1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain bounda...1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].展开更多
Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains...Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with ot...Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.展开更多
A combination of hard(SiCP)and soft(fly ash)particulate reinforcements could be a strategy to enhance combination of multiple properties of Magnesium and its alloys which otherwise suffer from low stiffness,low wear r...A combination of hard(SiCP)and soft(fly ash)particulate reinforcements could be a strategy to enhance combination of multiple properties of Magnesium and its alloys which otherwise suffer from low stiffness,low wear resistance,and many other critical properties.However,at present a comprehensive and robust map correlating different properties in particle-reinforced composites is much lacking.In this work,an industrial grade AZ91 magnesium alloy reinforced with hard SiC and soft fly ash particles(with 3 vol.%each),has been prepared using stir casting followed by hot extrusion at 325℃with a ratio of 21.5.Microstructure of the hybrid composite was characterized using optical and scanning electron microscopes.The composite exhibited a reduction in average grain size from 13.6 to 7.1μm,concomitantly an increase in Vickers hardness from 73 to 111 HV.The tension-compression yield asymmetry ratios of the unreinforced alloy and hybrid composite were 1.165 and 0.976,respectively indicating higher yield strength for the composite under compressive load.The composite exhibited 76%improvement in damping capacity under time sweep mode,and 28%improvement at 423 K under temperature sweep mode.The tribological characteristics of the composite under dry sliding conditions at sliding speeds and loads in the range of 0.5 to 1.5 m s^(-1)and 10 to 30 N,respectively showed higher wear resistance than the unreinforced alloy.The composite showed 23%improvement in sliding wear resistance at a load of 20 N and a speed of 1 m s^(-1).Finally,efforts have been made to understand the influence of one property on the other by developing statistical property correlation maps from the properties obtained in this study and from the literature.These maps are expected to help in the design of hybrid Metal Matrix Composites for a variety of targeted applications in different sectors.展开更多
Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechani...Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and land-use changes.Vegetation-related human interventions,such as agriculture and forestry planting,urban greening,and afforestation,can effectively reduce the surface warming caused by human activities.This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.展开更多
We derive explicit expressions for quantum discord and classical correlation for an X structure density matrix. Based on the characteristics of the expressions, the quantum discord and the classical correlation are ea...We derive explicit expressions for quantum discord and classical correlation for an X structure density matrix. Based on the characteristics of the expressions, the quantum discord and the classical correlation are easily obtained and compared under different initial conditions using a novel analytical method. We explain the relationships among quantum discord, classical correlation, and entanglement, and further find that the quantum discord is not always larger than the entanglement measured by concurrence in a general two-qubit X state. The new method, which is different from previous approaches, has certain guiding significance for analysing quantum discord and classical correlation of a two-qubit X state, such as a mixed state.展开更多
Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics o...Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.展开更多
With the advancement of human-computer interaction,surface electromyography(sEMG)-based gesture recognition has garnered increasing attention.However,effectively utilizing the spatio-temporal dependencies in sEMG sign...With the advancement of human-computer interaction,surface electromyography(sEMG)-based gesture recognition has garnered increasing attention.However,effectively utilizing the spatio-temporal dependencies in sEMG signals and integrating multiple key features remain significant challenges for existing techniques.To address this issue,we propose a model named the Two-Stream Hybrid Spatio-Temporal Fusion Network(TS-HSTFNet).Specifically,we design a dynamic spatio-temporal graph convolution module that employs an adaptive dynamic adjacency matrix to explore the spatial dynamic patterns in the sEMG signals fully.Additionally,a spatio-temporal attention fusion module is designed to fully utilize the potential correlations among multiple features for the final fusion.The results indicate that the proposed TS-HSTFNet model achieves 84.96%and 88.08%accuracy on the Ninapro DB2 and Ninapro DB5 datasets,respectively,demonstrating high precision in gesture recognition.Our work emphasizes the importance of extracting spatio-temporal features in gesture recognition and provides a novel approach for multi-source information fusion.展开更多
As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services p...As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services provided by mobile platforms. Many services can be executed on the mobile devices with various mobile applications launched to mobile platforms. People can choose what they like to install in their mobile devices and hence make their life more convenient, entertaining, and productive. However, there are too many mobile applications for users to choose. The goal of this research is to propose a methodology which can recommend top-N lists for mobile applications. A comment correlation matrix is proposed. Furthermore, a recommendation algorithm for mobile applications based on user comments and key attributes is built. With the proposed method, it outperforms Google play and is closer to user real feelings.展开更多
