When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of ...Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.展开更多
Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemin...Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.展开更多
Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to u...Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.展开更多
Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable opera...Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.展开更多
In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to q...In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.展开更多
The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic syst...The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.展开更多
The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based...The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation(BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism(DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification.展开更多
In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is pre...In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.展开更多
Shockwaves from fuel-air explosive(FAE)cloud explosions may cause significant casualties.The ground overpressure field is usually used to evaluate the damage range of explosion shockwaves.In this paper,a finite elemen...Shockwaves from fuel-air explosive(FAE)cloud explosions may cause significant casualties.The ground overpressure field is usually used to evaluate the damage range of explosion shockwaves.In this paper,a finite element model of multi-sources FAE explosion is established to simulate the process of multiple shockwaves propagation and interaction.The model is verified with the experimental data of a fourfoldsource FAE explosion,with the total fuel mass of 340 kg.Simulation results show that the overpressure fields of multi-sources FAE explosions are different from that of the single-source.In the case of multisources,the overpressure fields are influenced significantly by source scattering distance and source number.Subsequently,damage ranges of overpressure under three different levels are calculated.Within a suitable source scattering distance,the damage range of multi-sources situation is greater than that of the single-source,under the same amount of total fuel mass.This research provides a basis for personnel shockwave protection from multi-sources FAE explosion.展开更多
Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benef...Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benefits of seawater desalination, the desalination load can be combined with renewable energy sources such as solar energy, wind energy, and ocean energy or with the power grid to ensure its effective regulation. Utilizing energy internet(EI) technology, energy balance demand of the regional power grid, and coordinated control between coastal multi-source multi-load and regional distribution network with desalination load is reviewed herein. Several key technologies, including coordinated control of coastal multi-source multi-load system with seawater desalination load, flexible interaction between seawater desalination and regional distribution network, and combined control of coastal multi-source multi-load storage system with seawater desalination load, are discussed in detail. Adoption of the flexible interaction between seawater desalination and regional distribution networks is beneficial for solving water resource problems, improving the ability to dissipate distributed renewable energy, balancing and increasing grid loads, improving the safety and economy of coastal power grids, and achieving coordinated and comprehensive application of power grids, renewable energy sources, and coastal loads.展开更多
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luan...The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.展开更多
We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information.The locality preserving fusion arises from two-fold,i.e.,the local characterization in both sp...We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information.The locality preserving fusion arises from two-fold,i.e.,the local characterization in both spatial and feature domains.We commence by simultaneously learning a projection matrix,which preserves spatial localities,and a similarity matrix,which encodes feature similarities.We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image.On the other hand,by applying the Laplacian eigen-decomposition to the similarity matrix,we obtain another set of fusion vectors that preserve the feature local similarities.We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image.Finally,we classify the fusion image pixels by a novel sliding ensemble strategy,which enhances the locality preservation in classification.Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g.,multi-spectral and synthetic aperture radar(SAR)images)because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices.Experimental evaluations validate the effectiveness of our framework.展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for...For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.展开更多
The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great...The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.展开更多
With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integ...With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.展开更多
This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based regi...This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.展开更多
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金Under the auspices of Natural Science Foundation of China(No.41971166)。
文摘Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.
基金funded by the High-Quality and Cutting-Edge Discipline Construction Project for Universities in Beijing (Internet Information,Communication University of China).
文摘Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.
文摘Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.
文摘Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.
基金Beijing Municipal Social Science Foundation(22GLC062)Research on service function renewal of Beijing subway station living circle driven by multiple big data.Beijing Municipal Education Commission Social Science Project(KM202010009002)Young YuYou Talents Training Plan of North China University of Technology.
文摘In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.
基金Sponsored by Beijing Natural Science Foundation General Project(8212009)Construction of Philosophy and Social Sciences Base in Beijing-Research on Beijing Urban Renewal and Comprehensive Management of Old Community En-vironment2023 Education Reform Project of North China University of Technology(108051360023XN264-25).
