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当前,公安科技创新经费投入存在着一些问题,从投入机制、投入结构和投入管理方式等方面提高经费投入效应,对于满足社会安全需求,实现公安科技创新体系高效运行具有现实的指导意义。 相似文献
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为了分析元器件失效率的不确定性对系统可靠性的影响,借鉴Borgonovo的矩独立灵敏度分析思想,在充分考虑了系统可靠寿命完整不确定性信息的情况下,提出了基于系统可靠寿命的矩独立重要性测度,用来分析不确定性条件下系统元器件失效率对其可靠寿命的平均影响。但由于系统可靠寿命函数是系统可靠度函数的反函数,一般无法解析表达而以隐函数的形式存在,致使该矩独立重要性测度难以高效准确求解。为了解决这一问题,文章提出了一种新的Kriging自适应代理模型的高效算法,该算法以Kriging代理模型预测值的变异系数作为自适应学习函数,通过自主增加新的试验样本,增强代理模型的预测准确性。阀门控制系统和民用飞机电液舵机系统两个算例分析表明,在保证计算精度的情况下,通过变异系数自适应学习函数,仅需添加少量系统可靠寿命试验样本,就能够构建用来充分近似系统可靠寿命函数的Kriging代理模型,解决了重要性测度的高效求解问题,从而验证了所提方法的合理性和算法的高效性。 相似文献
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《防务技术》2020,16(3):543-554
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. 相似文献