The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix...The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.展开更多
A new approach that bounds the largest eigenvalue of 3 × 3 correlation matrices is presented. Optimal bounds by given determinant and trace of the squared correlation matrix are derived and shown to be more strin...A new approach that bounds the largest eigenvalue of 3 × 3 correlation matrices is presented. Optimal bounds by given determinant and trace of the squared correlation matrix are derived and shown to be more stringent than the optimal bounds by Wolkowicz and Styan in specific cases.展开更多
Massive multiple-input multiple-output(MIMO),a technique that can greatly increase spectral efficiency(SE)of cellular networks,has attracted significant interests in recent years.One of the major limitations of massiv...Massive multiple-input multiple-output(MIMO),a technique that can greatly increase spectral efficiency(SE)of cellular networks,has attracted significant interests in recent years.One of the major limitations of massive MIMO systems is pilot contamination,which will deteriorate the SE.The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction.However,it cannot break through another limitation of massive MIMO,i.e.,spatial correlation.In addition,it will also lead to interference between the pilot and user data since they are imposed together.In this paper,we try to tackle these two issues,which will be described as follows.Firstly,a column-wise asymptotically orthogonal matrix,named as pseudo-channel matrix,is developed by orthogonalization of received signal.To recover the information about the large-scale fading(LSF)coefficients,the pseudo-channel matrix is truncated according to the cardinality of adjacent users set(CAUS).By this means,spatial correlation can be mitigated effectively.Secondly,robust independent component analysis(RobustICA)is used to reduce the interference caused by user data,and as a result the system performance can be further improved.Numerical simulation results demonstrate the effectiveness of the proposed method.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
SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core ...SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core problem of autocorrelation matrix singular value in SNR estimation process,through making use of householder transforming autocorrelation matrix into tridiagonal matrix,and by using the relation of corresponding characteristic equation coefficients and singular value,a numerical algorithm is gi...展开更多
Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;"&g...Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>展开更多
Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmiss...Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.展开更多
Static disorder plays a crucial role in the electronic dynamics and spec-troscopy of complex molecular sys-tems.Traditionally,obtaining ob-servables averaged over static disor-der requires thousands of realiza-tions v...Static disorder plays a crucial role in the electronic dynamics and spec-troscopy of complex molecular sys-tems.Traditionally,obtaining ob-servables averaged over static disor-der requires thousands of realiza-tions via direct sampling of the dis-order distribution,leading to high computational costs.In this work,we extend the auxiliary degree-of-freedom based matrix product state(MPS)method to handle system-bath correlated thermal equilibrium initial states,which can capture static disorder effects using a one-shot quantum dynamical simulation.We validate the effectiveness of the extended method by computing the dipole-dipole time correlation function of the Holstein model relevant to the emission spectrum of molecular aggregates.Our results show that the one-shot method is very accu-rate with only a moderate increase in MPS bond dimension,thereby significantly reducing computational cost.Moreover,it enables the generation of a much larger number of samples than the conventional direct sampling method at negligible additional cost,thus reducing sta-tistical errors.This method provides a broadly useful tool for calculating equilibrium time cor-relation functions in system-bath coupled models with static disorder.展开更多
In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the e...In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the emerging market are more correlated than that of the developed market. Considering that the values of the components for the eigenvector may be positive or negative, we analyze the differences between two markets in combination with the endogenous and exogenous events which influence the financial markets. We find that the sparse pattern of components of eigenvectors out of the threshold value has no change in American bank stocks before and after the subprime crisis. However, it changes from sparse to dense for Chinese bank stocks. By using the threshold value to exclude the external factors, we simulate the interactions in financial markets.展开更多
Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-...Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.展开更多
基金support by the National Natural Science Foundation of China(Grant Nos.U23A20546 and 52271010)the Chinese National Natural Science Fund for Distinguished Young Scholars(Grant No.52025015)the Natural Science Foundation of Tianjin City(No.21JCZDJC00510).
文摘1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].
