Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical,...MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.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
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.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
The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with su...The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with such a topology,the joint optimal design in the physical,medium access control(MAC) and network layers is considered for network lifetime maximization(NLM).The problem of integrating multi-layer information to compute NLM,which involves routing flow,link schedule and transmission power,is formulated as a nonlinear optimization problem.Specially under time division multiple access(TDMA) scheme,this problem can be transformed into a convex optimization problem.To solve it analytically we make use of the property that local optimization is global optimization in convex problem.This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression,i.e.,the globally optimal network lifetime(NL).NL is derived as a function of number of nodes,their initial energy and data rate arrived at them. Based on the analysis of analytical approach,it takes the influence of data rates,link access and routing method over NLM into account.Moreover,the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT++ to compare with other suboptimal schemes.展开更多
System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic...System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic-based approach is proposed to solve the components assignment problem under budget constraint. The mathematical model of the optimization problem is presented and solved by the proposed genetic-based approach. The proposed approach is based on determining the optimal set of lower boundary points that maximize the system reliability such that the total assignment cost does not exceed the specified budget. Finally, to evaluate our approach, we applied it to various network examples with different numbers of available components;two-source two-sink network and three-source two-sink network.展开更多
During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.Th...During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.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
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
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Ch...1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.展开更多
There are two source plants for the traditional Chinese medicine Murrayae Folium et Cacumen(MFC)in Chinese Pharmacopoeia,i.e.Murraya exotica L.and M.paniculata(L.)Jack.Herein,a chemical comparison of M.exotica and M.p...There are two source plants for the traditional Chinese medicine Murrayae Folium et Cacumen(MFC)in Chinese Pharmacopoeia,i.e.Murraya exotica L.and M.paniculata(L.)Jack.Herein,a chemical comparison of M.exotica and M.paniculata by high performance liquid chromatography(HPLC)fingerprint analysis coupled with chemometrics and network pharmacology was performed.The main peaks in the fingerprints were identified by liquid chromatography coupled with ion trap/time-of-flight mass spectrometry(LC-IT-TOF-MS)and authenticated by references.The chemometrics results showed that the HPLC fingerprints of these two species were clearly divided into two categories using hierarchical cluster analysis(HCA)and principal component analysis(PCA),and a total of 13 significantly differentiated markers were screened out by orthogonal partial least squares-discriminant analysis(OPLS-DA).However,the following network pharmacology analysis showed that these discriminated markers were found to act via many common targets and metabolic pathways,indicating the possibly similar pharmacological effects and mechanisms for M.exotica and M.paniculata.The above results provide valuable evidence for the equivalent use of these two plants in clinical settings.Moreover,the chromatographic fingerprint analysis coupled with chemometrics and network pharmacology supplies an efficient approach for the comparative analysis of multi-source TCMs like MFC.展开更多
The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivi...The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.展开更多
Multipath Protocol Label Switching (MPLS) is a routing mechanism (technology) used in modern telecommunication networks, which is built as a multi-service network with a certain Quality of Service (QoS). To maintain t...Multipath Protocol Label Switching (MPLS) is a routing mechanism (technology) used in modern telecommunication networks, which is built as a multi-service network with a certain Quality of Service (QoS). To maintain the required QoS in such complicated networks, the Traffic Engineering (TE) should be equipped with optimum traffic management, routing mechanism, and switching operations, which are what this paper tackles, where the TE is identified as the optimal distribution of flows in the existing network. In this paper, an effective multipath routing mathematical model is developed based on several previous mechanisms to ensure the guaranteed desired QoS and loading balance according to the Telecommunication Network Systems (TCS) resources, to achieve the optimum TE, and hence improving the network usage efficiency. Moreover, the obtained mathematical optimization algorithm results are provided through the relative equations. Indeed the developed routing mathematical model in this paper is suitable for the dynamic distribution of information flows, which is the main improvement in the TE achieved in this work.展开更多
