Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries,...Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries, the introduction of noise, and the size of the matrix, the resulting eigenvalue and eigenvector distributions remain relatively unchanged. However, when there are certain anomalous eigenvalues and their corresponding eigenvectors that do not follow the predicted distributions, it could indicate that there’s an underlying non-random signal inside the data. As data and matrices become more important in the sciences and computing, so too will the importance of processing them with the principles of random matrix theory.展开更多
Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-In...Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed,which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.展开更多
We have applied the Random Matrix Theory in order to examine the validity of the NPT treatment in HSP. We have investigated the pathology examining the sEMG recorded signal for about eight minutes. We have performed s...We have applied the Random Matrix Theory in order to examine the validity of the NPT treatment in HSP. We have investigated the pathology examining the sEMG recorded signal for about eight minutes. We have performed standard electromyographic investigations as well as we have applied the RMT method of analysis. We have investigated the sEMG signals before and after the NPT treatment. The application of a so robust method as the RMT evidences that the NPT treatment was able to induce a net improvement of the disease respect to the pathological status before NPT.展开更多
We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fa...We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fact that the major part of the time series is random, and compare the eigenvalue spectrum of cross correlation matrix of a large set of random time series, to the spectrum derived by the random matrix theory (RMT) at the limit of large dimension (the number of independent time series) and long enough length of time series. We test this algorithm on the real tick data of American stocks at different years between 1994 and 2002 and show that the extracted principal components indeed reflects the change of leading stock sectors during this period.展开更多
Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based comp...Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.展开更多
In this work,a consistency detection method is proposed,to overcome the inconsistencies in the use of large-scale lead-carbon energy storage batteries(LCESBs)and the difficulties of large-scale detection for LCESBs.Ba...In this work,a consistency detection method is proposed,to overcome the inconsistencies in the use of large-scale lead-carbon energy storage batteries(LCESBs)and the difficulties of large-scale detection for LCESBs.Based on the chemical materials and physical mechanisms of LCESBs,the internal and external factors that affect the consistency and their characterization parameters are analyzed.The inconsistent characterization parameters,such as voltage,temperature,and resistance,are used to construct a high-dimensional random matrix and calculate the matrix eigenvalue.Single loop theorem and average spectral radius are then employed to carry out preliminary consistency detection.Next,short-term discharge experiments are conducted on individual batteries with inconsistent initial screening.The voltage and temperature data is collected,and sequential overlapping derivative(SOD)transformation is performed to extract the characteristics of voltage and temperature changes.The consistency of individual cells using the Wasserstein distance is quantitatively characterized.Finally,the reliability of the consistency detection method is evaluated by the confusion matrix.The large amounts of actual measurement data shows a false negative rate of the algorithm of 0 and an accuracy of 99.94%.This study shows that using random matrix theory for preliminary detection is suitable for processing high-dimensional data of large-scale energy storage power plants.Using SOD for precise detection can amplify the voltage,temperature,and resistance differences of inconsistent batteries,making the consistency detection more accurate.展开更多
The weighting subspace fitting(WSF)algorithm performs better than the multi-signal classification(MUSIC)algorithm in the case of low signal-to-noise ratio(SNR)and when signals are correlated.In this study,we use the r...The weighting subspace fitting(WSF)algorithm performs better than the multi-signal classification(MUSIC)algorithm in the case of low signal-to-noise ratio(SNR)and when signals are correlated.In this study,we use the random matrix theory(RMT)to improve WSF.RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate.The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance.Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory,the method of calculating WSF is obtained.Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.展开更多
Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the da...Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.展开更多
