利用加热减小水下航行器阻力的研究

如何减小表面摩擦阻力来增大航速一直是人们关心的问题,利用推进系统排放的废热加热航行器壳体的层流区可以大大减小水下航行器的阻力。本文计算了加热某型鱼雷的阻力变化,分析了阻力变化对航速和能耗的影响。对某型鱼雷,动力系统热效率为35%,在航速为26米/秒,阻力可减小42.3%,在保持相同能耗情况下,加热后的航速可增加20.1%;如果保持航速为26米/秒不变,加热后的鱼雷能耗可减小42.3%。     

有机钼化合物作为极压添加剂的抗磨机理

本文用X—射线光电子能谱(XPS)技术对新合成的S、O、Mo油溶性化合物作为极压剂所形成的边界润滑膜进行了微观分析,并结合DSC分析、歇尔式四球机试验和Falex试验结果对该有机化合物的极压抗磨机理进行了初探。     

稀疏平衡变分自动编码器的文本特征提取

针对文本特征提取方面的高维数据特征区分度较低、基于规则的特征学习的自学习性能差、变分自动编码器存在过度剪枝等问题,提出稀疏平衡变分自动编码器(Sparse Balanced Variational AutoEncoder,SBVAE)的文本特征提取模型.为消除噪声干扰,提高文本特征提取模型的鲁棒性,在文本特征提取的输入...     

高抗噪无线车内语音通信系统的研究

由于现行的车内通话器均采用有线连接和传统的降噪方式,导致了乘员活动范围受限以及降噪能力不强等问题.提出了一套综合应用蓝牙技术和骨传导技术的无线语音通信系统,并将其应用到装甲车辆上,最后提出了一些在实际设计中需要解决的问题,为今后进一步研究提供参考.     

基于强度折减法的海底隧道强震破坏机理

将强度折减法引入强震作用下的海底隧道衬砌结构安全稳定性研究,利用FLAC3D软件建立了海底隧道-岩土体-海水相互作用的强度折减法数值模型,综合考虑海底隧道衬砌在强震作用下的位移、剪应变增量和塑性区变化,确定了海底隧道衬砌局部破坏的极限平衡状态和整体破坏的极限平衡状态,分析了海底隧道衬砌在强震作用下的破坏机理。结果表明:海底隧道衬砌在水平地震波的作用下,主要是围岩变形导致隧道衬砌破坏;隧道衬砌破坏位置发生在右侧侧墙中部,破坏形式为剪切破坏;隧道衬砌局部破坏的极限安全系数为1.47,整体破坏的极限安全系数为1.8,2种安全系数的极限状态可对应结构设计中的正常使用极限状态和承载力极限状态。研究成果可为海底隧道衬砌设计提供理论依据。     

一种单轴旋转捷联惯导系统高精度快速对准方法

在晃动条件下,需要延长粗对准时间来提高粗对准精度。否则,无法把方位误差控制在小角度范围内,从而导致后续的精对准无法快速收敛。针对这个问题,提出了一种利用逆向导航技术的单轴旋转捷联惯导系统高精度快速对准方法,最大限度地延长粗对准时间,并把采样数据存储下来,进行逆向精对准。这种算法充分地利用了对准数据,在固定对准时间内极大程度的提高了对准精度。试验证明,这种算法计算量小,算法简单,能实现单轴旋转捷联惯导系统高精度快速对准,且对准精度高,具有一定的工程应用价值。     

基于粗糙集的装备维修信息属性约简算法

分析了装备维修信息的概念和组成,将粗糙集理论应用到装备维修信息处理中,结合信息熵和互信息的定义,界定了一种新的属性重要度,并将其作为启发信息,提出了改进的基于属性重要度和互信息的属性约简算法,并通过对装备维修信息约简,进行了算法验证。实验结果表明:所提出的算法精度较高,得到的结果比较符合实际,能够为装备维修保障提供决策依据。     

基于支持向量机的威胁判断模型

目标威胁判断是防空作战中一项重要内容,在建立目标威胁模型时,首先要挑选特征参数,分析了影响威胁度的若干因素.这里采用Rough理论中知识约简方法选择目标的特征参数;支持向量机是一类新型机器学习方法,由于其出色的学习能力,该技术已成为当前国际机器学习界的研究热点,利用支持向量机建立了威胁判断模型,给出了实例和解决此问题的支持向量机源程序.通过实例与神经网络法的结果进行了比较,结果表明支持向量机比较精确和简单.     

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.