Water shortage is one of the major water related problems for many cities in the world.The planning for utilization of reclaimed water has been or would be drafted in these cities.For using the reclaimed water soundly...Water shortage is one of the major water related problems for many cities in the world.The planning for utilization of reclaimed water has been or would be drafted in these cities.For using the reclaimed water soundly,Beijing planned to build a large scale reclaimed water pipe networks with multi-sources.In order to support the plan,the integrated hydraulic model of planning pipe network was developed based on EPANET supported by geographic information system(GIS).The complicated pipe network was divided into four weak conjunction subzones according to the distribution of reclaimed water plants and the elevation.It could provide a better solution for the problem of overhigh pressure in several regions of the network.Through the scenarios analy-sis in different subzones,some of the initial diameter of pipes in the network was adjusted.At last the pipe network planning scheme of reclaimed water was proposed.The proposed planning scheme could reach the balances between reclaimed water requirements and reclaimed water supplies,and provided a scientific basis for the reclaimed water utilization in Beijing.Now the scheme had been adopted by Beijing municipal government.展开更多
The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs...The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.展开更多
Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.The...Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.展开更多
Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accur...Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.展开更多
This paper proposes a novel algorithm, which can be used to model and analyzemesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain.Not only this algorithm enhances common meth...This paper proposes a novel algorithm, which can be used to model and analyzemesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain.Not only this algorithm enhances common method''s ability on analysis of power/ground network withirregular topology, but also very high accuracy it keeps. The accuracy and stability of thisalgorithm is proved using strict math method in this paper. Also, the usage of both preconditiontechnique based on Incomplete Choleskey Decomposition and fast variable elimination technique hasimproved the algorithm''s efficiency a lot. Experimental results show that it can finish the analysisof power/ground network with enormous, size within very short time. Also, this algorithm can beapplied to analyze the clock network, bus network, and signal network without buffer under highworking frequency because of the independence of the topology.展开更多
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.
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.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
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.
文摘The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with such a topology,the joint optimal design in the physical,medium access control(MAC) and network layers is considered for network lifetime maximization(NLM).The problem of integrating multi-layer information to compute NLM,which involves routing flow,link schedule and transmission power,is formulated as a nonlinear optimization problem.Specially under time division multiple access(TDMA) scheme,this problem can be transformed into a convex optimization problem.To solve it analytically we make use of the property that local optimization is global optimization in convex problem.This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression,i.e.,the globally optimal network lifetime(NL).NL is derived as a function of number of nodes,their initial energy and data rate arrived at them. Based on the analysis of analytical approach,it takes the influence of data rates,link access and routing method over NLM into account.Moreover,the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT++ to compare with other suboptimal schemes.
文摘System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic-based approach is proposed to solve the components assignment problem under budget constraint. The mathematical model of the optimization problem is presented and solved by the proposed genetic-based approach. The proposed approach is based on determining the optimal set of lower boundary points that maximize the system reliability such that the total assignment cost does not exceed the specified budget. Finally, to evaluate our approach, we applied it to various network examples with different numbers of available components;two-source two-sink network and three-source two-sink network.
基金supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China(51221004)the National Natural Science Foundation of China(11172260,11372270,and 51375434)+2 种基金the Higher School Specialized Research Fund for the Doctoral Program(20110101110016)the Science and technology project of Zhejiang Province(2013C31086)the Fundamental Research Funds forthe Central Universities of China(2013XZZX005)
文摘During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.
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.
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co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China under Grant Nos.62272288 and U22A2041the Fundamental Research Funds for the Central Universities of China,and Shaanxi Normal University under Grant No.GK202302006.
文摘1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.
