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
This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabili...This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.展开更多
Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacki...Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacking communication infrastructure.Unmanned aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high mobility.In this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN.Firstly,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial layers.The ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the UAVs.The aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for analysis.Secondly,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of CHs.Moreover,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs.Finally,simulation results verify the effectiveness of the proposed algorithms.展开更多
Data collection with microcrystals at synchrotron radiation facilities is challenging because it is difficult to harvest and locate microcrystals. Moreover,microcrystals are sensitive to radiation damage; thus, typica...Data collection with microcrystals at synchrotron radiation facilities is challenging because it is difficult to harvest and locate microcrystals. Moreover,microcrystals are sensitive to radiation damage; thus, typically, a complete data set cannot be obtained with a single microcrystal. Herein, we report a new method for data collection with multiple microcrystals having a crystal size ranging from 1 to 30 lm. This method is suitable for not only low-temperature(100 K) data collection but also room-temperature data collection. Thin Kapton membranes were used to capture multiple crystals simultaneously. The microcrystals were visible under an optical microscope and easily located because the membrane was transparent and sufficiently thin. The film was fixed to a bracket that was prepared using a three-dimensional printer. The bracket was fixed on a magnetic base via screwing and employed by the goniometer system for data collection. Multiple data sets of fatty acid-binding protein 4(FABP4) and lysozyme microcrystals were collected using this novel designed device. Finally, the structures of protein FABP4 and lysozyme were obtained from these data via the molecule replacement method. The data statistics reveal that this method provides a comparable result to traditional methods such as a nylon loop.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
As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT network...As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.展开更多
Underwater data collection is an importance part in the process of network monitoring,network management and intrusion detection.However,the limited-energy of nodes is a major challenge to collect underwater data.The ...Underwater data collection is an importance part in the process of network monitoring,network management and intrusion detection.However,the limited-energy of nodes is a major challenge to collect underwater data.The solution of this problem are not only in the hands of network topology but in the hands of path of autonomous underwater vehicle(AUV).With the problem in hand,an energy-efficient data collection scheme is designed for mobile underwater network.Especially,the data collection scheme is divided into two phases,i.e.,routing algorithm design for sensor nodes and path planing for AUV.With consideration of limited-energy and network robustness,Q-learning based dynamic routing algorithm is designed in the first phase to optimize the routing selection of nodes,through which a potential-game based optimal rigid graph method is proposed to balance the trade-off between the energy consumption and the network robustness.With the collected data,Q-learning based path planning strategy is proposed for AUV in the second phase to find the desired path to gather the data from data collector,then a mode-free tracking controller is developed to track the desired path accurately.Finally,the performance analysis and simulation results reveal that the proposed approach can guarantee energy-efficient and improve network stability.展开更多
This study addressed the issues related to the collection and management of basic data for railway green performance. A railway green performance basic database has been constructed based on metadata and data exchange...This study addressed the issues related to the collection and management of basic data for railway green performance. A railway green performance basic database has been constructed based on metadata and data exchange schemas. A data classification system has been established from the perspectives of businesses, processes,and entities. A BIM(Building Information Modelling) model data extraction scheme is proposed based on field similarity matching and a document content extraction scheme is proposed based on image recognition. A railway green performance basic data collection system has been developed, achieving efficient collection and integrated management of railway green performance basic data. This system can provide data support for applications such as railway carbon emissions accounting, green cost-benefit analysis, and evaluation of green design solutions.展开更多
Self-organizing network(SON)and minimization of driver tests(MDT)are functions designed for Long Term Evolution(LTE)system.SON is designed for network deployment by automatic configuration.MDT is designed for network ...Self-organizing network(SON)and minimization of driver tests(MDT)are functions designed for Long Term Evolution(LTE)system.SON is designed for network deployment by automatic configuration.MDT is designed for network performance evaluation by automatic signalling procedure.However,these functions do not support new features in new radio(NR)access technology,e.g.,multiple radio access technology(RAT)-dual connectivity(MR-DC),central unit-distribute unit(CU-DU)split architecture,beam,etc.Therefore,how to support these features is a challenge for the industry.This paper provides analysis for these problems and provides the summary of SON/MDT functions progress in3 GPP.The analysis includes sub functions such as inter/intra system mobility robustness enhancement,inter/intra system mobility load balance,measurement qualities and mechanism of MDT,energy saving mechanism and procedure,RACH procedure optimization,PCI selection optimization,coverage and capacity optimization,and quality of service(QoS)monitoring mechanism.In addition,this paper also provides an initial thought on artificial intelligence(AI)algorithms applied to SON/MDT functions in NR,so called Smart Grid.展开更多
Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challengin...Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.展开更多
With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users...With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to share.Many privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local devices.Unfortunately,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined tasks.In this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream tasks.To this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized features.With the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except privacy.Extensive experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.展开更多
