In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years,the research on the unsupervised classification of flight states for hypersonic targets is carri...In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years,the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper,which is based on the Hyperspectral Features(HFs)of hypersonic targets covered with plasma sheath during high-speed flight.First,a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs.Then,the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar.Finally,an unsupervised classification method based on the Density Peak Clustering(DPC)for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy.The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set.The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy(OA),Average Accuracy(AA)and Kappa coefficient.展开更多
The Tracking,Telemetering,and Command(TT&C)communication of spacecraft faces severe limitations during reentry.The Electromagnetic(EM)propagation channel is influenced by changes in the flight state and fluid dyna...The Tracking,Telemetering,and Command(TT&C)communication of spacecraft faces severe limitations during reentry.The Electromagnetic(EM)propagation channel is influenced by changes in the flight state and fluid dynamics.In addition to the high-speed spatial channel,the Plasma Sheath Channel(PSC),which covers the surface of spacecraft,exhibits unique nonstationary and highly dynamic time-varying characteristics.The absence of a channel model for the reentry process of spacecraft negatively impacts the quality evaluation and system design of TT&C communication.This paper summarizes the methods for nonstationary PSC modeling,involving calculation methods for attenuation and phase shift in the channel,as well as a multistate nonstationary channel statistical model based on a hidden Markov process.Furthermore,the correlation between amplitude variation and phase variation with electron density is analyzed.The paper also provides statistical characteristics of the channel and channel parameters at typical frequencies in the S/C/X/Ka bands.These findings serve as a reference for the design and evaluation of TT&C communication systems for reentry spacecraft.Finally,the paper discusses the prospects of various aspects related to the channel characteristics and modeling of spacecraft PSCs in the shortwave and terahertz/laser bands,as well as the potential challenges in cross-domain spacecraft channel sensing and prediction.展开更多
The rapid advancement of hypersonic targets presents significant challenges to aerospace defense systems.Recognition methods based on multi-band radiation spectral features offer a promising solution for hypersonic ve...The rapid advancement of hypersonic targets presents significant challenges to aerospace defense systems.Recognition methods based on multi-band radiation spectral features offer a promising solution for hypersonic vehicle identification.However,existing techniques struggle with the incremental recognition of new target classes and are vulnerable to catastrophic forgetting.To address these limitations,this paper introduces the Incremental Dense Net and Spectral Angle Learning Method(IDSALM)for hypersonic target incremental recognition.IDSALM combines Dense Net-based feature extraction with dynamic classification optimization,utilizing the Dynamic Adaptive Spectral Angle Classifier(DASA-Classifier)to address intraclass variability and interclass ambiguity through spectral angle metrics,dynamically adapting to changes in target distributions.The Selective Retention and Exemplar Management(SREM)module mitigates catastrophic forgetting and improves model update efficiency through knowledge replay and selection of representative exemplars.In the four-stage incremental experiments,with the addition of two new classes per stage,IDSALM achieves an Average Accuracy(AA)exceeding 94%and a Kappa coefficient surpassing 0.93,significantly reducing recognition confusion and catastrophic forgetting compared to alternative methods.Furthermore,IDSALM improves model update efficiency,reducing the update time to just 3.74%of that of non-incremental methods.These results demonstrate IDSALM's efficiency and robustness,establishing a solid technological foundation for space-based multi-band spectral recognition of hypersonic targets.展开更多
电化学一氧化氮传感器能够实时监测颅内一氧化氮浓度,对于了解大脑中一氧化氮的功能至关重要.然而,在大脑中使用的传统刚性传感电极面临着灵敏度低和植入后神经炎症引起一氧化氮浓度异常的问题.在这里,我们报道了一种结合物理和化学吸...电化学一氧化氮传感器能够实时监测颅内一氧化氮浓度,对于了解大脑中一氧化氮的功能至关重要.然而,在大脑中使用的传统刚性传感电极面临着灵敏度低和植入后神经炎症引起一氧化氮浓度异常的问题.在这里,我们报道了一种结合物理和化学吸附能力、具有高灵敏度和准确性的电化学一氧化氮传感器.其对一氧化氮的物理和化学吸附能力分别来自于电极的高比表面积和丰富的羧基官能团.此外,柔软的电极可以与脑组织的力学性能相匹配,实现了一个高度适应的电极/组织界面.由此设计的颅内一氧化氮传感器表现出迄今为止所报道文献中最高的灵敏度,为3245 pA nmol^(-1)L,检测限为0.1 nmol L^(-1).电极在植入后未观察到显著的炎症反应以及过量的一氧化氮表达,提高了检测的准确性.该传感器成功捕捉了大脑中的一氧化氮波动,并实现了对多个脑区的同时检测,促进了对大脑中一氧化氮生理病理作用的研究.展开更多
基金funded by the National Natural Science Foundation of China(Nos.61871302,62101406,and 62001340)the Innovation Capability Support Program of Shaanxi,China(No.2022TD-37)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.JB211311)the Innovation Fund of Xidian University,China(No.YJS2217).
文摘In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years,the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper,which is based on the Hyperspectral Features(HFs)of hypersonic targets covered with plasma sheath during high-speed flight.First,a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs.Then,the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar.Finally,an unsupervised classification method based on the Density Peak Clustering(DPC)for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy.The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set.The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy(OA),Average Accuracy(AA)and Kappa coefficient.
