基于MapReduce的CME参数识别模型并行计算技术

日冕物质抛射（Coronal Mass Ejection，CME）参数识别模型是太阳风预报过程的重要组成部分.在空间环境预报业务中，为提高太阳风预报的准确率，需要提高CME参数识别的精度.模型以计算任务串行的方式运行，运算效率低导致模型运算时间长，不能满足这种需求.CME参数识别模型的物理运算过程相互不独立，其在单节点上的运行方式不能满足并行化要求.基于MapReduce的并行计算框架，改进了CME参数识别模型的计算流程，提出CDMR（CME detection under MapReduce）方法，实现了CME参数识别模型的并行计算，并对比分析CME参数识别模型在串行计算和MapReduce并行计算下的运行时间，提高了模型的识别精度和计算效率.      

基于类脑模型与深度神经网络的目标检测与跟踪技术研究

目标检测与跟踪技术广泛应用于交通、医疗、安保和航天等领域.目前，目标检测与跟踪技术面临目标微弱、背景复杂、目标被遮挡等挑战.同时，随着脑科学研究的不断深入，人们对人脑视觉系统的理解逐渐透彻，利用类脑计算解决复杂背景下高精度目标检测与跟踪问题成为相关领域的重要研究方向.本文结合神经工程导向的类脑模型和计算机工程导向的深度神经网络(Deep Neural Networks, DNNs)，提出多种基于类脑模型与深度神经网络的目标检测与跟踪算法，包括：基于演算侧抑制的目标检测算法，基于结构 对比度(Structure Contrast, SC)视觉注意模型的弱小目标检测算法和基于记忆机制与分层卷积特征的目标跟踪算法.实验结果表明，将类脑模型和深度神经网络应用于目标检测和跟踪领域，有利于实现复杂条件下的高精度目标检测和鲁棒性目标跟踪.     

Effect of curing condition on bonding quality of scarf-repaired composite laminates

Both single-face vacuum bag curing (SVC) and double-face vacuum bag curing (DVC) can be used in scarf repair of composite structures. But different curing conditions caused by the sealing state may affect the bonding quality of scarf-repaired structures. In this paper, the effect of curing condition on bonding quality of scarf-repaired laminates was experimentally investigated in terms of surface profiles, moisture absorption curves and section profiles. In order to further explore the moisture absorption mechanism, finite element model of the repaired laminates using DVC was established with moisture diffusion of both the adhesive and composite laminates considered. This model was verified by experimental results. Based on the model of DVC case, the model of SVC case was built by changing moisture absorption parameters of the adhesive. Results show that SVC reduces the bonding quality, mainly reflecting in more adhesive inner voids and patch-to-parent dislocation. And SVC increases moisture absorption rate and moisture equilibrium content of the adhesive, and its effect on the former is far greater than that on the latter.     

A boundary vector cell model of place field repetition

Hippocampal place cells are thought to form the neural substrate of a global cognitive map. However, in multicompartment mazes, these cells exhibit locally repeating representations, undermining the global cognitive map view of place cells. This phenomenon appears to be related to the repetitive layout of these mazes, but still no hypothesis adequately explains it. Here, we use a boundary vector cell (BVC) model of place cell firing to model the activity of place cells in numerous multicompartment environments. The activity of modeled place cells bears a striking resemblance to experimental data, replicating virtually every major experimental result. Our results support the BVC model and indicate that locally repeating place cell firing could result purely from local geometry.     

直升机惯性传感器结构的模态优化

提出了一种基于迭代抽样和径向基插值的自适应代理模型方法。这种自适应方法以减少仿真计算数量和提高代理模型自适应能力为目的,使用多岛遗传算法选择新增样本点并使新增样本点位于设计空间的稀疏区域,使得所有的样本点均匀分布于设计空间。标准误差用来判断代理模型的精度大小以决定是否对代理模型进行更新。这种自适应代理模型结合多岛遗传算法对直升机的惯性传感器结构模态进行优化。用拉丁超立方抽样方法选择10个样本点构建初始的代理模型,自适应代理模型的计算结果表明2%的误差条件下需要额外增加7个样本点。优化结果表明不同的权重系数对最优模态特性的影响很大,惯性传感器结构的一至六阶模态值更加远离直升机的激励频率。     

基于Kriging模型梯度解析解的改进一次二阶矩方法

改进一次二阶矩（AFOSM）法是一种基于功能函数梯度的结构可靠性分析方法，鉴于其对隐式函数的梯度较难求解，提出了一种基于Kriging模型梯度解析解的AFOSM方法，利用Kriging代理模型的解析表达式推导得到功能函数对输入变量的梯度解析解，为AFOSM中设计点的确定提供高精度的梯度信息。通过Kriging与AFOSM的结合，很好地解决了基于有限元模型的隐式情况下梯度计算量相当大、可靠性分析难的问题。数值与工程算例验证了所提Kriging梯度解析解的较高精确性，同时验证了所提基于Kriging解析解的AFOSM结构可靠性分析方法的正确性与较高精度。     

300 MVA脉冲发电机组滑差方式起动过程研究

介绍了300 MVA脉冲发电机组的滑差装置。对机组采取滑差方式的起动过程进行了分析和等效建模。根据机组实际起动过程电气波形,利用非线性规划模型,建立了滑差装置液体电阻等效仿真模型,并代入机组参数对起动过程进行仿真,仿真结果和实际波形吻合,验证了模型的正确性。在此基础上,为了使机组的转速达到额定转速,提出在滑差装置液体电阻低位时并入两级固体金属电阻后短接的方案,给出了两级固体金属电阻的阻值等理论参数,并通过仿真验证了方案的可行性。建立的仿真模型可以作为研究不同参数、不同控制策略下的机组起动过程动态特性的重要工具。     

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.     

基于变刚度变阻尼的柔性喷管动态模型

针对柔性接头动态迟滞曲线受控制系统控制位置精度和动态响应速度影响较大的问题,基于电液伺服机构和柔性接头变刚度变阻尼模型,构建了柔性喷管的电液伺服机构-变刚度变阻尼模型,将其和电液伺服机构-定刚度定阻尼模型进行了对比。分析了电液伺服机构主要参数、柔性接头工作参数等对电液伺服机构-柔性接头系统动态特性的影响。分析结果表明:电液伺服机构-变刚度变阻尼模型所构造的迟滞曲线可更准确地与实验结果相吻合,并符合迟滞曲线随频率变化的规律,反馈系数、放大器静态放大系数、电液伺服机构增益、滑阀流量增益等参数对系统动态特性的影响更为明显。该模型为固体火箭发动机电液伺服机构-柔性接头系统动态特性的调整提供理论依据。