光纤陀螺温度建模及补偿技术研究

研究了干涉式光纤陀螺的温度补偿问题。首先分析了光纤陀螺仪温度漂移的主要影响因素，并应用人工神经网络与数理统计方法建立了陀螺仪的温度补偿模型。最后对某型号陀螺进行了0～50℃环境温度下的大量恒温及变温实验，通过实验数据完善所建模型并完成了对该陀螺的温度补偿。实验结果表明：在特定温度环境下，该方法能够有效地改善光纤陀螺仪零偏稳定性。     

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。     

调频多工数据传输技术在交通诱导系统中的应用

针对我国智能型交通的现状,提出了基于调频多工数据传输技术的交通诱导系统.本文在分析研究RDS-TMC技术的基础上,设计了利用RDS技术传输实时交通信息的车载动态导航装置,并介绍了该装置的主要功能模块.在实验室条件下进行了测试,证明设计可行,运行可靠.     

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。     

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。     

Provocative radio transients and base rate bias: A Bayesian argument for conservatism

Most searches for alien radio transmissions have focused on finding omni-directional or purposefully earth-directed beams of enduring duration. However, most of the interesting signals so far detected have been transient and non-repeatable in nature. These signals could very well be the first data points in an ever-growing data base of such signals used to construct a probabilistic argument for the existence of extraterrestrial intelligence. This paper looks at the effect base rate bias could have on deciding which signals to include in such an archive based upon the likely assumption that our ability to discern natural from artificial signals will be less than perfect.     

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.