基于字典学习的混合采样数据分类方法

混合采样数据不仅仅具有不同采样频率数据之间特征集合不同,还有样本数量不一致等特点,传统的分类方法不能直接使用。因此,本文提出一种基于Fisher判别准则字典学习的混合采样数据分类方法以处理采样数据的分类任务。该模型巧妙借助处理多视图数据的分类思想,利用基于Fisher判别准则的字典学习方法,生成的结构化字典的每个原子与数据的类标签相关,同时采用Fisher判别准则使类内散度更小,类间散度更大来约束编码系数矩阵,从而大大提升分类性能。此外,本文针对混合采样数据的样本数量不一致特点,设计了混合采样数据判别分析模型的分类方案。最后实验结果验证了本文方法的有效性。     

一种高速可变形飞行器智能变形决策方法

针对一类高速可变形飞行器(HMFV)的变形决策问题,提出一种基于深度确定性策略算法(DDPG)下考虑综合性能指标最优的智能变形决策方法。首先,以一类后掠角可连续变化的高速飞行器为研究对象,给出变形飞行器动力学模型,分析模型特性及变形量与关键气动参数之间的定性关系。其次,基于关键气动数据特征分析,考虑包含气动性能、控制误差在内的综合性能指标,设计一种基于DDPG算法的智能变形决策方案。再者,针对带有标称控制器的HMFV进行变形决策训练,实时获得滑翔过程中不同飞行状态下的最优构型。最后,仿真结果表明所设计的智能变形决策算法收敛效果好,且具备较好的泛化性能。相比于固定外形,可通过变形使得在不同状态下的升阻比保持最优,且与考虑单一决策指标相比,考虑综合指标最优的变形决策可进一步缩小姿态动态跟踪误差。     

空间变化场景下卫星部组件域适应识别研究

针对小样本限制下卫星部组件识别域适应困难的问题,提出一种变化场景下自适应迁移的目标识别算法.卫星部组件的识别模型框架为YOLO,迁移算法包括3个策略:基于特征关联性的样本加权策略,基于模型的参数自适应策略和最优特征变换自适应迁移策略.基于以上策略,YOLO模型建立域特征空间的相似性,选择性地迁移源域知识,同时在适应过程...     

基于蚁群优化的长短时神经网络变外形飞行器故障模式识别

变外形飞行器机械结构复杂,在变外形过程中发生故障的概率大,传感器测量成本高,针对这些问题,提出了一种基于长短时神经网络进行飞行器测试故障诊断的方法。首先根据变外形飞行器的气动参数模型和非线性动力学模型,构建变外形飞行器执行机构故障特征数据库。然后针对变外形飞行器发生故障时的序列化特征数据,提出基于长短时神经网络的执行器故障诊断框架。利用蚁群优化算法对网络训练的超参数进行优化,提高故障诊断的准确性与泛化性。通过仿真验证了该方法可实现变外形飞行器的低成本、高效率、高精度的故障快速定位。     

遥感场景分类的批处理协方差神经网络方法

针对卫星遥感图像场景分类数据集中存在的局部区域特征异常问题,提出一种采用批处理协方差层的神经网络(CovNN)模型进行遥感场景分类的方法。该方法通过计算全输入通道的局部区域均值实现一种3D批处理协方差算法,能够有效消除局部区域均值的影响,从而更好地处理局部光照过强和局部区域存在无关特征的问题。将其应用于存在局部光照异常和局部无关特征问题的卫星采集AID数据集和NWPU RESISC45数据集中,实验表明CovNN在两个数据集上均取得了超过现有卷积神经网络(CNN)的召回率,可有效降低图像局部区域特征异常的不利影响。     

基于机器学习的二次电子发射唯象模型

基于机器学习和深度人工神经网络(artificial neural network, ANN)提出一种二次电子发射唯象模型。利用Vaughan模型生成先验数据集,用于训练生成描述二次电子发射一般规律的先验知识ANN模型,并在不同参数条件下验证了先验知识ANN模型的正确性。然后,分别利用银和铝合金材料的二次电子发射系数实验数据修正先验知识ANN模型,分别得到了描述两种材料的特异ANN模型。测试结果表明,特异ANN模型计算结果与实验结果相比的平均绝对误差较Vaughan模型和Furman模型降低了30%以上,与复合唯象模型精度相当或更高。在小样本条件下测试了二次电子发射ANN模型的正确性,验证了分步训练方式的有效性和二次电子发射ANN模型对于小样本集的适应性。提出的基于机器学习的二次电子发射唯象模型能够避免复杂的参数修正过程,能够基于先验知识提升模型对于小样本的适应性,能够实现二次电子发射系数的连续插值,适于在数值模拟软件中使用。     

小推力最优轨迹协态估计的高效机器学习方法

针对变比冲小推力轨迹间接优化中的协态变量初值猜测问题,提出了一种基于机器学习的协态变量初值高精度高效估计方法。首先,基于标称最优轨迹延拓,建立了状态量边值高扰动上限情形下的数据集生成方法,并分析了扰动上限对求解效率的影响。然后,构建了基于位置速度、轨道根数和改进春分点轨道根数多形式状态量组合输入的人工神经网络(ANN)映射关系,分析并优化了神经网络结构。将提出的方法应用于深空探测小推力转移场景,仿真结果表明该方法相对于标称轨迹直接扰动的数据集生成方法及单一形式状态量输入的人工神经网络映射方法,均有效地提升了求解收敛率,能够高效高精度地估计协态变量初值,实现轨迹快速优化。     

An inverse design method for supercritical airfoil based on conditional generative models

Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper, a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning. A Conditional Variational Auto Encoder(CVAE) and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN), are conducted as generative models. They are used to generate target wall Mach distributions for the inve...     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.     

Heading south with a north-up map: A choice processing analysis of map alignment effects

This study employed an information accumulation model of choice reaction times to investigate alignment effects in mental representations of maps. University students studied a map from a single orientation (with north at the top). In a subsequent two-choice reaction time task, the students’ spatial knowledge of the map was assessed employing spatial left/right judgments, which were made from imagined perspectives that were either north-aligned or south-aligned. Data showed a standard alignment effect, favoring north- over south-aligned trials. To examine the locus of this effect, data were fit using the Linear Ballistic Accumulator (LBA) model of speeded decisions (Brown & Heathcote, 2008). Of interest were three model parameters: drift rate, the speed at which evidence accumulates toward a response; response threshold, the amount of evidence demanded from the decision maker before selecting a response; and non-decision time, the time consumed by pre- and postdecisional processes. The best-fitting model suggested that non-decision time accounted for the alignment effect. The difference in non-decision time between north and south-aligned judgments suggests a mental alignment stage on south-aligned trials, accounting for the longer reaction times for judgements misaligned with the presented north orientation of the map.