基于多模型融合的航空电子产品故障预测方法

针对复杂机载环境应力条件下航空电子产品故障预测所面临的退化趋势差异大、训练数据样本量小等问题,提出了一种改进长短期记忆(Long Short-Term Memory, LSTM)神经网络模型与集成学习框架相结合的故障预测方法,以满足现代综合航空电子系统智能调度管理与自主维护保障的需求。该方法在LSTM模型中引入Dropout机制,构建基于不同历史数据集的差异性LSTM模型组,以解决故障预测时序信息记忆问题与小样本条件下数据驱动模型训练过拟合问题;采用Adaboosting算法计算模型权重,并基于实时数据动态调整,以滤除复杂机载环境应力引入的预测误差,解决多模型融合的性能差异问题。最后,采用NASA公开的锂电池退化数据集进行仿真验证,实验结果表明,相较于传统BP神经网络、经典LSTM和LSTM基模型,该方法具有更高的趋势拟合度和预测精度。     

Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons,breathers, and rogue waves

The dimensionless third-order nonlinear Schrödinger equation (alias the Hirota equation) is investigated via deep leaning neural networks. In this paper, we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g. bright soliton, breather, and rogue waves) of the Hirota equation when the two types of the unperturbated and perturbated (a 2% noise) training data are considered. Moreover, we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of bright solitons.     

基于数据驱动的卫星锂离子电池寿命预测方法

锂离子电池由于具有工作电压高、体积小、质量轻、比能量高、寿命长和自放电率小等优点,成为 替代传统镍氢、镍镉电池的第三代航天器用储能电源。寿命预测是锂离子电池健康管理的重要方面,是掌 握电源衰退的重要手段。锂离子电池剩余使用寿命预测问题已成为电子系统健康管理领域的研究热点和具 有挑战性的问题之一。本文基于NASA埃姆斯中心的锂离子电池地面试验采集数据,首先分析了3种类型 的锂离子电池预测方法,之后重点研究了几种有效的数据驱动的锂离子电池寿命预测方法,并对各种预测 方法的效果进行了评价。实验结果表明,本文提出的方法能够有效的用于基于数据驱动的锂离子电池寿命 预测中,具有较强的工程应用价值。     

HRM: H-tree based reconfiguration mechanism in reconfigurable homogeneous PE array

In order to accommodate the variety of algorithms with different performance in specific application and improve power efficiency,reconfigurable architecture has become an effective methodology in academia and industry.However,existing architectures suffer from performance bottleneck due to slow updating of contexts and inadequate flexibility.This paper presents an H-tree based reconfiguration mechanism(HRM)with Huffman-coding-like and mask addressing method in a homogeneous processing element(PE)array,which supports both programmable and data-driven modes.The proposed HRM can transfer reconfiguration instructions/contexts to a particular PE or associated PEs simultaneously in one clock cycle in unicast,multicast and broadcast mode,and shut down the unnecessary PE/PEs according to the current configuration.To verify the correctness and efficiency,we implement it in RTL synthesis and FPGA prototype.Compared to prior works,the experiment results show that the HRM has improved the work frequency by an average of 23.4%,increased the updating speed by 2×,and reduced the area by 36.9%;HRM can also power off the unnecessary PEs which reduced 51%of dynamic power dissipation in certain application configuration.Furthermore,in the data-driven mode,the system frequency can reach 214 MHz,which is 1.68×higher compared with the programmable mode.     

数据驱动片内多核通信结构

针对当前条件下多核处理器遇到的通信瓶颈问题,设计了一种采用数据驱动机制的片内多核通信结构,该结构包括数据驱动模块和片上路由器.数据驱动模块用来进行数据完备性检测;片上路由器则实现处理器核间的通信及"簇"间通信.在Altera公司的CycloneIII开发板上使用NIOS软核构建了多核系统进行了验证.实验结果表明,本设计可以有效的实现多核片内通信,具有很好的可扩展性.     