The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and ...The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and reliability of the existing schemes are vulnerable to high resistance and data errors.To improve the insufficiencies,this paper proposes a pilot protection scheme by using the random matrix for DC lines in the symmetrical bipolar MMC high-voltage direct current(HVDC)grid.Firstly,the 1-mode voltage time-domain characteristics of the line end,DC bus,and adjacent line end are analyzed by the inverse Laplace transform to find indicators of fault direction.To combine the actual model with the data-driven method,the methods to construct the data expansion matrix and to calculate additional noise are proposed.Then,the mean spectral radiuses of two random matrices are used to detect fault directions,and a novel pilot protection criterion is proposed.The protection scheme only needs to transmit logic signals,decreasing the communication burden.It performs well in high-resistance faults,abnormal data errors,measurement errors,parameters errors,and different topology conditions.Numerous simulations in PSCAD/EMTDC confirm the effectiveness and reliability of the proposed protection scheme.展开更多
Fault detection and location are critically significant applications of a supervisory control system in a smart grid.The methods,based on random matrix theory(RMT),have been practiced using measurements to detect shor...Fault detection and location are critically significant applications of a supervisory control system in a smart grid.The methods,based on random matrix theory(RMT),have been practiced using measurements to detect short circuit faults occurring on transmission lines.However,the diagnostic accuracy is infuenced by the noise signal in the measurements.The relationship between mean eigenvalue of a random matrix and noise is detected in this paper,and the defects of the Mean Spectral Radius(MSR),as an indicator to detect faults,are theoretically determined,along with a novel indicator of the shifting degree of maximum eigenvalue and its threshold.By comparing the indicator and the threshold,the occurrence of a fault can be assessed.Finally,an augmented matrix is constructed to locate the fault area.The proposed method can effectively achieve fault detection via the RMT without any influence of noise,and also does not depend on system models.The experiment results are based on the IEEE 39-bus system.Also,actual provincial grid data is applied to validate the effectiveness of the proposed method.展开更多
I discuss the results from a study of the central ^12CC collisions at 4.2 A GeV/c. The data have been analyzed using a new method based on the Random Matrix Theory. The simulation data coming from the Ultra Relativist...I discuss the results from a study of the central ^12CC collisions at 4.2 A GeV/c. The data have been analyzed using a new method based on the Random Matrix Theory. The simulation data coming from the Ultra Relativistic Quantum Molecular Dynamics code were used in the analyses. I found that the behavior of the nearest neighbor spacing distribution for the protons, neutrons and neutral pions depends critically on the multiplicity of secondary particles for simulated data. I conclude that the obtained results offer the possibility of fixing the centrality using the critical values of the multiplicity.展开更多
Using the method based on Random Matrix Theory （RMT）, the results for the nearest-neighbor distributions obtained from the experimental data on ^12C-C collisions at 4.2 AGeV/c have been discussed and compared with t...Using the method based on Random Matrix Theory （RMT）, the results for the nearest-neighbor distributions obtained from the experimental data on ^12C-C collisions at 4.2 AGeV/c have been discussed and compared with the simulated data on ^12C-C collisions at 4.2 AGeV/c produced with the aid of the Dubna Cascade Model. The results show that the correlation of secondary particles decreases with an increasing number of charged particles Nch. These observed changes in the nearest-neighbor distributions of charged particles could be associated with the centrality variation of the collisions.展开更多
The impact of large-scale wind farms on power system stability should be carefully investigated,in which mal-functions usually exist in the collector line's relay protection.In order to solve this challenging prob...The impact of large-scale wind farms on power system stability should be carefully investigated,in which mal-functions usually exist in the collector line's relay protection.In order to solve this challenging problem,a novel time-domain protection scheme for collector lines,based on random matrix theory(RMT),is proposed in this paper.First,the collected currents are preprocessed to form time series data.Then,a real-time sliding time window is used to form a consecutive time series data matrix.Based on RMT,mean spectral radius(MSR)is used to analyze time series data characteristics after real-time calculations are performed.Case studies demonstrate that RMT is independent from fault locations and fault types.In particular,faulty and non-faulty collector lines can be accurately and efficiently identified compared with traditional protection schemes.展开更多
