Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system....Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system.It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum.Therefore,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results.Waveform fidelity is usually employed to evaluate the distortion of an antenna.However,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP sensors.In this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual waveforms.The system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite functions.Distortion of a sensor vs.frequency is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the distortion.Measurement of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.展开更多
Massive neutrinos are expected to affect the large-scale structure formation,including the major component of solid substances,dark matter halos.How halos are influenced by neutrinos is vital and interesting,and angul...Massive neutrinos are expected to affect the large-scale structure formation,including the major component of solid substances,dark matter halos.How halos are influenced by neutrinos is vital and interesting,and angular momentum(AM)as a significant feature provides a statistical perspective for this issue.Exploring halos from TianNu N-body cosmological simulation with the co-evolving neutrino particles,we obtain some concrete conclusions.First,by comparing the same halos with and without neutrinos,in contrast to the neutrino-free case,over 89.71%of halos have smaller halo moduli,over 71.06%have smaller particle-mass-reduced(PMR)AM moduli,and over 95.44%change their orientations of less than 0°.65.Moreover,the relative variation of PMR modulus is more visible for low-mass halos.Second,to explore the PMR moduli of halos in dense or sparse areas,we divide the whole box into big cubes,and search for halos within a small spherical cell in a single cube.From the two-level divisions,we discover that in denser cubes,the variation of PMR moduli with massive neutrinos decreases more significantly.This distinction suggests that neutrinos exert heavier influence on halos'moduli in compact regions.With massive neutrinos,most halos(86.60%)have lower masses than without neutrinos.展开更多
Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network har...Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network hardening solution. Numerous attack surface models have been proposed in the past decade,but they are not appropriate for describing complex systems with heterogeneous components. To address this limitation, we propose to use a two-layer Hierarchical Attack Surface Network(HASN) that models the data interactions and resource distribution of the system in a component-oriented view. First, we formally define the HASN by extending the entry point and exit point framework. Second, in order to assess data input risk and output risk on the HASN, we propose two behaviour models and two simulation-based risk metrics. Last, we conduct experiments for three network systems. Our experimental results show that the proposed approach is applicable and effective.展开更多
Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield env...Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield environment simulation system and command and control system is still imperfect, and the traditional simulation data model cannot meet war fighters’ high-efficient and accurate understanding and analysis on battlefield environment’s information. Therefore, a kind of task-orientated battlefield environment simulation process model needs to be construed to effectively analyze the key information demands of the command and control system. The structured characteristics of tasks and simulation process are analyzed, and the simulation process concept model is constructed with the method of object-orientated. The data model and formal syntax of GeoBML are analyzed, and the logical model of simulation process is constructed with formal language. The object data structure of simulation process is defined and the object model of simulation process which maps tasks is constructed. In the end, the battlefield environment simulation platform modules are designed and applied based on this model, verifying that the model can effectively express the real-time dynamic correlation between battlefield environment simulation data and operational tasks.展开更多
1 Introduction In the financial market,the partial correlation is a technical tool for studying the relationships between stocks,pairs trading,and industry classification[1,2].Many of these studies[2-4]focus on tradit...1 Introduction In the financial market,the partial correlation is a technical tool for studying the relationships between stocks,pairs trading,and industry classification[1,2].Many of these studies[2-4]focus on traditional statistical methods,which solve the partial correlation of stocks with a large number of observation samples and a relatively small number of stocks.For example,Jung and Chang[2]used 10 years of daily-level data to calculate the partial correlation matrix of 300 stocks.展开更多
Under low signal-to-noise ratio(SNR)and burst noise conditions,the speech enhancement effect of existing deep learning network models is not satisfactory.In contrast,humans can exploit the long-term correlation of spe...Under low signal-to-noise ratio(SNR)and burst noise conditions,the speech enhancement effect of existing deep learning network models is not satisfactory.In contrast,humans can exploit the long-term correlation of speech to form an integrated perception of different speech signals.Thus,describing the long-term dependencies of speech can help improve the enhancement performance under low SNR and burst noise conditions.Inspired by this feature,a time domain end-to-end monaural speech enhancement model TU-net that fuses the multi-head self-attention mechanism and U-net deep network is proposed.The TU-net model adopts the codec layer structure of U-net to achieve multi-scale feature fusion.It introduces the dual-path Transformer module using the multi-head self-attention mechanism to calculate the speech mask and better model long-term correlation.The TU-net model is trained with a weighted sum loss function in the time,time-frequency,and perceptual domains.Simulation experiments are carried out and the results show that with maintaining relatively fewer network model parameters,TU-net outperforms other similar monaural enhancement network models in several evaluation metrics such as perceptual evaluation of speech quality(PESQ),short-time objective intelligibility(STOI)and SNR gain under low SNR and burst noise conditions.展开更多
基金Research Project of High-Level Talents of Jiangsu Police Institute(No.2911118010).
