Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM...A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM using an unequal power distribution strategy.In addition,a reduced complexity selective mapping (RC-SLM) scheme was proposed.The proposed scheme is based on partitioning the frequency domain symbol sequence into several sub-blocks,and then each sub-block is multiplied by different phase sequences whose length is shorter than that used in the conventional SLM scheme.Then,a kind of low complexity conversions is used to replace the IFFT blocks.The performance of the proposed RC-SLM scheme along with the new approach was studied with computer simulation.The obtained results show that the proposed RC-SLM scheme is able to achieve the lowest computational complexity when compared with other low complexity schemes proposed in the literature while at the same time improves the PAPR reduction performance by about 0.3 dB.展开更多
为了降低正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中传统选择性映射(conventional selected mapping,CSLM)算法的计算复杂度,提高系统的频谱利用效率,提出了一种基于盲检测的低复杂度分块选择性映射(block s...为了降低正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中传统选择性映射(conventional selected mapping,CSLM)算法的计算复杂度,提高系统的频谱利用效率,提出了一种基于盲检测的低复杂度分块选择性映射(block selected mapping,BSLM)算法,发送端利用逆快速傅里叶反变换(inverse fast fourier transform,IFFT)性质仅需少量低维IFFT运算即可获得较多的备选序列,接收端采用低复杂度的盲检测方式。仿真分析了所提算法的峰均功率比(peak to average power ratio,PAPR)、立方度量(cubic metric,CM)和误比特率(bit error rate,BER)性能。结果表明,所提算法不仅明显降低了计算复杂度,而且有效抑制了OFDM信号的PAPR和CM,获得与已知边带信息的CSLM算法相近的BER性能。展开更多
为了降低空频分组编码的多输入多输出正交频分复用(Space Frequency Block Coding Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing,SFBC MIMO-OFDM)系统中传统选择性映射(Selected Mapping,SLM)算法的计...为了降低空频分组编码的多输入多输出正交频分复用(Space Frequency Block Coding Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing,SFBC MIMO-OFDM)系统中传统选择性映射(Selected Mapping,SLM)算法的计算复杂度,本文提出了结合时域信号的循环移位和等效SFBC编码来产生更多具有不同峰均功率比(Peak to Average Power Ratio,PAPR)的备选序列的方法.接收端通过比较反向旋转序列与最近星座点的距离来恢复出循环移位因子和相位旋转因子,从而实现接收信号的盲检测.仿真结果表明,本文提出方法能有效地抑制SFBC MIMO-OFDM系统的PAPR.另外,本文提出方法明显降低了传统SLM算法的计算复杂度,而且可以获得与传统SLM算法在已知边带副信息情况下相似的比特误码率(Bit Error Rate,BER)性能.展开更多
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
文摘A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM using an unequal power distribution strategy.In addition,a reduced complexity selective mapping (RC-SLM) scheme was proposed.The proposed scheme is based on partitioning the frequency domain symbol sequence into several sub-blocks,and then each sub-block is multiplied by different phase sequences whose length is shorter than that used in the conventional SLM scheme.Then,a kind of low complexity conversions is used to replace the IFFT blocks.The performance of the proposed RC-SLM scheme along with the new approach was studied with computer simulation.The obtained results show that the proposed RC-SLM scheme is able to achieve the lowest computational complexity when compared with other low complexity schemes proposed in the literature while at the same time improves the PAPR reduction performance by about 0.3 dB.
文摘为了降低正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中传统选择性映射(conventional selected mapping,CSLM)算法的计算复杂度,提高系统的频谱利用效率,提出了一种基于盲检测的低复杂度分块选择性映射(block selected mapping,BSLM)算法,发送端利用逆快速傅里叶反变换(inverse fast fourier transform,IFFT)性质仅需少量低维IFFT运算即可获得较多的备选序列,接收端采用低复杂度的盲检测方式。仿真分析了所提算法的峰均功率比(peak to average power ratio,PAPR)、立方度量(cubic metric,CM)和误比特率(bit error rate,BER)性能。结果表明,所提算法不仅明显降低了计算复杂度,而且有效抑制了OFDM信号的PAPR和CM,获得与已知边带信息的CSLM算法相近的BER性能。
文摘为了降低空频分组编码的多输入多输出正交频分复用(Space Frequency Block Coding Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing,SFBC MIMO-OFDM)系统中传统选择性映射(Selected Mapping,SLM)算法的计算复杂度,本文提出了结合时域信号的循环移位和等效SFBC编码来产生更多具有不同峰均功率比(Peak to Average Power Ratio,PAPR)的备选序列的方法.接收端通过比较反向旋转序列与最近星座点的距离来恢复出循环移位因子和相位旋转因子,从而实现接收信号的盲检测.仿真结果表明,本文提出方法能有效地抑制SFBC MIMO-OFDM系统的PAPR.另外,本文提出方法明显降低了传统SLM算法的计算复杂度,而且可以获得与传统SLM算法在已知边带副信息情况下相似的比特误码率(Bit Error Rate,BER)性能.