The panel-type structures used in aerospace engineering can be subjected to severe highfrequency acoustic loadings in service. This paper evaluates the frequency-dependent random fatigue of panel-type structures made ...The panel-type structures used in aerospace engineering can be subjected to severe highfrequency acoustic loadings in service. This paper evaluates the frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites（CMCs） under acoustic loadings. Firstly, the high-frequency random responses from the broadband random excitation will result in more stress cycles in a deinite period of time. The probability density distributions of stress amplitudes will be different in different frequency bandwidths, though the peak stress estimations are identical. Secondly, the fatigue properties of CMCs can be highly frequency-dependent. The fatigue evaluation method for the random vibration case is adopted to evaluate the fatigue damage of a representative stiffened panel structure. The frequency effect through S-N curves on random fatigue damage is numerically veriied. Finally, a parameter is demonstrated to characterize the mean vibration frequency of a random process, and hence this parameter can further be considered as a reasonable loading frequency in the fatigue tests of CMCs to obtain more reliable S-N curves.Therefore, the inluence of vibration frequency can be incorporated in the random fatigue model from the two perspectives.展开更多
Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things(IoT)terminals.However,the massive realtime data processing requirements challenge the existing cloud computing m...Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things(IoT)terminals.However,the massive realtime data processing requirements challenge the existing cloud computing model.The edge server is closer to the data source.The end-edge-cloud collaboration offloads the cloud computing tasks to the edge environment,which solves the shortcomings of the cloud in resource storage,computing performance,and energy consumption.IoT terminals and sensors have caused security and privacy challenges due to resource constraints and exponential growth.As the key technology of IoT,Radio-Frequency Identification(RFID)authentication protocol tremendously strengthens privacy protection and improves IoT security.However,it inevitably increases system overhead while improving security,which is a major blow to low-cost RFID tags.The existing RFID authentication protocols are difficult to balance overhead and security.This paper designs an ultra-lightweight encryption function and proposes an RFID authentication scheme based on this function for the end-edge-cloud collaborative environment.The BAN logic proof and protocol verification tools AVISPA formally verify the protocol’s security.We use VIVADO to implement the encryption function and tag’s overhead on the FPGA platform.Performance evaluation indicates that the proposed protocol balances low computing costs and high-security requirements.展开更多
A new algorithm of structure random response numerical characteristics, namedas matrix algebra algorithm of structure analysis is presented. Using the algorithm, structurerandom response numerical characteristics can ...A new algorithm of structure random response numerical characteristics, namedas matrix algebra algorithm of structure analysis is presented. Using the algorithm, structurerandom response numerical characteristics can easily be got by directly solving linear matrixequations rather than structure motion differential equations. Moreover, in order to solve thecorresponding linear matrix equations, the numerical integration fast algorithm is presented. Thenaccording to the results, dynamic design and life-span estimation can be done. Besides, the newalgorithm can solve non-proportion damp structure response.展开更多
单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究...单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究设计了一种基于极值特征值差与特征值几何平均(difference of extreme eigenvalues and geometric average of eigenvalues,DEEGAE)的多主用户信号检测判决规则;提出了一种基于Wishart矩阵特征值统计分布理论的感知判决门限的闭式求解方法。该算法在频谱感知过程中直接利用认知用户的多天线接收数据构造判决规则并实施感知判决,具有全盲检测的优点;通过融合2种极限特征值门限分析结果,提高了非渐近感知条件下感知结果的准确性。Monte-Carlo仿真试验表明,新算法具有比经典的最大最小特征值之比算法和协方差绝对值检测算法更优的多主用户信号检测性能,同时能获得比传统基于最大最小特征值之差及其改进算法更为可靠的检测结果;与此同时,新算法的检测性能随着样本数目以及天线数目的增大而显著提升。展开更多
文摘Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries, the introduction of noise, and the size of the matrix, the resulting eigenvalue and eigenvector distributions remain relatively unchanged. However, when there are certain anomalous eigenvalues and their corresponding eigenvectors that do not follow the predicted distributions, it could indicate that there’s an underlying non-random signal inside the data. As data and matrices become more important in the sciences and computing, so too will the importance of processing them with the principles of random matrix theory.