This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors ha...This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.展开更多
This paper considers an underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes,where the mobile destination requests retransmission from each underwater node ind...This paper considers an underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes,where the mobile destination requests retransmission from each underwater node individually employing traditional automatic-repeat-request(ARQ) protocol.We propose a practical node cooperation(NC) protocol to enhance the collection efficiency,utilizing the fact that underwater nodes can overhear the transmission of others.To reduce the source level of underwater nodes,the underwater data collection area is divided into several sub-zones,and in each sub-zone,the mobile surface node adopting the NC protocol could switch adaptively between selective relay cooperation(SRC) and dynamic network coded cooperation(DNC) .The difference of SRC and DNC lies in whether or not the selected relay node combines the local data and the data overheard from undecoded node(s) to form network coded packets in the retransmission phase.The NC protocol could also be applied across the sub-zones due to the wiretap property.In addition,we investigate the effects of different mobile collection paths,collection area division and cooperative zone design for energy saving.The numerical results showthat the proposed NC protocol can effectively save energy compared with the traditional ARQ scheme.展开更多
Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and...Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.展开更多
Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the e...Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs,and it relates to the network lifetime.In this paper,we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks(WSNs),including the formation of cooperative MIMO and relay link establishment.Firstly,the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with it.Particularly,from the perspective of the node’s energy consumption,the expected cooperative MIMO size and the selection of master node(MN) are proposed.Sequentially,to improve the coverage of the networks and prolong the network lifetime,relay links are established by relay selection algorithm that using matching theory.Finally,the simulation results show that the proposed data collection improves its efficiency,reduces the energy consumption of the master node,improves the networks’ coverage,and extends the network lifetime.展开更多
Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application ...Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.展开更多
A new X-ray absorption fine structure(XAFS)data-collection system based on the Experimental Physics and Industrial Control System software environment has been established at the BL14W1 beamline of the Shanghai Synchr...A new X-ray absorption fine structure(XAFS)data-collection system based on the Experimental Physics and Industrial Control System software environment has been established at the BL14W1 beamline of the Shanghai Synchrotron Radiation Facility. The system provides for automatic sequential analysis of multiple samples for continuous high-throughput(HT) measurements. Specifically, 8 sample pellets are loaded into an alumina holder,and a high-precision two-dimensional translation stage is programmed to switch these samples automatically for collecting the XAFS spectrum of each sample in sequence.Experimenters implement HT measurements via a graphical user interface developed with Control System Studio.Finally, the successful operation of the HT XAFS system is demonstrated by running experiments on two groups of copper–ceria catalysts, each of which contains 8 different powder samples.展开更多
The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Neverth...The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.展开更多
There are several approaches to data collection, such as questionnaires, interviews, observations, focus groups and so on. This essay will look at two specific methods, interviews and questionnaires. It will involve i...There are several approaches to data collection, such as questionnaires, interviews, observations, focus groups and so on. This essay will look at two specific methods, interviews and questionnaires. It will involve individual strengths and weaknesses at first and then have a brief clarification of'why'and'when'to use. Finally, this essay will list out some problems that researchers might face during the process.展开更多
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supported by the National Key Research and Development Program under Grant 2022YFB3303702the Key Program of National Natural Science Foundation of China under Grant 61931001+1 种基金supported by the National Natural Science Foundation of China under Grant No.62203368the Natural Science Foundation of Sichuan Province under Grant No.2023NSFSC1440。
文摘This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.
基金supported by the National Natural Science Foundation of China(NSFC)(61831002,62001076)the General Program of Natural Science Foundation of Chongqing(No.CSTB2023NSCQ-MSX0726,No.cstc2020jcyjmsxmX0878).
文摘Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacking communication infrastructure.Unmanned aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high mobility.In this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN.Firstly,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial layers.The ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the UAVs.The aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for analysis.Secondly,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of CHs.Moreover,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs.Finally,simulation results verify the effectiveness of the proposed algorithms.
基金supported by the Strategic Priority Research program of the Chinese Academy of Sciences(No.XDB08030101)
文摘Data collection with microcrystals at synchrotron radiation facilities is challenging because it is difficult to harvest and locate microcrystals. Moreover,microcrystals are sensitive to radiation damage; thus, typically, a complete data set cannot be obtained with a single microcrystal. Herein, we report a new method for data collection with multiple microcrystals having a crystal size ranging from 1 to 30 lm. This method is suitable for not only low-temperature(100 K) data collection but also room-temperature data collection. Thin Kapton membranes were used to capture multiple crystals simultaneously. The microcrystals were visible under an optical microscope and easily located because the membrane was transparent and sufficiently thin. The film was fixed to a bracket that was prepared using a three-dimensional printer. The bracket was fixed on a magnetic base via screwing and employed by the goniometer system for data collection. Multiple data sets of fatty acid-binding protein 4(FABP4) and lysozyme microcrystals were collected using this novel designed device. Finally, the structures of protein FABP4 and lysozyme were obtained from these data via the molecule replacement method. The data statistics reveal that this method provides a comparable result to traditional methods such as a nylon loop.
