针对飞行器遥测振动信号具有典型的非平稳、非线性及强噪声干扰的特征,提出了一种变步长KLMS遥测振动信号降噪方法。首先利用遥测时序分析法将遥测振动信号序列分为平稳飞行段落和特征飞行段落,以平稳飞行段落采集信号作为参考噪声,实...针对飞行器遥测振动信号具有典型的非平稳、非线性及强噪声干扰的特征,提出了一种变步长KLMS遥测振动信号降噪方法。首先利用遥测时序分析法将遥测振动信号序列分为平稳飞行段落和特征飞行段落,以平稳飞行段落采集信号作为参考噪声,实现对特征飞行段落的噪声对消,解决了单通道遥测振动信号参考噪声来源问题。噪声对消采用变步长核最小均方误差算法(Kernel Least Mean Square,KLMS),以保证在非平稳非线性信号条件下算法的有效性。利用输出误差调整学习步长,进一步提高算法的降噪性能。仿真信号和实测数据处理结果证明了算法的有效性。展开更多
Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive...Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.展开更多
With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued no...With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.展开更多
为了扩展高频水声换能器带宽,添加背衬是方法之一,然而针对水声换能器的激励信号形式和工作状态,当前缺少针对水声换能器背衬设计的准则,本文开展了水声换能器背衬参数的作用机制研究工作。基于KLM (krimholtz leedom and matthaei)传...为了扩展高频水声换能器带宽,添加背衬是方法之一,然而针对水声换能器的激励信号形式和工作状态,当前缺少针对水声换能器背衬设计的准则,本文开展了水声换能器背衬参数的作用机制研究工作。基于KLM (krimholtz leedom and matthaei)传输线模型研究了背衬厚度、衰减系数及特征阻抗对水声换能器性能的影响。研究结果表明:背衬在水声换能器中作为谐振体的一部分参与振动并形成了若干阶纵向振动模态,其作用是增加系统阻尼,而不是吸收声波。从扩展带宽的角度出发,背衬的衰减系数应尽量大,此时,发送电压响应波动减弱且对响应幅值影响很小;背衬特征阻抗能够调节两响应峰的相对幅值,阻抗过高或者过低都不利于宽带的形成,因此选择中等特征阻抗的背衬较为适宜。最后,基于上述的背衬优化方法,制备了水声换能器样机,其测试带宽为102%,与理论吻合较好。展开更多
In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzi...In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.展开更多
The reccnt Alcatel Lucent failure is an example of how many mergers run into considerable implementing difficulties in the months following their launch. Despite promising perspectives,no coherent whole seems to come ...The reccnt Alcatel Lucent failure is an example of how many mergers run into considerable implementing difficulties in the months following their launch. Despite promising perspectives,no coherent whole seems to come out of the merger,just as anticipated synergies have a hard time materializing.展开更多
By striving to attain excellence as an airline and by participating in the world's most successful airline alliance,KLM intends to generate value for its customers,employees and shareholders.
文摘针对飞行器遥测振动信号具有典型的非平稳、非线性及强噪声干扰的特征,提出了一种变步长KLMS遥测振动信号降噪方法。首先利用遥测时序分析法将遥测振动信号序列分为平稳飞行段落和特征飞行段落,以平稳飞行段落采集信号作为参考噪声,实现对特征飞行段落的噪声对消,解决了单通道遥测振动信号参考噪声来源问题。噪声对消采用变步长核最小均方误差算法(Kernel Least Mean Square,KLMS),以保证在非平稳非线性信号条件下算法的有效性。利用输出误差调整学习步长,进一步提高算法的降噪性能。仿真信号和实测数据处理结果证明了算法的有效性。
基金National Natural Science Foundation of China(No.51467008)
文摘Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.
基金supported by the National Natural Science Foundation of China(6100115361271415+4 种基金6140149961531015)the Fundamental Research Funds for the Central Universities(3102014JCQ010103102014ZD0041)the Opening Research Foundation of State Key Laboratory of Underwater Information Processing and Control(9140C231002130C23085)
文摘With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.
文摘为了扩展高频水声换能器带宽,添加背衬是方法之一,然而针对水声换能器的激励信号形式和工作状态,当前缺少针对水声换能器背衬设计的准则,本文开展了水声换能器背衬参数的作用机制研究工作。基于KLM (krimholtz leedom and matthaei)传输线模型研究了背衬厚度、衰减系数及特征阻抗对水声换能器性能的影响。研究结果表明:背衬在水声换能器中作为谐振体的一部分参与振动并形成了若干阶纵向振动模态,其作用是增加系统阻尼,而不是吸收声波。从扩展带宽的角度出发,背衬的衰减系数应尽量大,此时,发送电压响应波动减弱且对响应幅值影响很小;背衬特征阻抗能够调节两响应峰的相对幅值,阻抗过高或者过低都不利于宽带的形成,因此选择中等特征阻抗的背衬较为适宜。最后,基于上述的背衬优化方法,制备了水声换能器样机,其测试带宽为102%,与理论吻合较好。
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1I1A3058103,2019R1A2C1002525).
文摘In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.
文摘The reccnt Alcatel Lucent failure is an example of how many mergers run into considerable implementing difficulties in the months following their launch. Despite promising perspectives,no coherent whole seems to come out of the merger,just as anticipated synergies have a hard time materializing.
文摘By striving to attain excellence as an airline and by participating in the world's most successful airline alliance,KLM intends to generate value for its customers,employees and shareholders.
