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.     

Pricing and capacity allocation strategies: Implications for manufacturers with product sharing

Emerging sharing modes, like the consumer-to-consumer (C2C) sharing of Uber and the business-to-consumer (B2C) sharing of GoFun, have considerably affected the retailing markets of traditional manufacturers, who are motivated to consider product sharing when making pricing and capacity decisions, particularly electric car manufacturers with limited capacity. In this paper, we examine the equilibrium pricing for a capacity-constrained manufacturer under various sharing modes and further analyze the impact of capacity constraint on the manufacturer's sharing mode selection as well as equilibrium outcomes. We find that manufacturers with low-cost products prefer B2C sharing while those with high-cost products prefer C2C sharing except when the sharing price is moderate. However, limited capacity motivates manufacturers to enter into the B2C sharing under a relatively low sharing price, and raise the total usage level by sharing high-cost products. We also show that the equilibrium capacity allocated to the sharing market with low-cost products first increases and then decreases. Finally, we find that sharing low-cost products with a high limited capacity leads to a lower retail price under B2C sharing, which creates a win-win situation for both the manufacturer and consumers. However, sharing high-cost products with a low limited capacity creates a win-lose situation for them.     

温度对半导体激光器退化的影响

采取等效电路模型仿真和加速退化试验相结合的方法研究温度对半导体激光器不同退化模式的影响规律。针对半导体激光器有源区退化和腔面退化进行分析,发现有源区退化会使半导体激光器阈值电流增大,而腔面退化会使半导体激光器斜率效率减小;进行了半导体激光器热特性建模与仿真,发现温度升高会使半导体激光器阈值电流增大;利用半导体激光器加速退化试验平台进行了半导体激光器加速退化试验。仿真与试验结果证明:温度升高会加剧半导体激光器腔面退化,而对有源区退化无显著影响。上述结论对进一步完善半导体激光器温度-退化仿真模型,研究温度对半导体激光器退化的作用机理和防护措施有积极作用。     

稳定特征选择的多目标蚁群优化

为了提高进化算法特征选择稳定性,提出一种面向稳定特征选择的多目标蚁群优化方法。通过抽样策略集成三种特征排序法的输出作为多目标蚁群优化的稳定性指导信息,聚合特征的费舍尔分值和最大信息系数值作为多目标蚁群优化的启发式信息,以分类正确率和扩展昆彻瓦指标值作为两个优化目标,兼顾算法的分类性能与特征选择稳定性。在四个标准数据集上进行对比实验,结果表明,所提方法能够在分类性能与稳定性方面达到较好的平衡。     

考虑弹体动态特性的非奇异有限时间制导律

为满足导弹拦截机动目标时交会角约束和有限时间收敛的需求,建立了考虑弹体一阶动态特性的制导模型。把目标加速度视为有界外界干扰,同时结合非线性反步设计法中的动态面法,设计一种考虑弹体动态延迟的非奇异滑模制导律,并且证明了基于Lyapunov稳定性理论制导系统状态可渐进收敛到零。在所设计的制导律下,对单侧机动的低空高速目标进行仿真。仿真结果表明所设计的非奇异制导律可以有效降低弹体动态延迟带来的影响,而且具有较低的脱靶量与交会角误差;与考虑弹体动态特性和交会角约束的最优导引律相比,其具有更高的制导精度。     

基于迭代学习观测器的卫星姿态滑模容错控制

为解决卫星上反作用飞轮存在安装偏差、故障及外部干扰情况下的姿态控制问题,提出了一种基于迭代学习观测器的姿态容错控制方法。该方法通过设计迭代学习观测器,以较小的计算量实现对执行机构发生的故障以及安装偏差进行精确的估计。并利用观测器的观测结果设计滑模控制器,使处于外部干扰条件下的卫星系统在执行机构发生故障的情况下可以快速稳定地完成姿态机动任务。进一步基于Lyapunov稳定性定理证明了迭代学习观测器及控制器的全局渐近稳定性。针对反作用飞轮存在不确定性及故障的情况下进行仿真,仿真结果表明,与同类容错控制方法相比,所提方法可以更加快速精确地对故障进行估计并完成姿态控制。     

空气深度预冷组合循环发动机吸气式模态建模及性能分析

针对空气深度预冷组合循环发动机——协同吸气式火箭发动机(Synergistic Air-Breathing Rocket Engine,SABRE),采用部件法对其进行建模,匹配计算得到吸气式模态下飞行走廊内其性能参数变化规律,并研究其高度速度特性。计算模型可信度较高,推力误差小于6%,能够较为准确地模拟SABRE吸气式模态的性能参数。结果表明:SABRE兼具火箭发动机大推力和航空发动机高比冲的特点,吸气式模态下比冲介于21 300~27 380 m/s,随着高度速度的增大,其推力比冲先增大后减小;SABRE利用预冷器将入口空气温度降低,可使其空域速域拓宽至25 km、5Ma,满足高超声速飞行的动力需求;发动机速度下限由压气机最大流量决定,速度上限则由氦气回路减压器工作限制条件决定。     

双门式炮闩膨胀波火炮内弹道建模与仿真

根据膨胀波火炮的发射原理,针对两种不同开闩方式分别进行讨论,着重对最新设计研制的双门式炮闩膨胀波火炮进行内弹道建模与仿真,并将仿真结果与同型号常规封闭火炮的内弹道性能进行对比。结果表明,在相同装填条件下,膨胀波火炮在不影响弹丸初速的前提下能大幅度减小火炮后坐冲量和身管温度。     

机械系统状态监测特征参数评价与选取

根据机械不同异常状态出现的频率和危害度的大小,引入状态权值,建立了特征参数总体敏感性和稳定性的评价指标,采用专家系统设计思想,编写了机械状态监测特征参数评价与选取应用软件,实现了特征参数的自动选取以及对特征参数和机械状态数据的有效管理。     

法学教学中案例教学法的反思与探索

法学是一门理论性与实践性都很强的学科,法学教育的目的在于为社会培养应用型、实践型的法律人才。传统上重理论轻实践的教学模式已无法满足这一要求,而科学合理地引入案例教学是弥补传统模式不足的有效手段。