基金supported by the Beijing Natural Science Foundation(5252014)the National Natural Science Foundation of China(62303063)。
文摘Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11875042 and 11505114)the Shanghai Project for Construction of Top Disciplines (Grant No. USST-SYS-01)。
文摘Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.
文摘A combination of hard(SiCP)and soft(fly ash)particulate reinforcements could be a strategy to enhance combination of multiple properties of Magnesium and its alloys which otherwise suffer from low stiffness,low wear resistance,and many other critical properties.However,at present a comprehensive and robust map correlating different properties in particle-reinforced composites is much lacking.In this work,an industrial grade AZ91 magnesium alloy reinforced with hard SiC and soft fly ash particles(with 3 vol.%each),has been prepared using stir casting followed by hot extrusion at 325℃with a ratio of 21.5.Microstructure of the hybrid composite was characterized using optical and scanning electron microscopes.The composite exhibited a reduction in average grain size from 13.6 to 7.1μm,concomitantly an increase in Vickers hardness from 73 to 111 HV.The tension-compression yield asymmetry ratios of the unreinforced alloy and hybrid composite were 1.165 and 0.976,respectively indicating higher yield strength for the composite under compressive load.The composite exhibited 76%improvement in damping capacity under time sweep mode,and 28%improvement at 423 K under temperature sweep mode.The tribological characteristics of the composite under dry sliding conditions at sliding speeds and loads in the range of 0.5 to 1.5 m s^(-1)and 10 to 30 N,respectively showed higher wear resistance than the unreinforced alloy.The composite showed 23%improvement in sliding wear resistance at a load of 20 N and a speed of 1 m s^(-1).Finally,efforts have been made to understand the influence of one property on the other by developing statistical property correlation maps from the properties obtained in this study and from the literature.These maps are expected to help in the design of hybrid Metal Matrix Composites for a variety of targeted applications in different sectors.
基金National Natural Science Foundation of China(41461086)National Natural Science Foundation of China(41761108)。
文摘Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and land-use changes.Vegetation-related human interventions,such as agriculture and forestry planting,urban greening,and afforestation,can effectively reduce the surface warming caused by human activities.This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.
基金supported by the Natural Science Foundation of Hunan Province of China (Grant No. 09JJ6011)the Natural Science Foundation of Education Department of Hunan Province, China (Grant Nos. 08A055 and 07C528)
文摘We derive explicit expressions for quantum discord and classical correlation for an X structure density matrix. Based on the characteristics of the expressions, the quantum discord and the classical correlation are easily obtained and compared under different initial conditions using a novel analytical method. We explain the relationships among quantum discord, classical correlation, and entanglement, and further find that the quantum discord is not always larger than the entanglement measured by concurrence in a general two-qubit X state. The new method, which is different from previous approaches, has certain guiding significance for analysing quantum discord and classical correlation of a two-qubit X state, such as a mixed state.
基金Supported by the National Natural Science Foundation of China （40971275, 50811120111）
文摘Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.
基金Funding from the Key Research and development plan of Shaanxi Province"Human robot interaction technology and implementation of bionic robotic arm based on remote operation"(2023-ZDLGY-24).
文摘With the advancement of human-computer interaction,surface electromyography(sEMG)-based gesture recognition has garnered increasing attention.However,effectively utilizing the spatio-temporal dependencies in sEMG signals and integrating multiple key features remain significant challenges for existing techniques.To address this issue,we propose a model named the Two-Stream Hybrid Spatio-Temporal Fusion Network(TS-HSTFNet).Specifically,we design a dynamic spatio-temporal graph convolution module that employs an adaptive dynamic adjacency matrix to explore the spatial dynamic patterns in the sEMG signals fully.Additionally,a spatio-temporal attention fusion module is designed to fully utilize the potential correlations among multiple features for the final fusion.The results indicate that the proposed TS-HSTFNet model achieves 84.96%and 88.08%accuracy on the Ninapro DB2 and Ninapro DB5 datasets,respectively,demonstrating high precision in gesture recognition.Our work emphasizes the importance of extracting spatio-temporal features in gesture recognition and provides a novel approach for multi-source information fusion.