基金supported by the National Natural Sciences Foundation of China(Nos.81773864,81973199,and81473106)the National Key Research and Development Project(No.2019YFC1711000)。
文摘There are two source plants for the traditional Chinese medicine Murrayae Folium et Cacumen(MFC)in Chinese Pharmacopoeia,i.e.Murraya exotica L.and M.paniculata(L.)Jack.Herein,a chemical comparison of M.exotica and M.paniculata by high performance liquid chromatography(HPLC)fingerprint analysis coupled with chemometrics and network pharmacology was performed.The main peaks in the fingerprints were identified by liquid chromatography coupled with ion trap/time-of-flight mass spectrometry(LC-IT-TOF-MS)and authenticated by references.The chemometrics results showed that the HPLC fingerprints of these two species were clearly divided into two categories using hierarchical cluster analysis(HCA)and principal component analysis(PCA),and a total of 13 significantly differentiated markers were screened out by orthogonal partial least squares-discriminant analysis(OPLS-DA).However,the following network pharmacology analysis showed that these discriminated markers were found to act via many common targets and metabolic pathways,indicating the possibly similar pharmacological effects and mechanisms for M.exotica and M.paniculata.The above results provide valuable evidence for the equivalent use of these two plants in clinical settings.Moreover,the chromatographic fingerprint analysis coupled with chemometrics and network pharmacology supplies an efficient approach for the comparative analysis of multi-source TCMs like MFC.
文摘The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.
文摘Multipath Protocol Label Switching (MPLS) is a routing mechanism (technology) used in modern telecommunication networks, which is built as a multi-service network with a certain Quality of Service (QoS). To maintain the required QoS in such complicated networks, the Traffic Engineering (TE) should be equipped with optimum traffic management, routing mechanism, and switching operations, which are what this paper tackles, where the TE is identified as the optimal distribution of flows in the existing network. In this paper, an effective multipath routing mathematical model is developed based on several previous mechanisms to ensure the guaranteed desired QoS and loading balance according to the Telecommunication Network Systems (TCS) resources, to achieve the optimum TE, and hence improving the network usage efficiency. Moreover, the obtained mathematical optimization algorithm results are provided through the relative equations. Indeed the developed routing mathematical model in this paper is suitable for the dynamic distribution of information flows, which is the main improvement in the TE achieved in this work.
基金This work was supported by the Beijing Municipal Planning Committee.The authors thank Mr.Wang Jun and Mr.Liu Jing for their helps.
文摘Water shortage is one of the major water related problems for many cities in the world.The planning for utilization of reclaimed water has been or would be drafted in these cities.For using the reclaimed water soundly,Beijing planned to build a large scale reclaimed water pipe networks with multi-sources.In order to support the plan,the integrated hydraulic model of planning pipe network was developed based on EPANET supported by geographic information system(GIS).The complicated pipe network was divided into four weak conjunction subzones according to the distribution of reclaimed water plants and the elevation.It could provide a better solution for the problem of overhigh pressure in several regions of the network.Through the scenarios analy-sis in different subzones,some of the initial diameter of pipes in the network was adjusted.At last the pipe network planning scheme of reclaimed water was proposed.The proposed planning scheme could reach the balances between reclaimed water requirements and reclaimed water supplies,and provided a scientific basis for the reclaimed water utilization in Beijing.Now the scheme had been adopted by Beijing municipal government.
基金supported by the National Key R&D Program of China (No. 2020YFB0906000 and 2020YFB0906001)。
文摘The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.
文摘Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.
文摘Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.
文摘This paper proposes a novel algorithm, which can be used to model and analyzemesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain.Not only this algorithm enhances common method''s ability on analysis of power/ground network withirregular topology, but also very high accuracy it keeps. The accuracy and stability of thisalgorithm is proved using strict math method in this paper. Also, the usage of both preconditiontechnique based on Incomplete Choleskey Decomposition and fast variable elimination technique hasimproved the algorithm''s efficiency a lot. Experimental results show that it can finish the analysisof power/ground network with enormous, size within very short time. Also, this algorithm can beapplied to analyze the clock network, bus network, and signal network without buffer under highworking frequency because of the independence of the topology.
