基于隶属度和LMK-ELM的航空电子部件诊断方法

为提高航空电子部件模块级故障诊断精度，提出一种新的面向"软聚类"的局部多核学习（LMKL）-超限学习机（ELM）离线诊断方法。通过引入模糊C均值聚类对样本空间进行模糊划分，挖掘聚类内部多样性的同时，实现了对过学习的抑制；将模糊划分产生的隶属度信息融入LMKL-ELM的优化过程，运用基于初始-对偶混合优化问题的三步优化策略克服了局部核权重二次非凸的问题，在l₁-范数与l₂-范数约束下分别给出了相应的更新方法。将所提方法应用于某型机前端接收机，结果表明：与4种流行的多核诊断方法相比，该方法可有效避免漏警、抑制虚警，在l₁-范数和l₂-范数约束下，其诊断精度比其他方法的平均值分别提升了4.09%和5.13%。     

Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed “HASDM-ML,” “CHAMP-ML,” and “MSIS-UQ” machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density values. We develop several Monte Carlo methods, each capturing a different spatiotemporal density correlation, to study the effects of density uncertainty on orbit uncertainty propagation. However, Monte Carlo analysis is computationally expensive, so a faster method based on the Kalman filtering technique for orbit uncertainty propagation is also explored. It is difficult to translate the uncertainty in atmospheric density to the uncertainty in orbital states under a standard extended Kalman filter or unscented Kalman filter framework. This work uses the so-called “consider covariance sigma point (CCSP)” filter that can account for the density uncertainties during orbit propagation. As a test-bed for validation purposes, a comparison between CCSP and Monte Carlo methods of orbit uncertainty propagation is carried out. Finally, using the HASDM-ML, CHAMP-ML, and MSIS-UQ density models, we propose an ensemble approach for orbit uncertainty quantification for four different space weather conditions.     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

基于机器学习的导弹干扰试验效果评估实证研究

针对复杂电磁环境下导弹干扰试验影响因素众多,难以量化,试验数据采集困难以及实验数据中普遍存在类不平衡等问题,基于机器学习创建导弹试验干扰效果评估模型,采用随机森林、支持向量机、朴素贝叶斯、多层感知机等常见模型对导弹试验干扰效果进行评估。特别针对小数据样本中的类不平衡问题提出 2阶段分类模型,采用过采样方式解决类不平衡问题并采用随机森林进行分类。基于开源的导弹干扰效果评估数据,通过实证研究说明,基于过采样的随机森林模型在干扰效果评估问题中具有较强的泛化能力和鲁棒性,在 AUC指标上,该模型比多层感知机模型在中位数上最多提高 60%,建议在后续的试验中采用该模型进行导弹干扰效果评估。     

测量数据部分丢失的非线性系统迭代学习控制

研究非线性离散系统的迭代学习控制．利用并行分配补偿方法(PDC)确定非线性系统的T-S模型,把非线性模型特换为局部线性模型。假定数据丢失的概率已知,采用满足Bernoulli分布的序列来描述测量数据的丢失,研究具有测量数据部分丢失的线性离散系统的迭代学习控制器的设计。结合T-S模型设计了数据丢失的迭代学习控制器,所设计的迭代学习控制器具有期望收敛特性和二次型性能指标。仿真结果表明了该设计方法的有效性。     

自主空战连续决策方法

未来空战正朝着无人化、自主化方向发展,自主空战决策方法是未来空战的重要支撑手段之一。传统空战决策方法由于维度限制,存在无法处理连续动作与远视决策的问题。基于Actor-Critic 方法提出空战连续决策的统一架构,依据空战训练经验对状态空间、动作空间、奖励及训练科目进行合理设计,测试多种连续动作空间强化学习算法在高不确定性空战场景下的学习效果并进行可视化验证。结果表明：基于本文提出的方法架构,可以实现连续动作下的远视价值寻优,智能体可以在复杂空战态势下做出最优决策,对随机机动飞行目标有较高的击杀率,且空战机动轨迹具有较高的合理性。     

星座碰撞规避的迭代学习构型保持方法CSCD

针对低轨卫星星座运行中地球引力摄动的周期特性,基于迭代学习控制(ILC)方法,提出了星座碰撞规避的迭代学习构型保持方法。该方法由反馈控制和ILC两部分构成,分别抑制卫星运行过程中的非周期摄动和周期摄动对构型保持精度的影响,进而在地球非球形引力摄动未知条件下,通过相对构型的精确保持实现对星座卫星碰撞的有效规避。仿真结果表明,在地球J摄动影响下,与传统反馈控制相比,ILC方法以更小的控制输入实现了轨道保持精度的显著提升,进而在星座卫星轨道高度相近的情形下显著降低了碰撞风险,且控制器可在保证收敛性能的前提下,实现启动时间的灵活选择。     

内圣外王

内圣是人格主体在德、能、学三个基本方面的自我修养,其至高境界是"极高明而道中庸";外王则是指卓越的"治平"社会功德。内圣是外王的前提和基础,外王是内圣的实践归宿和社会价值体现。内圣外王具有道德的、政治的和学术的三重理想人格涵义。内圣外王人格论的思想理论基础是传统的民本思想、"人贵论"和"人性论"。     

一种适用于航天器某些故障诊断的知识获取方法

尽可能快地找出航天器在工作过程中出现的故障，采取补救措施，可减少或避免重大损失。故障诊断专家系统可帮助人们作出快速诊断，但它的诊断准确率主要取决于其知识的正确性。以往这些知识是靠领域专家给出的。本文根据航天器某些故障的特点，提出了一种处理连续变元示例的知识自动获取方法，用这种方法获取的诊断故障的规则，在实践中检验是正确的     

知识获取子系统ACQUIST的设计与实现

知识获取是专家系统的瓶颈问题,本文详细叙述了数控系统故障诊断专家系统的知识获取子系统ACQUIST的设计思想和实施方法。