文摘The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.
基金supported by the National Basic Research Program of China(973 Program)(2013CB733000)
文摘The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation(BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism(DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51138003)
文摘In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.
基金The authors would like to acknowledge the China Postdoctoral Science Foundation(Grant No.2019M660488)to provide fund for this work.
文摘Shockwaves from fuel-air explosive(FAE)cloud explosions may cause significant casualties.The ground overpressure field is usually used to evaluate the damage range of explosion shockwaves.In this paper,a finite element model of multi-sources FAE explosion is established to simulate the process of multiple shockwaves propagation and interaction.The model is verified with the experimental data of a fourfoldsource FAE explosion,with the total fuel mass of 340 kg.Simulation results show that the overpressure fields of multi-sources FAE explosions are different from that of the single-source.In the case of multisources,the overpressure fields are influenced significantly by source scattering distance and source number.Subsequently,damage ranges of overpressure under three different levels are calculated.Within a suitable source scattering distance,the damage range of multi-sources situation is greater than that of the single-source,under the same amount of total fuel mass.This research provides a basis for personnel shockwave protection from multi-sources FAE explosion.
基金supported by the State Grid Science and Technology Project, “Study on Multi-source and Multiload Coordination and Optimization Technology Considering Desalination of Sea Water” (No. SGTJDK00DWJS1800011)
文摘Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benefits of seawater desalination, the desalination load can be combined with renewable energy sources such as solar energy, wind energy, and ocean energy or with the power grid to ensure its effective regulation. Utilizing energy internet(EI) technology, energy balance demand of the regional power grid, and coordinated control between coastal multi-source multi-load and regional distribution network with desalination load is reviewed herein. Several key technologies, including coordinated control of coastal multi-source multi-load system with seawater desalination load, flexible interaction between seawater desalination and regional distribution network, and combined control of coastal multi-source multi-load storage system with seawater desalination load, are discussed in detail. Adoption of the flexible interaction between seawater desalination and regional distribution networks is beneficial for solving water resource problems, improving the ability to dissipate distributed renewable energy, balancing and increasing grid loads, improving the safety and economy of coastal power grids, and achieving coordinated and comprehensive application of power grids, renewable energy sources, and coastal loads.
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
文摘The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.
基金The National Natural Science Foundation of China under contract No.61671481the Qingdao Applied Fundamental Research under contract No.16-5-1-11-jchthe Fundamental Research Funds for Central Universities under contract No.18CX05014A
文摘We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information.The locality preserving fusion arises from two-fold,i.e.,the local characterization in both spatial and feature domains.We commence by simultaneously learning a projection matrix,which preserves spatial localities,and a similarity matrix,which encodes feature similarities.We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image.On the other hand,by applying the Laplacian eigen-decomposition to the similarity matrix,we obtain another set of fusion vectors that preserve the feature local similarities.We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image.Finally,we classify the fusion image pixels by a novel sliding ensemble strategy,which enhances the locality preservation in classification.Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g.,multi-spectral and synthetic aperture radar(SAR)images)because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices.Experimental evaluations validate the effectiveness of our framework.
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
基金supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335the Shandong Provincial Natural Science Foundation under Grant JQ201808+5 种基金The Fundamental Research Funds for the Central Universities under Grant 18CX02097Athe Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006111 Project under Grant B08028Sinopec Science and Technology Project under Grant P20050-1
文摘For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金This work was supported by the National Advance Research Program(Item No.Y1601-1).
文摘The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.
基金This work is supported by State Grid Science and Technology Project under Grant No.520613180002,62061318C002the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)+4 种基金Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103)Sanming Science and Technology Project,Grant No.2015-G-6,Shandong province vocational education educational reform research project.Grant No.2017209Study and Development of Smart Agriculture Control System Based on Spark Big Data Decision(2017N0029)Jiangsu Province industrial Communication Technology Application Technology Innovation Team Project.
文摘With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.
文摘This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.
