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
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
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
Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed ...Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed and compared.The results show that there are meaningful true correlations as well as correlation＂noises＂in the TCMs.The true correlations contain short range correlations（SRCs） among temperature series of neighboring grid points as well as long range correlations（LRCs） among temperature series of different regions,such as the El Nino area and the warm pool areas of the Pacific,the Indian Ocean,the Atlantic,etc.At different time scales,these two kinds of correlations show different features：at 1-10-day scale,SRCs are more important than LRCs;while at 15-day-or-more scale,the importance of SRCs and LRCs decreases and increases respectively,compared with the case of 1-10-day scale.It is found from the analyses of eigenvalues and eigenvectors of TCMs and corresponding RCMs that most correlation information is contained in several eigenvectors of TCMs with relatively larger eigenvalues,and the projections of global temperature series onto these eigenvectors are able to reflect the overall characteristics of global temperature changes to some extent.Besides,the correlation coefficients（CCs） of grid point temperature series show significant temporal and spatial variations.The average CCs over 1950-1956,1972-1977,and 1996-2000 are significantly higher than average while that over the periods 1978-1982 and 1991-1996 are opposite,suggesting a distinctive oscillation of quasi-10-20 yr.Spatially,the CCs at 1-and 15-day scales both show band-like zonal distributions;the zonally averaged CCs at 1-day scale display a better latitudinal symmetry,while they are relatively worse at 15-day scale because of sea-land contrast of the Northern and Southern Hemisphere.However,the meridionally averaged CCs at 15-day scale display a longitudinal quasi-symmetry.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
We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study ...We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.展开更多
Total Knee Replacement(TKR)is the increasing trend now a day,in revision surgery which is associated with aseptic loosening,which is a challenging research for the TKR component.The selection of optimal material loose...Total Knee Replacement(TKR)is the increasing trend now a day,in revision surgery which is associated with aseptic loosening,which is a challenging research for the TKR component.The selection of optimal material loosening can be controlled at some limits.This paper is going to consider the best material selected among a number of alternative materials for the femoral component(FC)by using Graph Theory.Here GTMA process used for optimization of material and a systematic technique introduced through sensitivity analysis to find out the more reliable result.Obtained ranking suggests the use of optimized material over the other existing material.By following GTMA Co_Cr-alloys(wrought-Co-Ni-Cr-Mo)and Co_Cr-alloys(cast-able-Co-Cr-Mo)are on the 1st and 2nd position respectively.展开更多
Unmanned Aerial Vehicles(UAVs)have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes(SNs)from an Internet of Things(IoT)perspective.However,due to physical limi...Unmanned Aerial Vehicles(UAVs)have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes(SNs)from an Internet of Things(IoT)perspective.However,due to physical limitations,UAVs are unable to further process the harvested data and have to rely on terrestrial servers,thus extra spectrum resource is needed to convey the harvested data.To avoid the cost of extra servers and spectrum resources,in this paper,we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output(CF-mMIMO)system,where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing.In order to avoid using additional spectrum resources,the entire bandwidth is shared among radio access networks and wireless fronthaul links.Moreover,considering the limited capacity of the fronthaul links,the compress-and-forward scheme is adopted.In this work,in order to maximize the ergodically achievable sum rate of SNs,the power allocation of ground access points,the compression of fronthaul links,and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized.To avoid the high overhead introduced by computing ergodically achievable rates,we introduce an approximate problem,using the large-dimensional random matrix theory,which relies only on statistical channel state information.We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach’s methods to find solutions.Finally,simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.展开更多
Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeli...Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.展开更多
From the comparison of correlation tensor in the theory of quantum network, the Alexander relation matrix in the theory of knot crystals and the identical inversion relations under the action of Pauli matrices, we sho...From the comparison of correlation tensor in the theory of quantum network, the Alexander relation matrix in the theory of knot crystals and the identical inversion relations under the action of Pauli matrices, we show that there is a one to one correspondence between four Bell bases and four oriented links of the linkage in knot theory.展开更多
