应用少量短电阻应变丝布置的损伤评估智能材料结构

智能材料结构是一门新学科，它越来越引起不同学科研究者们的兴趣，本文研究采用电阻应变丝为传感元件，人工神经网络为处理辨识器的损伤评估智能材料结构，提出并实施了一种少量短电阻应变丝的传感元件布置方法，用以对结构中损伤或大应变的区域和级别进行在线评估。     

基于神经网络模型的学习诊断

基于故障树模型的诊断把故障树的底事件分成三部分：必然的故障源集（ＣＦＳ）、正常底事件集（ＮＥＳ）和可能的故障源集（ＰＦＳ），及如何进一步确定ＰＦＳ中各元素的状态（正常或异常）。在存在大量训练样本的情况下，可采用基于神经网络模型的学习诊断方法来确定ＰＦＳ中各元素的状态，并通过对某卫星能源系统故障模拟原理性试验台的故障诊断验证了该方法的有效性。     

复合材料设计的回顾与展望

复合材料设计是复合材料研制过程中必须首先解决的问题，复合材料设计由定性化向定量化方向发展是其必然趋势。复合材料力学、材料设计专家系统和智能化设计系统应用于复合材料设计方面已取得巨大的进展，并推动复合材料科学与技术的发展。     

基于室内无线模型的穿墙损耗校正

K-M（Keenan-Motley）模型将单墙固定损耗值相加来计算室内无线信号穿透多墙的总损耗值，存在较大误差。针对该问题选取多种室内场景分别进行连续波（Continuous Wave，CW）测试，对无线信号穿墙损耗的影响因素和变化规律进行分析，提出了一种基于人工神经网络的室内无线模型穿墙损耗校正方法，对预处理后的测试数据进行训练并建立预测模型。经验证该预测模型符合校正判别准则，在实际场景下具有良好的预测准确度。     

密闭舱室中微生物防控实践

随着人类对外太空认知的增加和探索的深入，星球之间的交叉感染问题引起了越来越多的关注。行星保护已成为航天开发领域的一个重要的关注热点。本文对生物再生生命保障地基综合试验系统"月宫一号"105天有人密闭舱室中的微生物污染问题作了实验研究。实验获取的空气微生物演替规律可为今后密闭BLSS实验中建立更有针对性的微生物防控措施提供依据，并为解决密闭舱室微生物污染问题提供重要经验。     

神经网络BP算法预测可靠性增长

叙述可靠性增长的基本概念和神经网络BP算法，着重探讨BP算法用于可靠性增长预测的可能性。通过两个实例的预测结果，与常用的可靠性增长参数模型Duane，Gompertz的结果相比较。实例表明，该方法是可行的，而且是有效的，适应性强。可以预料，它不但能用于仪器设备试制过程中预测可靠性增长，而且可用于武器装备在储存过程之中可靠性下降的预估。     

Height-latitude structure of stationary planetary waves in the stratosphere and lower mesosphere

Daily UK Met Office stratospheric assimilated data for the Northern and Southern Hemispheres, accumulated for the period from 2004 to 2012 and pressure range of 1000–0.1 hPa, are used in this paper. The paper presents and thoroughly discusses spatial–temporal distributions of stationary planetary wave (SPW) amplitudes and phases, calculated on the basis of geopotential height, temperature, zonal and meridional wind data for zonal wave numbers 1 and 2 (SPW1 and SPW2). The climatological planetary wave amplitudes and phases are calculated by extracting waves from three types of data: daily, monthly mean and climatological monthly mean. It has been established that magnitude of amplitudes and height-latitude distribution of amplitudes of SPW1 and SPW2 depend on data processing method for all parameters. It has also been established that height-latitude distribution amplitudes and phases significantly differ for geopotential height, temperature, zonal and meridional wind and depend on wave number and hemisphere. However, height-latitude distributions of phases are little different from each other for the used methods of data processing.     

人工智能在OODA 循环中的应用

当前人工智能(AI) 是商业和工业，甚至军事应用领域中最热门的流行语。军事国防领域也已经将人 工智能发展成为了一种技术，并应用到军事作战和决策系统中。最初OODA 循环是最知名的空战战术模型， 后来发展成为各国空军的作战方法。OODA 循环对于任务的定义和决策过程的理解起到了重要的作用。本文主 要研究了人工智能在OODA 循环决策中的应用。讨论了未来OODA 循环各阶段中所需的AI 作用和技术，包 括可解释的人工智能和基于学习的人工智能。     

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.