随机振动台根据功率谱密度(PSD,Power Spectral Density)生成的信号为高斯信号,而实际振动环境有时是非高斯的,因此随机振动实验常常无法准确模拟产品在真实振动环境下的失效情况.通过两个案例分别对均方根值(RMS,Root Mean Square)不...随机振动台根据功率谱密度(PSD,Power Spectral Density)生成的信号为高斯信号,而实际振动环境有时是非高斯的,因此随机振动实验常常无法准确模拟产品在真实振动环境下的失效情况.通过两个案例分别对均方根值(RMS,Root Mean Square)不随时间变化和随时间变化的非高斯随机振动进行了模拟方法研究.案例1利用Hermite多项式法对高斯信号进行了转换,在保证功率谱密度不变的同时得到了具有指定峭度的RMS不随时间变化的非高斯信号,但该方法对于输入的峭度有限制,当输入峭度大于10时,误差达到了20%.案例2利用一种新方法对实测的RMS随时间变化的非高斯振动进行了模拟,模拟后得到的非高斯信号和实测信号具有相同的功率谱密度、峭度以及概率分布,验证了新方法的准确性.展开更多
An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-al...An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-algorithms.One is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability（DLD-ECCP）.DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes.Through simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.展开更多
以对铁道车辆轴箱振动非高斯特征与分布为对象开展研究。基于列车线路轴箱实测加速度信号,提取由轨道冲击引起的轴箱振动特征非高斯信号。使用多个概率密度函数(Probability Density Function,PDF)模型对实测信号进行拟合,并与实测特征...以对铁道车辆轴箱振动非高斯特征与分布为对象开展研究。基于列车线路轴箱实测加速度信号,提取由轨道冲击引起的轴箱振动特征非高斯信号。使用多个概率密度函数(Probability Density Function,PDF)模型对实测信号进行拟合,并与实测特征信号的经验分布进行对比,评估各模型对轴箱特征非高斯信号的拟合精度。基于W-H非线性变换模型,建立一种非高斯信号模拟方法。利用模拟信号分析非高斯特征对各模型拟合精度的影响。结果表明:列车在行驶过程中具有非高斯特征,当列车经过轨道焊接接头、道岔与波磨路段时,由于轮轨冲击,非高斯特征明显增大,车轮多边形对信号非高斯特征几乎没有影响;基于W-H模型的非线性变换法,可以在保证模拟信号功率谱与指定功率谱基本一致的情况下,进行不同非高斯特征的信号模拟;高斯混合模型能够对铁道车辆非高斯信号较为准确地拟合;随着模拟非高斯信号峭度与偏度的增大,各模型与经验分布的相对误差也会增大,其中高斯混合模型拟合精度相对较高。展开更多
To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming c...To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.展开更多
文摘随机振动台根据功率谱密度(PSD,Power Spectral Density)生成的信号为高斯信号,而实际振动环境有时是非高斯的,因此随机振动实验常常无法准确模拟产品在真实振动环境下的失效情况.通过两个案例分别对均方根值(RMS,Root Mean Square)不随时间变化和随时间变化的非高斯随机振动进行了模拟方法研究.案例1利用Hermite多项式法对高斯信号进行了转换,在保证功率谱密度不变的同时得到了具有指定峭度的RMS不随时间变化的非高斯信号,但该方法对于输入的峭度有限制,当输入峭度大于10时,误差达到了20%.案例2利用一种新方法对实测的RMS随时间变化的非高斯振动进行了模拟,模拟后得到的非高斯信号和实测信号具有相同的功率谱密度、峭度以及概率分布,验证了新方法的准确性.
基金supported by the Major State Basic Research Program of China(B1420080204)National Science Fund for Distinguished Young Scholars(60725415)the National Natural Science Foundation of China(60606006)
文摘An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-algorithms.One is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability（DLD-ECCP）.DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes.Through simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.
文摘以对铁道车辆轴箱振动非高斯特征与分布为对象开展研究。基于列车线路轴箱实测加速度信号,提取由轨道冲击引起的轴箱振动特征非高斯信号。使用多个概率密度函数(Probability Density Function,PDF)模型对实测信号进行拟合,并与实测特征信号的经验分布进行对比,评估各模型对轴箱特征非高斯信号的拟合精度。基于W-H非线性变换模型,建立一种非高斯信号模拟方法。利用模拟信号分析非高斯特征对各模型拟合精度的影响。结果表明:列车在行驶过程中具有非高斯特征,当列车经过轨道焊接接头、道岔与波磨路段时,由于轮轨冲击,非高斯特征明显增大,车轮多边形对信号非高斯特征几乎没有影响;基于W-H模型的非线性变换法,可以在保证模拟信号功率谱与指定功率谱基本一致的情况下,进行不同非高斯特征的信号模拟;高斯混合模型能够对铁道车辆非高斯信号较为准确地拟合;随着模拟非高斯信号峭度与偏度的增大,各模型与经验分布的相对误差也会增大,其中高斯混合模型拟合精度相对较高。
基金supported by the National Natural Science Foundation of China(U19B2016)Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University。
文摘To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.
