利用波形合成技术对柴油机排气噪声实行有源控制的研究

对四冲程柴油机(4-85)的排气噪声的机理和特性进行了研究;利用波形合成的原理,控制高速信号处理芯片TMS320C25合成次级声源的声波,可以实现宽带消声．对柴油机的排气噪声进行了有源消声的初步设计．     

基于时频分析的噪声去除

经典的数字信道化思想的实施都是建立在一组硬件滤波器构成的并行多路,虽然能实时处理,但随着处理数据带宽的增大和子信道频率分辨率的增加,设备开销将线性增长,整体可靠性也必然下降.基于信号的检测理论,在利用时频分析工具得到的时频图上进行信号检测,提出了检测去噪法.针对宽带数据背景噪声的特点,提出了噪声基底的3种估计算法:去大留小法、对数统计法和频移均值法,并线面结合对噪声基底进行平滑,实现了天电干扰的去除.通过实际接收到的某短波信号检验,证明了算法的有效性.     

基于概率推断网的水雷战专家系统

针对反水雷作战决策的需要,运用贝叶斯推理、模糊逻辑以及可信度不确定推理方法,设计了一个基于概率推断网的水雷战专家系统.利用该系统可得出两种结论:一是利用计算概率的方法得出的结论,二是利用计算可信因子方法得出的结论,两种结论呈现定量和定性的互补关系.可较好地为反水雷作战指挥员提供辅助决策,以减少舰船遭毁伤的概率.     

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.     

A guarantee rate optimization model for wastewater treatment system design under uncertainty

We consider a design problem for wastewater treatment systems that considers uncertainty in pollutant concentration levels at water sources. The goal is to optimize the selection of treatment technologies and pipeline connections, so that treated wastewater can achieve specified effluents discharge limits as well as possible. We propose a new two-stage model to optimize a set of guarantee levels, that is, the maximum concentration level of source pollutants for which treated wastewater can be compliant with discharge limits. In the first stage, treatment technologies and pipeline connections are selected. In the second stage, when pollutant concentration levels are revealed, wastewater distribution and mixing are determined. A key attractiveness of the proposed guarantee rate optimization model is that it can be simplified into a single-stage mixed-integer linear program. In our numerical experiments based on real-world pollutants data, the guarantee rate model demonstrates its advantages in terms of computational efficiency, scalability and solution quality, compared with the standard probability maximization model. Finally, the methodology proposed in this paper can also be applied to other two-stage problems under uncertainty with similar uncertainty characteristics.     

协方差压缩感知逆合成孔径雷达成像技术

为解决低信噪比条件下短观测时间雷达成像问题,提出一种基于回波协方差矩阵处理的压缩感知逆合成孔径雷达成像技术。该方法构建了回波协方差矩阵层面下的压缩感知问题模型,并通过特定的线性变换降低环境噪声对成像结果的影响。在仿真实验中,通过处理短观测时间和低信噪比条件下的模拟回波数据,该方法获得比传统压缩感知方法像质更好、对比度更强的目标成像结果。同时,其成像结果的目标背景比和背景噪声能量两个雷达图像评价指标都优于传统方法,进一步验证了该方法的有效性。     

随机-区间混合不确定性单输出模型确认指标

针对既有随机变量又有区间变量的模型预测结果与实验数据之间的一致性度量问题,对随机-区间混合不确定性模型确认指标进行研究。根据工程数学模型和实验过程中的不确定性来源,分析随机-区间混合不确定性模型确认的特点;运用概率方法和区间理论,提出新的随机-区间混合不确定性模型确认指标,讨论所提指标的性质,给出指标的计算方法和步骤。通过数字算例和工程算例,验证了所提指标的可行性和有效性。     

基于正交分集的灵巧噪声干扰对抗方法*

针对数字射频存储干扰机多通道宽带转发模式下的灵巧噪声干扰,提出了基于混沌调制信号、多谐波相位调制线性调频信号的正交分集抗干扰方法。混沌调制信号与多谐波相位调制线性调频信号具有“图钉”型模糊函数,除了具有良好的距离、多普勒分辨力,而且对回波频率敏感,通过正交分集设计,能够更好的适应宽带转发灵巧噪声干扰。通过计算机仿真对新方法的抗干扰性能进行了分析和验证,结果表明,在多通道宽带转发灵巧噪声干扰环境下,新方法的抗干扰改善因子能够达到10dB以上,抗干扰性能明显优于传统的频率捷变、斜率捷变方法。     

随机振动中晶体振荡器的有源降噪设计

针对晶体振荡器在振动下会发生输出频率漂移的问题,在分析声学有源降噪技术和加速度对晶体振荡器输出相位噪声的影响机理基础上,提出降低随机振动中晶体振荡器噪声的方法。在晶体振荡器外围电路中嵌入加速度传感器、模数转换器、数模转换器和数字处理器,构建对晶体振荡器相位噪声进行实时补偿的有源降噪系统。结果表明:设计的有源降噪系统在0.03g/Hz振动幅度、10~850 Hz频率范围的随机振动条件下,能达到20 d B的相位噪声补偿效果。     

Governing risks in international security

Risks are omnipresent in contemporary international security. Despite a long tradition in security studies going at least back to Von Clausewitz, we consider that the topic of risk remains under-examined. This forum seeks to advance the research agenda on risk in security studies by showcasing work of scholars using advanced concepts of risk, based on insights from sociology, biology, psychology, and safety studies, to better understand the role of risk in international security. As a way of introduction, this short article sets out the main debates.