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
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
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）.
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.