基金supported by the National Natural Science Foundation of China(Nos.62371375,62371372,62101406 and 62001340)Innovation Capability Support Program of Shaanxi,China(No.2022TD-37)the Fundamental Research Funds for Central Universities,China(Nos.XJS221302 and QTZX23060).
文摘The Tracking,Telemetering,and Command(TT&C)communication of spacecraft faces severe limitations during reentry.The Electromagnetic(EM)propagation channel is influenced by changes in the flight state and fluid dynamics.In addition to the high-speed spatial channel,the Plasma Sheath Channel(PSC),which covers the surface of spacecraft,exhibits unique nonstationary and highly dynamic time-varying characteristics.The absence of a channel model for the reentry process of spacecraft negatively impacts the quality evaluation and system design of TT&C communication.This paper summarizes the methods for nonstationary PSC modeling,involving calculation methods for attenuation and phase shift in the channel,as well as a multistate nonstationary channel statistical model based on a hidden Markov process.Furthermore,the correlation between amplitude variation and phase variation with electron density is analyzed.The paper also provides statistical characteristics of the channel and channel parameters at typical frequencies in the S/C/X/Ka bands.These findings serve as a reference for the design and evaluation of TT&C communication systems for reentry spacecraft.Finally,the paper discusses the prospects of various aspects related to the channel characteristics and modeling of spacecraft PSCs in the shortwave and terahertz/laser bands,as well as the potential challenges in cross-domain spacecraft channel sensing and prediction.
基金funded by the National Natural Science Foundation of China(Nos.62371375,62371372 and 52305128)the Innovation Capability Support Program of Shaanxi,China(No.2022TD-37)+3 种基金the Shaanxi Province Funds for Distinguished Young Youths,China(No.S2025-JC-JQ-0103)the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-274)the Fundamental Research Funds for Central Universities,China(Nos.QTZX23060 and XJS221302)the Innovation Fund of Xidian University,China(No.YJSJ25019)。
文摘The rapid advancement of hypersonic targets presents significant challenges to aerospace defense systems.Recognition methods based on multi-band radiation spectral features offer a promising solution for hypersonic vehicle identification.However,existing techniques struggle with the incremental recognition of new target classes and are vulnerable to catastrophic forgetting.To address these limitations,this paper introduces the Incremental Dense Net and Spectral Angle Learning Method(IDSALM)for hypersonic target incremental recognition.IDSALM combines Dense Net-based feature extraction with dynamic classification optimization,utilizing the Dynamic Adaptive Spectral Angle Classifier(DASA-Classifier)to address intraclass variability and interclass ambiguity through spectral angle metrics,dynamically adapting to changes in target distributions.The Selective Retention and Exemplar Management(SREM)module mitigates catastrophic forgetting and improves model update efficiency through knowledge replay and selection of representative exemplars.In the four-stage incremental experiments,with the addition of two new classes per stage,IDSALM achieves an Average Accuracy(AA)exceeding 94%and a Kappa coefficient surpassing 0.93,significantly reducing recognition confusion and catastrophic forgetting compared to alternative methods.Furthermore,IDSALM improves model update efficiency,reducing the update time to just 3.74%of that of non-incremental methods.These results demonstrate IDSALM's efficiency and robustness,establishing a solid technological foundation for space-based multi-band spectral recognition of hypersonic targets.
基金financially supported by the National Natural Science Foundation of China (22175086, 22005137, 22205098, and 82201992)the Natural Science Foundation of Jiangsu Province (BK20200321 and BK20210681)+5 种基金the Postdoctoral Research Foundation of Jiangsu Province (2021K007A)China Postdoctoral Science Foundation (2021M700067)the National Postdoctoral Program for Innovative Talents (BX20200161)the Program for Innovative Talents and Entrepreneurs in Jiangsu (JSSCTD202138)the Fundamental Research Funds for the Central Universities (021314380234)the Natural Science Foundation of Nanjing University of Chinese Medicine (XPT82201992)。
文摘电化学一氧化氮传感器能够实时监测颅内一氧化氮浓度,对于了解大脑中一氧化氮的功能至关重要.然而,在大脑中使用的传统刚性传感电极面临着灵敏度低和植入后神经炎症引起一氧化氮浓度异常的问题.在这里,我们报道了一种结合物理和化学吸附能力、具有高灵敏度和准确性的电化学一氧化氮传感器.其对一氧化氮的物理和化学吸附能力分别来自于电极的高比表面积和丰富的羧基官能团.此外,柔软的电极可以与脑组织的力学性能相匹配,实现了一个高度适应的电极/组织界面.由此设计的颅内一氧化氮传感器表现出迄今为止所报道文献中最高的灵敏度,为3245 pA nmol^(-1)L,检测限为0.1 nmol L^(-1).电极在植入后未观察到显著的炎症反应以及过量的一氧化氮表达,提高了检测的准确性.该传感器成功捕捉了大脑中的一氧化氮波动,并实现了对多个脑区的同时检测,促进了对大脑中一氧化氮生理病理作用的研究.