文摘As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services provided by mobile platforms. Many services can be executed on the mobile devices with various mobile applications launched to mobile platforms. People can choose what they like to install in their mobile devices and hence make their life more convenient, entertaining, and productive. However, there are too many mobile applications for users to choose. The goal of this research is to propose a methodology which can recommend top-N lists for mobile applications. A comment correlation matrix is proposed. Furthermore, a recommendation algorithm for mobile applications based on user comments and key attributes is built. With the proposed method, it outperforms Google play and is closer to user real feelings.
文摘The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.
文摘A new approach that bounds the largest eigenvalue of 3 × 3 correlation matrices is presented. Optimal bounds by given determinant and trace of the squared correlation matrix are derived and shown to be more stringent than the optimal bounds by Wolkowicz and Styan in specific cases.
基金National Key R&D Program of China(2017YFB0403604)the Fundamental Research Funds for the Central Universities(No.292021000242)the National Natural Science Foundation of China(Grant No.61571416,61072045,61032006)。
文摘Massive multiple-input multiple-output(MIMO),a technique that can greatly increase spectral efficiency(SE)of cellular networks,has attracted significant interests in recent years.One of the major limitations of massive MIMO systems is pilot contamination,which will deteriorate the SE.The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction.However,it cannot break through another limitation of massive MIMO,i.e.,spatial correlation.In addition,it will also lead to interference between the pilot and user data since they are imposed together.In this paper,we try to tackle these two issues,which will be described as follows.Firstly,a column-wise asymptotically orthogonal matrix,named as pseudo-channel matrix,is developed by orthogonalization of received signal.To recover the information about the large-scale fading(LSF)coefficients,the pseudo-channel matrix is truncated according to the cardinality of adjacent users set(CAUS).By this means,spatial correlation can be mitigated effectively.Secondly,robust independent component analysis(RobustICA)is used to reduce the interference caused by user data,and as a result the system performance can be further improved.Numerical simulation results demonstrate the effectiveness of the proposed method.
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supported by the National Natural Science Foundation of China (Grant No.90604031)
文摘SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core problem of autocorrelation matrix singular value in SNR estimation process,through making use of householder transforming autocorrelation matrix into tridiagonal matrix,and by using the relation of corresponding characteristic equation coefficients and singular value,a numerical algorithm is gi...
文摘Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>
文摘Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.
基金supported by the National Natural Science Foundation of China(No.22273005 and No.22422301)the Innovation Program for Quantum Science and Technology(No.2023ZD0300200)+1 种基金the National Security Academic Foundation(No.U2330201)the Fundamental Research Funds for the Central Universities.
文摘Static disorder plays a crucial role in the electronic dynamics and spec-troscopy of complex molecular sys-tems.Traditionally,obtaining ob-servables averaged over static disor-der requires thousands of realiza-tions via direct sampling of the dis-order distribution,leading to high computational costs.In this work,we extend the auxiliary degree-of-freedom based matrix product state(MPS)method to handle system-bath correlated thermal equilibrium initial states,which can capture static disorder effects using a one-shot quantum dynamical simulation.We validate the effectiveness of the extended method by computing the dipole-dipole time correlation function of the Holstein model relevant to the emission spectrum of molecular aggregates.Our results show that the one-shot method is very accu-rate with only a moderate increase in MPS bond dimension,thereby significantly reducing computational cost.Moreover,it enables the generation of a much larger number of samples than the conventional direct sampling method at negligible additional cost,thus reducing sta-tistical errors.This method provides a broadly useful tool for calculating equilibrium time cor-relation functions in system-bath coupled models with static disorder.
基金supported by the National Natural Science Foundation of China(Grant Nos.11275186,91024026,and FOM2014OF001)the University of Shanghai for Science and Technology(USST)of Humanities and Social Sciences,China(Grant Nos.USST13XSZ05 and 11YJA790231)
文摘In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the emerging market are more correlated than that of the developed market. Considering that the values of the components for the eigenvector may be positive or negative, we analyze the differences between two markets in combination with the endogenous and exogenous events which influence the financial markets. We find that the sparse pattern of components of eigenvectors out of the threshold value has no change in American bank stocks before and after the subprime crisis. However, it changes from sparse to dense for Chinese bank stocks. By using the threshold value to exclude the external factors, we simulate the interactions in financial markets.
基金Supported by the National Basic Research Program of China(2011CB707904)the Natural Science Foundation of China(61472289)Hubei Province Natural Science Foundation of China(2015CFB254)
文摘Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.