文摘Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system.It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum.Therefore,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results.Waveform fidelity is usually employed to evaluate the distortion of an antenna.However,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP sensors.In this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual waveforms.The system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite functions.Distortion of a sensor vs.frequency is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the distortion.Measurement of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.
基金supported by the National Natural Science Foundation of China(grant Nos.11929301 and 61802428)。
文摘Massive neutrinos are expected to affect the large-scale structure formation,including the major component of solid substances,dark matter halos.How halos are influenced by neutrinos is vital and interesting,and angular momentum(AM)as a significant feature provides a statistical perspective for this issue.Exploring halos from TianNu N-body cosmological simulation with the co-evolving neutrino particles,we obtain some concrete conclusions.First,by comparing the same halos with and without neutrinos,in contrast to the neutrino-free case,over 89.71%of halos have smaller halo moduli,over 71.06%have smaller particle-mass-reduced(PMR)AM moduli,and over 95.44%change their orientations of less than 0°.65.Moreover,the relative variation of PMR modulus is more visible for low-mass halos.Second,to explore the PMR moduli of halos in dense or sparse areas,we divide the whole box into big cubes,and search for halos within a small spherical cell in a single cube.From the two-level divisions,we discover that in denser cubes,the variation of PMR moduli with massive neutrinos decreases more significantly.This distinction suggests that neutrinos exert heavier influence on halos'moduli in compact regions.With massive neutrinos,most halos(86.60%)have lower masses than without neutrinos.
基金supported by the Jiangsu Provincial Natural Science Foundation of China(no.BK20150721)the 2017 National Key Research and Development Program of China(no.2017YFB0802900)
文摘Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network hardening solution. Numerous attack surface models have been proposed in the past decade,but they are not appropriate for describing complex systems with heterogeneous components. To address this limitation, we propose to use a two-layer Hierarchical Attack Surface Network(HASN) that models the data interactions and resource distribution of the system in a component-oriented view. First, we formally define the HASN by extending the entry point and exit point framework. Second, in order to assess data input risk and output risk on the HASN, we propose two behaviour models and two simulation-based risk metrics. Last, we conduct experiments for three network systems. Our experimental results show that the proposed approach is applicable and effective.
基金The National Natural Science Foundation of China(41271393).
文摘Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield environment simulation system and command and control system is still imperfect, and the traditional simulation data model cannot meet war fighters’ high-efficient and accurate understanding and analysis on battlefield environment’s information. Therefore, a kind of task-orientated battlefield environment simulation process model needs to be construed to effectively analyze the key information demands of the command and control system. The structured characteristics of tasks and simulation process are analyzed, and the simulation process concept model is constructed with the method of object-orientated. The data model and formal syntax of GeoBML are analyzed, and the logical model of simulation process is constructed with formal language. The object data structure of simulation process is defined and the object model of simulation process which maps tasks is constructed. In the end, the battlefield environment simulation platform modules are designed and applied based on this model, verifying that the model can effectively express the real-time dynamic correlation between battlefield environment simulation data and operational tasks.
基金supported by the General Financial Grant from Jiangsu Natural Science Foundation for Youth in 2014(BK20140075).
文摘1 Introduction In the financial market,the partial correlation is a technical tool for studying the relationships between stocks,pairs trading,and industry classification[1,2].Many of these studies[2-4]focus on traditional statistical methods,which solve the partial correlation of stocks with a large number of observation samples and a relatively small number of stocks.For example,Jung and Chang[2]used 10 years of daily-level data to calculate the partial correlation matrix of 300 stocks.
基金supported by the National Natural Science Foundation of China(62071484)。
文摘Under low signal-to-noise ratio(SNR)and burst noise conditions,the speech enhancement effect of existing deep learning network models is not satisfactory.In contrast,humans can exploit the long-term correlation of speech to form an integrated perception of different speech signals.Thus,describing the long-term dependencies of speech can help improve the enhancement performance under low SNR and burst noise conditions.Inspired by this feature,a time domain end-to-end monaural speech enhancement model TU-net that fuses the multi-head self-attention mechanism and U-net deep network is proposed.The TU-net model adopts the codec layer structure of U-net to achieve multi-scale feature fusion.It introduces the dual-path Transformer module using the multi-head self-attention mechanism to calculate the speech mask and better model long-term correlation.The TU-net model is trained with a weighted sum loss function in the time,time-frequency,and perceptual domains.Simulation experiments are carried out and the results show that with maintaining relatively fewer network model parameters,TU-net outperforms other similar monaural enhancement network models in several evaluation metrics such as perceptual evaluation of speech quality(PESQ),short-time objective intelligibility(STOI)and SNR gain under low SNR and burst noise conditions.
