一种故障诊断专家知识库编辑和维护系统

设计了一种适用于火控系统的故障诊断专家知识库编辑和维护系统。从诊断对象的结构和功能原理出发,提出了一种对象式框架知识表示方法。该方法将诊断对象的原理知识统一为一种形式,简化了知识表示的复杂度。该系统分别建立对象式框架的状态、测试、规则、维修、图表等知识项,利用这些知识项建立系统的知识库。还对系统的可视化实现和功能进行了说明。实践表明该方法可以提高系统的直观性、简洁性,提高人机交互能力。     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

大规模科学数据体绘制技术综述

体绘制是刻画大规模科学数据中复杂物理特征的有效途径,然而,数据量极大、特征难以捕捉等问题依然是目前体绘制研究的主要挑战。为此,研究者们从三个方面对体绘制算法进行了深入研究,以提高大规模数据体绘制的效率和效果。一方面,依托硬件通过多处理器核来分担计算,降低单处理器核所要完成的计算量,是提高体绘制效率的一个有效途径。另一方面,充分发掘数据场内在特性对三维数据场进行约简,大幅减少绘制处理数据量从而降低算法开销,也是提高体绘制效率的一个有效途径。同时,在体绘制算法中融入特征分析和特征增强方法,让复杂物理特征从数据场中突显出来,以实现对科学数据的高质量绘制。本文对国内外体绘制技术相关研究进展进行了调研、综述,并分析了不同的研究方法,最后展望了未来体绘制技术研究的可能发展方向,包括应用驱动的特征体绘制、基于特征的约简体绘制、适应硬件的体绘制多级加速以及原位智能化体绘制等。     

喷水推进装置及其在舰艇上的应用

船舶喷水推进装置具有推进效率高、噪音小、操作性能好以及适合浅水航行等特点,其军事价值将越来越受到世界各国海军的重视.分析了喷水推进装置的优、缺点,简要介绍了其基本原理和结构,并总结了其在潜艇和水面舰艇上的应用,对其今后的应用与发展趋势作了简要分析.     

知识经济与创新教育

知识经济需要创新型人才,而创新型人才必须靠创新教育来培养,所以创新教育是知识经济时代的必然要求.创新教育在教育的基本理念、教育的过程与方法以及教育评价等方面都与传统教育有着本质的区别.     

实时潜艇损管专家系统的设计与实现

介绍实时潜艇损管专家系统的设计与实现.该系统应用专家系统理论对潜艇损管训练及其在危险情况下进行损管决策领域的知识进行了建模,具有连续实时判断潜艇受损后的浮态和稳性以及制定损管决策方案和损管有效性检验功能.     

新建构主义知识观

传统知识观将间接知识理解为人类现有的静态认识成果,从而造成了问接知识的绝对化,使间接知识不可避免地具有反建构性。要实现间接知识与个人认识建构的有机统一,就必须打破传统知识观对间接知识的成果化理解,把间接知识理解为人类认识的发展过程,使间接知识本身建构化。     

基于稀疏表示和非参数判别分析的降维算法

针对人脸识别问题提出一种新的监督降维算法。算法首先基于稀疏表示理论,利用同类样本间的稀疏重构来构建图。此方案不仅可以克服传统图构造方法中参数选择的困难,而且能够更好地刻画类内信息。然后,算法采用非参数类间离差来刻画类间信息,非参数类间离差在处理复杂分布数据时相比于参数类间离差更具判别力。最后,算法通过保持类内稀疏重构关系的同时最大化非参数类间离差来求得最优的投影矩阵。在ORL和Extended Yale B公共人脸数据库的实验表明,该算法能够获得较好的识别结果。     

普通建筑用钢与耐火钢火灾高温力学性能对比研究

钢材的高温力学性能是建筑结构耐火设计的重要参数。介绍了耐火钢的特点,通过试验对普通建筑用钢与两种耐火钢从常温到700℃高温的力学性能进行了对比研究,结果表明:温度在400~700℃之间,耐火钢的强度折减系数比普通建筑用钢高出很多;以钢材常温屈服强度的50%为破坏标准,普通建筑用钢与两种耐火钢的临界温度分别为550℃、650℃、660℃。