基金Supported by the National Natural Science Foundation of China (No.60972039)Natural Science Foundation of Jiangsu Province (No.BK2007729)Natural Science Funding of Jiangsu Province (No.06KJA51001)
文摘Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed,which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.
文摘We have applied the Random Matrix Theory in order to examine the validity of the NPT treatment in HSP. We have investigated the pathology examining the sEMG recorded signal for about eight minutes. We have performed standard electromyographic investigations as well as we have applied the RMT method of analysis. We have investigated the sEMG signals before and after the NPT treatment. The application of a so robust method as the RMT evidences that the NPT treatment was able to induce a net improvement of the disease respect to the pathological status before NPT.
文摘We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fact that the major part of the time series is random, and compare the eigenvalue spectrum of cross correlation matrix of a large set of random time series, to the spectrum derived by the random matrix theory (RMT) at the limit of large dimension (the number of independent time series) and long enough length of time series. We test this algorithm on the real tick data of American stocks at different years between 1994 and 2002 and show that the extracted principal components indeed reflects the change of leading stock sectors during this period.
基金National Natural Science Foundations of China(Nos.61201161,61271335)Postdoctoral Science Foundation of Jiangsu Province of China(No.1301002B)
文摘Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.
基金supported in part by the National Natural Science Foundation of China(No.52037003)the Major Science and Technology Projects in Yunnan Province(No.202402AG050006).
文摘In this work,a consistency detection method is proposed,to overcome the inconsistencies in the use of large-scale lead-carbon energy storage batteries(LCESBs)and the difficulties of large-scale detection for LCESBs.Based on the chemical materials and physical mechanisms of LCESBs,the internal and external factors that affect the consistency and their characterization parameters are analyzed.The inconsistent characterization parameters,such as voltage,temperature,and resistance,are used to construct a high-dimensional random matrix and calculate the matrix eigenvalue.Single loop theorem and average spectral radius are then employed to carry out preliminary consistency detection.Next,short-term discharge experiments are conducted on individual batteries with inconsistent initial screening.The voltage and temperature data is collected,and sequential overlapping derivative(SOD)transformation is performed to extract the characteristics of voltage and temperature changes.The consistency of individual cells using the Wasserstein distance is quantitatively characterized.Finally,the reliability of the consistency detection method is evaluated by the confusion matrix.The large amounts of actual measurement data shows a false negative rate of the algorithm of 0 and an accuracy of 99.94%.This study shows that using random matrix theory for preliminary detection is suitable for processing high-dimensional data of large-scale energy storage power plants.Using SOD for precise detection can amplify the voltage,temperature,and resistance differences of inconsistent batteries,making the consistency detection more accurate.
基金Project supported by the National Natural Science Foundation of China(No.61976113)。
文摘The weighting subspace fitting(WSF)algorithm performs better than the multi-signal classification(MUSIC)algorithm in the case of low signal-to-noise ratio(SNR)and when signals are correlated.In this study,we use the random matrix theory(RMT)to improve WSF.RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate.The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance.Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory,the method of calculating WSF is obtained.Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.
基金This work was supported in part by the National Key Research and Development Program of China(2018YFB0904200)Eponymous Complement S&T Program of State Grid Corporation of China(SGLNDKOOKJJS1800266).
文摘Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.
基金supported by the State Scholarship Fund of China Scholarship Council(No.202007000168).