Scalable Linear Array Architecture with Data-Driven Control for Ultrahigh-Speed Vector Quantization

Current and future requirements for adaptive real-time image compression challenge even the capabilities of highly parallel realizations in terms of hardware performance. Previously proposed linear array structures for full-search vector quantization do not offer scalability and adaptivity in this context, because they require separate data/control pins for dynamically updating the codevectors and complicated interlock mechanisms to ensure that the regular data flow is not corrupted as a result of updates. We explore the design space for full-search vector quantizers and propose a novel linear processor array architecture in which global wiring is limited to clock and power supply distribution, thus allowing high-speed processing in spite of only limited communication with the host via the boundary processors. The resulting fully pipelined design is not only area-efficient for VLSI implementation but is also readily scalable and offers extremely high performance.     

基于弹性力学第一性原理的数据驱动力学建模

提出了一种基于弹性力学第一性原理的数据驱动力学建模方法,其能够从基于弹性力学方程的数值计算结果建立简洁且能准确捕捉变形机制的力学模型。基于有限元计算得到的高精度数据和无监督数据驱动控制方程识别方法Seq-SVF,从梁的载荷和位移数据中自动识别出了Timoshenko梁形式的弯曲控制微分方程,得到了三种不同加载条件下剪切影响系数关于结构尺寸和力学参数的函数表达式。揭示了经典模型适用的加载条件,同时还给出了一种未发现的新模型。通过将基于弹性力学的第一性原理计算与数据驱动范式相结合,克服了传统建模方法的局限性和对人类经验的强依赖性,为建立简洁的力学模型提供了一种新途径。     

联合订货下基于ø-散度的多产品库存鲁棒优化模型

针对多产品联合库存决策问题,在市场需求不确定条件下,建立了考虑联合订货成本的多产品库存鲁棒优化模型。针对不确定市场需求,采用一系列未知概率的离散情景进行描述,给出了基于最小最大准则的鲁棒对应模型,并证明了(s,S)库存策略的最优性。进一步,在仅知多产品市场需求历史数据基础上,采用基于ø-散度的数据驱动方法构建了满足一定置信度要求的关于未知需求概率分布的不确定集。在此基础上,为获得(s,S)库存策略的相关参数,运用拉格朗日对偶方法将所建模型等价转化为易于求解的数学规划问题。最后,通过数值计算分析了Kullback-Leibler散度和Cressie-Read散度以及不同的置信水平下的多产品库存绩效,并将其与真实分布下应用鲁棒库存策略得到的库存绩效进行对比。结果表明,需求分布信息的缺失虽然会导致一定的库存绩效损失,但损失值很小,表明基于文中方法得到的库存策略能够有效抑制需求不确定性扰动,具有良好的鲁棒性。     

基于数据驱动模型求解热传导反问题

热传导反问题求解在工程领域具有重要的应用价值.本文发展数据驱动模型识别了管道内壁几何形状和皮肤肿瘤生长参数等热传导反问题.在管道内壁几何形状识别问题中,首先采用随机生成模型结合有限元法求解热传导正问题,并采用有效导热系数转化的思想,建立机器学习模型,求解了测点温度与有效导热系数之间的抽象映射关系,进而实现管道内壁几何形...     

Data-Driven Corrections of Partial Lotka–Volterra Models

In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many transient chemical species are not of interest in a given reaction) and in epidemiological models (only certain subpopulations are consequential). Thus, it is common to use greatly reduced or partial models in which only the interactions among the species of interest are known. In this work, we explore the use of an embedded, sparse, and data-driven discrepancy operator to augment these partial interaction models. Preliminary results show that the model error caused by severe reductions—e.g., elimination of hundreds of terms—can be captured with sparse operators, built with only a small fraction of that number. The operator is embedded within the differential equations of the model, which allows the action of the operator to be interpretable. Moreover, it is constrained by available physical information and calibrated over many scenarios. These qualities of the discrepancy model—interpretability, physical consistency, and robustness to different scenarios—are intended to support reliable predictions under extrapolative conditions.