We report on the theoretical and experimental investigations of the transition of a typical quantum system with mixed regular-integrable classical dynamics to one with violated time-reversal(T)invariance.The measureme...We report on the theoretical and experimental investigations of the transition of a typical quantum system with mixed regular-integrable classical dynamics to one with violated time-reversal(T)invariance.The measurements are performed with a flat superconducting microwave resonator with circular shape in which chaoticity is induced by using either long antennas or inserting two circular disks into the cavity,and by magnetizing a ferrite disk placed at its center,which leads to violation of T invariance.We propose an extension of the Rosenzweig-Porter(RP)model,which interpolates between mixed regular-chaotic instead of integrable dynamics and fully chaotic dynamics with violated T-invariance,and derive a Wigner-surmise like analytical expression for the corresponding nearest-neighbor spacing distribution.We propose a procedure involving this result and those for the RP model to determine the size of T-invariance violation and chaoticity and validate it employing the experimental eigenfrequency spectra.展开更多
To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fus...To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.展开更多
The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained n...The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.展开更多
Construction of Global Energy Interconnection(GEI) is regarded as an effective way to utilize clean energy and it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is...Construction of Global Energy Interconnection(GEI) is regarded as an effective way to utilize clean energy and it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is accompanied with the sharing, fusion and comprehensive application of energy related data all over the world. The paper analyzes the technology innovation direction of GEI and the advantages of big data technologies in supporting GEI development, and then gives some typical application scenarios to illustrate the application value of big data. Finally, the architecture for applying random matrix theory in GEI is presented.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.
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.
基金Supported jointly by the National Natural Science Foundation of China under Grant Nos. 40930952, 40875040, and 40905034the National Basic Research Program of China under Grant No. 2006CB400503the National Science & Technology Support Program of China under Grant Nos. 2007BAC03A01 and 2007BAC29B01
文摘Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed and compared.The results show that there are meaningful true correlations as well as correlation＂noises＂in the TCMs.The true correlations contain short range correlations（SRCs） among temperature series of neighboring grid points as well as long range correlations（LRCs） among temperature series of different regions,such as the El Nino area and the warm pool areas of the Pacific,the Indian Ocean,the Atlantic,etc.At different time scales,these two kinds of correlations show different features：at 1-10-day scale,SRCs are more important than LRCs;while at 15-day-or-more scale,the importance of SRCs and LRCs decreases and increases respectively,compared with the case of 1-10-day scale.It is found from the analyses of eigenvalues and eigenvectors of TCMs and corresponding RCMs that most correlation information is contained in several eigenvectors of TCMs with relatively larger eigenvalues,and the projections of global temperature series onto these eigenvectors are able to reflect the overall characteristics of global temperature changes to some extent.Besides,the correlation coefficients（CCs） of grid point temperature series show significant temporal and spatial variations.The average CCs over 1950-1956,1972-1977,and 1996-2000 are significantly higher than average while that over the periods 1978-1982 and 1991-1996 are opposite,suggesting a distinctive oscillation of quasi-10-20 yr.Spatially,the CCs at 1-and 15-day scales both show band-like zonal distributions;the zonally averaged CCs at 1-day scale display a better latitudinal symmetry,while they are relatively worse at 15-day scale because of sea-land contrast of the Northern and Southern Hemisphere.However,the meridionally averaged CCs at 15-day scale display a longitudinal quasi-symmetry.
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.
文摘We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.
文摘Total Knee Replacement(TKR)is the increasing trend now a day,in revision surgery which is associated with aseptic loosening,which is a challenging research for the TKR component.The selection of optimal material loosening can be controlled at some limits.This paper is going to consider the best material selected among a number of alternative materials for the femoral component(FC)by using Graph Theory.Here GTMA process used for optimization of material and a systematic technique introduced through sensitivity analysis to find out the more reliable result.Obtained ranking suggests the use of optimized material over the other existing material.By following GTMA Co_Cr-alloys(wrought-Co-Ni-Cr-Mo)and Co_Cr-alloys(cast-able-Co-Cr-Mo)are on the 1st and 2nd position respectively.