文摘The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and reliability of the existing schemes are vulnerable to high resistance and data errors.To improve the insufficiencies,this paper proposes a pilot protection scheme by using the random matrix for DC lines in the symmetrical bipolar MMC high-voltage direct current(HVDC)grid.Firstly,the 1-mode voltage time-domain characteristics of the line end,DC bus,and adjacent line end are analyzed by the inverse Laplace transform to find indicators of fault direction.To combine the actual model with the data-driven method,the methods to construct the data expansion matrix and to calculate additional noise are proposed.Then,the mean spectral radiuses of two random matrices are used to detect fault directions,and a novel pilot protection criterion is proposed.The protection scheme only needs to transmit logic signals,decreasing the communication burden.It performs well in high-resistance faults,abnormal data errors,measurement errors,parameters errors,and different topology conditions.Numerous simulations in PSCAD/EMTDC confirm the effectiveness and reliability of the proposed protection scheme.
基金This work was supported in part by the National Natural Science Foundation of China(Key Project Number:51437003)。
文摘Fault detection and location are critically significant applications of a supervisory control system in a smart grid.The methods,based on random matrix theory(RMT),have been practiced using measurements to detect short circuit faults occurring on transmission lines.However,the diagnostic accuracy is infuenced by the noise signal in the measurements.The relationship between mean eigenvalue of a random matrix and noise is detected in this paper,and the defects of the Mean Spectral Radius(MSR),as an indicator to detect faults,are theoretically determined,along with a novel indicator of the shifting degree of maximum eigenvalue and its threshold.By comparing the indicator and the threshold,the occurrence of a fault can be assessed.Finally,an augmented matrix is constructed to locate the fault area.The proposed method can effectively achieve fault detection via the RMT without any influence of noise,and also does not depend on system models.The experiment results are based on the IEEE 39-bus system.Also,actual provincial grid data is applied to validate the effectiveness of the proposed method.
文摘I discuss the results from a study of the central ^12CC collisions at 4.2 A GeV/c. The data have been analyzed using a new method based on the Random Matrix Theory. The simulation data coming from the Ultra Relativistic Quantum Molecular Dynamics code were used in the analyses. I found that the behavior of the nearest neighbor spacing distribution for the protons, neutrons and neutral pions depends critically on the multiplicity of secondary particles for simulated data. I conclude that the obtained results offer the possibility of fixing the centrality using the critical values of the multiplicity.
文摘Using the method based on Random Matrix Theory （RMT）, the results for the nearest-neighbor distributions obtained from the experimental data on ^12C-C collisions at 4.2 AGeV/c have been discussed and compared with the simulated data on ^12C-C collisions at 4.2 AGeV/c produced with the aid of the Dubna Cascade Model. The results show that the correlation of secondary particles decreases with an increasing number of charged particles Nch. These observed changes in the nearest-neighbor distributions of charged particles could be associated with the centrality variation of the collisions.
基金the National Natural Science Foundation of China(No.51807085,52037003)Key Science and Technology Project of Yunnan Province,China(202002AF080001)。
文摘The impact of large-scale wind farms on power system stability should be carefully investigated,in which mal-functions usually exist in the collector line's relay protection.In order to solve this challenging problem,a novel time-domain protection scheme for collector lines,based on random matrix theory(RMT),is proposed in this paper.First,the collected currents are preprocessed to form time series data.Then,a real-time sliding time window is used to form a consecutive time series data matrix.Based on RMT,mean spectral radius(MSR)is used to analyze time series data characteristics after real-time calculations are performed.Case studies demonstrate that RMT is independent from fault locations and fault types.In particular,faulty and non-faulty collector lines can be accurately and efficiently identified compared with traditional protection schemes.
