Atmospheric particulate matter pollution has attracted much wider attention globally.In recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source a...Atmospheric particulate matter pollution has attracted much wider attention globally.In recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques.Such demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar source.In this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source contribution.The proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration modules.Benefited from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear way.The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the restraints.Additionally,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.展开更多
Superconducting quantum interference devices(SQUIDs) are low-noise amplifiers that are essential for the readouts of translation edge sensors(TESs). The linear flux range is an important parameter for SQUID amplifiers...Superconducting quantum interference devices(SQUIDs) are low-noise amplifiers that are essential for the readouts of translation edge sensors(TESs). The linear flux range is an important parameter for SQUID amplifiers, especially those controlled by high-bandwidth digital flux-locked-loop circuits. A large linear flux range conduces to accurately measuring the input signal and also increasing the multiplexing factor in the time-division multiplexed(TDM) readout scheme of the TES array. In this work, we report that the linear flux range of an SQUID can be improved by using self-feedback effect. When the SQUID loop is designed to be asymmetric, a voltage-biased SQUID shows an asymmetric current–flux(I–Φ) response curve. The linear flux range is improved along the I–Φ curve with a shallow slope. The experimental results accord well with the numerical simulations. The asymmetric SQUID will be able to serve as a building block in the development of the TDM readout systems for large TES arrays.展开更多
The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remot...The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.展开更多
针对监督学习方法采集攻击样本困难以及无监督学习方法检测精度不足的问题,提出一种融合自监督学习与主动学习的域名系统(domain name system,DNS)隧道检测方法。该方法采用异常检测框架,无需获取攻击样本,同时,通过自监督学习引入训练...针对监督学习方法采集攻击样本困难以及无监督学习方法检测精度不足的问题,提出一种融合自监督学习与主动学习的域名系统(domain name system,DNS)隧道检测方法。该方法采用异常检测框架,无需获取攻击样本,同时,通过自监督学习引入训练指导过程,通过主动学习引入反馈调节过程,显著提升了检测精度。构建基于Transformer架构的自编码器,通过对正常样本特征进行自监督学习,实现了DNS数据包级别的异常检测。以此为基础,将主动学习方法应用于反馈引导的孤立森林(feedback-guided isolated forest,FBIF),实现了DNS交互流级别的异常检测,将检出的异常流视为与隧道攻击活动相关。实验结果表明,该检测方法在无需获取攻击样本的前提下,能准确检测出多种类型的隧道攻击,且在资源消耗方面具备高可扩展性。展开更多
基金supported by Key Laboratory For Environmental Factors Control of Agro-product Quality Safety,Ministry of Agriculture and Rural Affairs(No.2018hjyzkfkt-002)Qian Xuesen Laboratory of Space Technology,CAST(No.GZZKFJJ2020002)National Research Program for Key Issues in Air Pollution Control(No.DQGG-05-30)
文摘Atmospheric particulate matter pollution has attracted much wider attention globally.In recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques.Such demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar source.In this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source contribution.The proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration modules.Benefited from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear way.The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the restraints.Additionally,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.
基金Project supported by the Fund from China National Space Administration (CNSA) (Grant No. D050104)the Fund for Low Energy Gamma Ray Detection Research Based on SQUID Techniquethe Superconducting Electronics Facility (SELF) of Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences。
文摘Superconducting quantum interference devices(SQUIDs) are low-noise amplifiers that are essential for the readouts of translation edge sensors(TESs). The linear flux range is an important parameter for SQUID amplifiers, especially those controlled by high-bandwidth digital flux-locked-loop circuits. A large linear flux range conduces to accurately measuring the input signal and also increasing the multiplexing factor in the time-division multiplexed(TDM) readout scheme of the TES array. In this work, we report that the linear flux range of an SQUID can be improved by using self-feedback effect. When the SQUID loop is designed to be asymmetric, a voltage-biased SQUID shows an asymmetric current–flux(I–Φ) response curve. The linear flux range is improved along the I–Φ curve with a shallow slope. The experimental results accord well with the numerical simulations. The asymmetric SQUID will be able to serve as a building block in the development of the TDM readout systems for large TES arrays.
基金Acknowledgements： This work is supported by A Foundation of National Excellent Doctoral Dissertation of China （No. 200250）, Natural Science Foundation of Henan Province China （No. 411012400） and National Science Foundation of China （No. 60871080）.
文摘The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.
文摘针对监督学习方法采集攻击样本困难以及无监督学习方法检测精度不足的问题,提出一种融合自监督学习与主动学习的域名系统(domain name system,DNS)隧道检测方法。该方法采用异常检测框架,无需获取攻击样本,同时,通过自监督学习引入训练指导过程,通过主动学习引入反馈调节过程,显著提升了检测精度。构建基于Transformer架构的自编码器,通过对正常样本特征进行自监督学习,实现了DNS数据包级别的异常检测。以此为基础,将主动学习方法应用于反馈引导的孤立森林(feedback-guided isolated forest,FBIF),实现了DNS交互流级别的异常检测,将检出的异常流视为与隧道攻击活动相关。实验结果表明,该检测方法在无需获取攻击样本的前提下,能准确检测出多种类型的隧道攻击,且在资源消耗方面具备高可扩展性。