基金supported in part by the Jiangsu Provincial Key Research and Development Program(No.BE2022068-2)in part by the National Natural Science Foundation of China under Grant 62201285+1 种基金in part by Young Elite Scientists Sponsorship Program by CAST under Grant 2022QNRC001in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX23_1012.
文摘Unmanned Aerial Vehicles(UAVs)have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes(SNs)from an Internet of Things(IoT)perspective.However,due to physical limitations,UAVs are unable to further process the harvested data and have to rely on terrestrial servers,thus extra spectrum resource is needed to convey the harvested data.To avoid the cost of extra servers and spectrum resources,in this paper,we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output(CF-mMIMO)system,where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing.In order to avoid using additional spectrum resources,the entire bandwidth is shared among radio access networks and wireless fronthaul links.Moreover,considering the limited capacity of the fronthaul links,the compress-and-forward scheme is adopted.In this work,in order to maximize the ergodically achievable sum rate of SNs,the power allocation of ground access points,the compression of fronthaul links,and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized.To avoid the high overhead introduced by computing ergodically achievable rates,we introduce an approximate problem,using the large-dimensional random matrix theory,which relies only on statistical channel state information.We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach’s methods to find solutions.Finally,simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.
基金National Natural Science Foundation of China under Grant 42276055National Key Research and Development Program under Grant 2022YFC2803503Fundamental Research Funds for the Central Universities under Grant 202262008.
文摘Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.
文摘From the comparison of correlation tensor in the theory of quantum network, the Alexander relation matrix in the theory of knot crystals and the identical inversion relations under the action of Pauli matrices, we show that there is a one to one correspondence between four Bell bases and four oriented links of the linkage in knot theory.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11775100,12247101,and 11961131009)the financial support from the China Scholarship Council(Grant No.CSC202306180087)the financial support from the Institute for Basic Science in Korea(Grant No.IBS-R024-D1)。
文摘We report on the theoretical and experimental investigations of the transition of a typical quantum system with mixed regular-integrable classical dynamics to one with violated time-reversal(T)invariance.The measurements are performed with a flat superconducting microwave resonator with circular shape in which chaoticity is induced by using either long antennas or inserting two circular disks into the cavity,and by magnetizing a ferrite disk placed at its center,which leads to violation of T invariance.We propose an extension of the Rosenzweig-Porter(RP)model,which interpolates between mixed regular-chaotic instead of integrable dynamics and fully chaotic dynamics with violated T-invariance,and derive a Wigner-surmise like analytical expression for the corresponding nearest-neighbor spacing distribution.We propose a procedure involving this result and those for the RP model to determine the size of T-invariance violation and chaoticity and validate it employing the experimental eigenfrequency spectra.
基金supported by the National Natural Science Foundation of China(No.51875465)the Shaanxi Province Postdoctoral Research Project Funding,Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(No.CX2023002)+1 种基金the Civil Aircraft Scientific Research Projectthe Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring(No.GCZX-2022-01).
文摘To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.
文摘The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.
基金supported by National High-technology Research and Development Program of China (863 Program) (2015AA050203)the State Grid Science and Technology Project (5442DZ170019-P)
文摘Construction of Global Energy Interconnection(GEI) is regarded as an effective way to utilize clean energy and it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is accompanied with the sharing, fusion and comprehensive application of energy related data all over the world. The paper analyzes the technology innovation direction of GEI and the advantages of big data technologies in supporting GEI development, and then gives some typical application scenarios to illustrate the application value of big data. Finally, the architecture for applying random matrix theory in GEI is presented.
