种基于Zig-Zag的移位系数设计方法, 采用数学公式计算循环移位系数, 无需计算机搜索即可完全消除长度为4的短环. 给出了所构造码字线性编码的实现过程. 仿真结果表明, 所提构造方法在保证线性编码复杂度的前提下, 增大了码字间最小距离, 降低了差错平底, 提高了码字性能; 采用结构化的方法设计循环移位系数, 无需计算机搜索即可消除4环, 所构造的码字与CCSDS标准中的码字在性能相近的情况下, 降低了实现的复杂度.      

多径干扰下GPS/INS跟踪环路的一种高斯码相位鉴别器

针对载体在多径信号的影响下,传统码相位鉴别器不能实时有效鉴别出码相位误差,提出一种高斯码相位鉴别器构建方法。在GPS/INS深组合模式中,接收机码相位鉴别器的构建将改变传统设计方法。INS系统利用GPS接收机产生的即时码相位信息,建立INS系统码相位鉴别器,该INS码相位鉴别器用于辅助GPS接收机码相位鉴别器,构建高斯码相位鉴别器,有效降低码相位跟踪误差。仿真结果表明,与传统的码鉴相器相比,高斯码相位鉴别器减小了噪声方差,能有效提高鉴相精度,从而在多径干扰下提高定位精度。     

俄罗斯Fregat上面级

介绍了俄罗斯Fregat上面级的基本功能、组成和主要技术指标。给出了其几何布置、贮箱可承力、独立性与自主性、液-固耦合及大幅晃动和多次起动等特点与关键技术等。给出了2003年欧空局火星探测项目、2005年欧空局金星探测项目和中俄联合火星探测项目等典型应用。