基金supports from the National Natural Science Foundation of China (No. 11572086 , No. 11402052 )the New Century Excellent Talent in University (NCET-11-0086)+3 种基金the Natural Science Foundation of Jiangsu province (No. BK20140616 )the Fundamental Research Funds for the Central Universities and the Scientiic Research Innovation Program of Jiangsu Province College Postgraduates (KYLX_0093, KYLX15_0092)the China Scholarship Council ( 201506090047 )the Ministry of Education, Science and Technological Development of Republic of Serbia ( TR 35011 and ON 74001 )
文摘The panel-type structures used in aerospace engineering can be subjected to severe highfrequency acoustic loadings in service. This paper evaluates the frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites（CMCs） under acoustic loadings. Firstly, the high-frequency random responses from the broadband random excitation will result in more stress cycles in a deinite period of time. The probability density distributions of stress amplitudes will be different in different frequency bandwidths, though the peak stress estimations are identical. Secondly, the fatigue properties of CMCs can be highly frequency-dependent. The fatigue evaluation method for the random vibration case is adopted to evaluate the fatigue damage of a representative stiffened panel structure. The frequency effect through S-N curves on random fatigue damage is numerically veriied. Finally, a parameter is demonstrated to characterize the mean vibration frequency of a random process, and hence this parameter can further be considered as a reasonable loading frequency in the fatigue tests of CMCs to obtain more reliable S-N curves.Therefore, the inluence of vibration frequency can be incorporated in the random fatigue model from the two perspectives.
基金supported in part by the “Pioneer” and “Leading Goose” R&D Program of Zhejiang (Grant No. 2022C03174)the National Natural Science Foundation of China (No. 92067103)+4 种基金the Key Research and Development Program of Shaanxi (No.2021ZDLGY06- 02)the Natural Science Foundation of Shaanxi Province (No.2019ZDLGY12-02)the Shaanxi Innovation Team Project (No.2018TD007)the Xi’an Science and technology Innovation Plan (No.201809168CX9JC10)National 111 Program of China B16037
文摘Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things(IoT)terminals.However,the massive realtime data processing requirements challenge the existing cloud computing model.The edge server is closer to the data source.The end-edge-cloud collaboration offloads the cloud computing tasks to the edge environment,which solves the shortcomings of the cloud in resource storage,computing performance,and energy consumption.IoT terminals and sensors have caused security and privacy challenges due to resource constraints and exponential growth.As the key technology of IoT,Radio-Frequency Identification(RFID)authentication protocol tremendously strengthens privacy protection and improves IoT security.However,it inevitably increases system overhead while improving security,which is a major blow to low-cost RFID tags.The existing RFID authentication protocols are difficult to balance overhead and security.This paper designs an ultra-lightweight encryption function and proposes an RFID authentication scheme based on this function for the end-edge-cloud collaborative environment.The BAN logic proof and protocol verification tools AVISPA formally verify the protocol’s security.We use VIVADO to implement the encryption function and tag’s overhead on the FPGA platform.Performance evaluation indicates that the proposed protocol balances low computing costs and high-security requirements.
基金This project is supported by National Natural Science Foundation of China (No.59805001)
文摘A new algorithm of structure random response numerical characteristics, namedas matrix algebra algorithm of structure analysis is presented. Using the algorithm, structurerandom response numerical characteristics can easily be got by directly solving linear matrixequations rather than structure motion differential equations. Moreover, in order to solve thecorresponding linear matrix equations, the numerical integration fast algorithm is presented. Thenaccording to the results, dynamic design and life-span estimation can be done. Besides, the newalgorithm can solve non-proportion damp structure response.
文摘单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究设计了一种基于极值特征值差与特征值几何平均(difference of extreme eigenvalues and geometric average of eigenvalues,DEEGAE)的多主用户信号检测判决规则;提出了一种基于Wishart矩阵特征值统计分布理论的感知判决门限的闭式求解方法。该算法在频谱感知过程中直接利用认知用户的多天线接收数据构造判决规则并实施感知判决,具有全盲检测的优点;通过融合2种极限特征值门限分析结果,提高了非渐近感知条件下感知结果的准确性。Monte-Carlo仿真试验表明,新算法具有比经典的最大最小特征值之比算法和协方差绝对值检测算法更优的多主用户信号检测性能,同时能获得比传统基于最大最小特征值之差及其改进算法更为可靠的检测结果;与此同时,新算法的检测性能随着样本数目以及天线数目的增大而显著提升。
