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
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
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
Classical Correlations were founded in 1900 by Karl Pearson and have since been applied as a statistical tool in virtually all sciences. Quantum correlations go back to Albert Einstein et al. in 1935 and Erwin Schr...Classical Correlations were founded in 1900 by Karl Pearson and have since been applied as a statistical tool in virtually all sciences. Quantum correlations go back to Albert Einstein et al. in 1935 and Erwin Schrödinger’s responses shortly after. In this paper, we contrast classical with quantum correlations. We find that classical correlations are weaker than quantum correlations in the CHSH framework. With respect to correlation matrices, the trace of classical correlation matrices is dissimilar to quantum density matrices. However, the off-diagonal terms have equivalent interpretations. We contrast classical dynamic (i.e., time evolving) stochastic correlation with dynamic quantum density matrices and find that the off-diagonal elements, while different in nature, have similar interpretations. So far, due to the laws of quantum physics, no classical correlations are applied to the quantum spectrum. However, conversely, quantum correlations are applied in classical environments such as quantum computing, cryptography, metrology, teleportation, medical imaging, laser technology, the quantum Internet and more.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
Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the ...In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the least residual problem of the above generalized inverse eigenvalue problem is studied by using the canonical correlation decomposition（CCD）.The solutions to these problems are derived.Some numerical examples are given to illustrate the main results.展开更多
The key of the subspace-based Direction Of Arrival (DOA) estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel...The key of the subspace-based Direction Of Arrival (DOA) estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.展开更多
In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow rep...In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor(KNN) spatio-temporal attention(STA) graph convolutional network(KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set(CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network(GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30%, 50%, and 70% respectively. The results show that the mean absolute error(MAE), root mean square error(RMSE), and mean absolute percentage error(MAPE) of the KAGCN model increased by at least 3.83%, 2.80%, and 5.33%, respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.展开更多
Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,whi...Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.展开更多
With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the...With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.展开更多
Let{Xk,i;k≥1,i≥1}be an array of random variables,{Xk;k≥1}be a strictly stationaryα-mixing sequence,where Xk=(Xk,1,Xk,2,...).Let{pn;n≥1}be a sequence of positive integers such that c1≤p n n≤c2,where c1,c2>0.I...Let{Xk,i;k≥1,i≥1}be an array of random variables,{Xk;k≥1}be a strictly stationaryα-mixing sequence,where Xk=(Xk,1,Xk,2,...).Let{pn;n≥1}be a sequence of positive integers such that c1≤p n n≤c2,where c1,c2>0.In this paper,we obtain the asymptotic distributions of the largest entries Ln=max1≤i<j≤pn|ρ(n)ij|of the sample correlation matrices,whereρ(n)ij denotes the Pearson correlation coefficient between X(i)and X(j),X(i)=(X1,i,X2,i,...).The asymptotic distributions of Ln is derived by using the Chen–Stein Poisson approximation method.展开更多
The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its pot...The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.展开更多
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 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.
文摘Classical Correlations were founded in 1900 by Karl Pearson and have since been applied as a statistical tool in virtually all sciences. Quantum correlations go back to Albert Einstein et al. in 1935 and Erwin Schrödinger’s responses shortly after. In this paper, we contrast classical with quantum correlations. We find that classical correlations are weaker than quantum correlations in the CHSH framework. With respect to correlation matrices, the trace of classical correlation matrices is dissimilar to quantum density matrices. However, the off-diagonal terms have equivalent interpretations. We contrast classical dynamic (i.e., time evolving) stochastic correlation with dynamic quantum density matrices and find that the off-diagonal elements, while different in nature, have similar interpretations. So far, due to the laws of quantum physics, no classical correlations are applied to the quantum spectrum. However, conversely, quantum correlations are applied in classical environments such as quantum computing, cryptography, metrology, teleportation, medical imaging, laser technology, the quantum Internet and more.
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National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
基金Supported by the Key Discipline Construction Project of Tianshui Normal University
文摘In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the least residual problem of the above generalized inverse eigenvalue problem is studied by using the canonical correlation decomposition（CCD）.The solutions to these problems are derived.Some numerical examples are given to illustrate the main results.
基金Supported by the National Natural Science Foundation of China(No.60372049)
文摘The key of the subspace-based Direction Of Arrival (DOA) estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.
基金supported by the National Natural Science Foundation of China (62162040)the Gansu Provincial Science and Technology Plan Funding Key Project of Natural Science Foundation of China (22JR5RA226)+1 种基金the Gansu Province Higher Education Innovation Fund-Funded Project (2021A-028)the Gansu Provincial Science and Technology Program Funding Project (21ZD4GA028)。
文摘In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor(KNN) spatio-temporal attention(STA) graph convolutional network(KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set(CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network(GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30%, 50%, and 70% respectively. The results show that the mean absolute error(MAE), root mean square error(RMSE), and mean absolute percentage error(MAPE) of the KAGCN model increased by at least 3.83%, 2.80%, and 5.33%, respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.
基金This work was supported by the Science and Technology Project of China Southern Power Grid Corporation(ZBKJXM20180157)the National Natural Science Foundation of China(Grant Nos.61772456,61761136020).
文摘Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.
基金supported by the National Key Research and Development Program of China under Grant No.2016YFB1000601
文摘With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.
基金National Natural Science Foundation of China(Grant Nos.11771178 and 12171198)the Science and Technology Development Program of Jilin Province(Grant No.20210101467JC)+1 种基金Science and Technology Program of Jilin Educational Department during the“13th Five-Year”Plan Period(Grant No.JJKH20200951KJ)Fundamental Research Funds for the Central Universities。
文摘Let{Xk,i;k≥1,i≥1}be an array of random variables,{Xk;k≥1}be a strictly stationaryα-mixing sequence,where Xk=(Xk,1,Xk,2,...).Let{pn;n≥1}be a sequence of positive integers such that c1≤p n n≤c2,where c1,c2>0.In this paper,we obtain the asymptotic distributions of the largest entries Ln=max1≤i<j≤pn|ρ(n)ij|of the sample correlation matrices,whereρ(n)ij denotes the Pearson correlation coefficient between X(i)and X(j),X(i)=(X1,i,X2,i,...).The asymptotic distributions of Ln is derived by using the Chen–Stein Poisson approximation method.
基金supported by the National Natural Science Foundation of China under Grant No.61876117.
文摘The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.