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supported by National Natural Science Foundation of China(No.62171158)the project“The Major Key Project of PCL(PCL2021A03-1)”from Peng Cheng Laboratorysupported by the Science and the Research Fund Program of Guangdong Key Laboratory of Aerospace Communication and Networking Technology(2018B030322004).
文摘As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.
基金Supported by the National Natural Science Foundation of China(61873345,62222314)the Distinguished Young Foundation of Hebei Province(F2022203001)+2 种基金the Central Guidance Local Foundation of Hebei Province(226Z3201G)the three-three-three Foundation of Hebei Province(C20221019)the Open Fund Project of Key Laboratory of Ocean Observation Technology,MNR(2021klootA02).
文摘Underwater data collection is an importance part in the process of network monitoring,network management and intrusion detection.However,the limited-energy of nodes is a major challenge to collect underwater data.The solution of this problem are not only in the hands of network topology but in the hands of path of autonomous underwater vehicle(AUV).With the problem in hand,an energy-efficient data collection scheme is designed for mobile underwater network.Especially,the data collection scheme is divided into two phases,i.e.,routing algorithm design for sensor nodes and path planing for AUV.With consideration of limited-energy and network robustness,Q-learning based dynamic routing algorithm is designed in the first phase to optimize the routing selection of nodes,through which a potential-game based optimal rigid graph method is proposed to balance the trade-off between the energy consumption and the network robustness.With the collected data,Q-learning based path planning strategy is proposed for AUV in the second phase to find the desired path to gather the data from data collector,then a mode-free tracking controller is developed to track the desired path accurately.Finally,the performance analysis and simulation results reveal that the proposed approach can guarantee energy-efficient and improve network stability.
基金supported by the Science and Technology Research and Development Plan of China State Railway Group Co.,Ltd.(L2023Z001).
文摘This study addressed the issues related to the collection and management of basic data for railway green performance. A railway green performance basic database has been constructed based on metadata and data exchange schemas. A data classification system has been established from the perspectives of businesses, processes,and entities. A BIM(Building Information Modelling) model data extraction scheme is proposed based on field similarity matching and a document content extraction scheme is proposed based on image recognition. A railway green performance basic data collection system has been developed, achieving efficient collection and integrated management of railway green performance basic data. This system can provide data support for applications such as railway carbon emissions accounting, green cost-benefit analysis, and evaluation of green design solutions.
文摘Self-organizing network(SON)and minimization of driver tests(MDT)are functions designed for Long Term Evolution(LTE)system.SON is designed for network deployment by automatic configuration.MDT is designed for network performance evaluation by automatic signalling procedure.However,these functions do not support new features in new radio(NR)access technology,e.g.,multiple radio access technology(RAT)-dual connectivity(MR-DC),central unit-distribute unit(CU-DU)split architecture,beam,etc.Therefore,how to support these features is a challenge for the industry.This paper provides analysis for these problems and provides the summary of SON/MDT functions progress in3 GPP.The analysis includes sub functions such as inter/intra system mobility robustness enhancement,inter/intra system mobility load balance,measurement qualities and mechanism of MDT,energy saving mechanism and procedure,RACH procedure optimization,PCI selection optimization,coverage and capacity optimization,and quality of service(QoS)monitoring mechanism.In addition,this paper also provides an initial thought on artificial intelligence(AI)algorithms applied to SON/MDT functions in NR,so called Smart Grid.
文摘Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.
基金supported by National Key R&D Program of China (Grant No. 2021ZD0112803)National Natural Science Foundation of China (Grants No. 62122066, U20A20182, 61872274)
文摘With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to share.Many privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local devices.Unfortunately,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined tasks.In this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream tasks.To this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized features.With the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except privacy.Extensive experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.
基金funding from the Australian Government,via Grant No.AUSMURIB000001 associated with ONR MURI Grant No.N00014-19-1-2571。
文摘This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.
基金supported in part by National Key Research and Development Program of China under Grants No.2016YFC1400200 and 2016YFC1400204National Natural Science Foundation of China under Grants No.41476026,41676024 and 41376040Fundamental Research Funds for the Central Universities of China under Grant No.220720140506
文摘This paper considers an underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes,where the mobile destination requests retransmission from each underwater node individually employing traditional automatic-repeat-request(ARQ) protocol.We propose a practical node cooperation(NC) protocol to enhance the collection efficiency,utilizing the fact that underwater nodes can overhear the transmission of others.To reduce the source level of underwater nodes,the underwater data collection area is divided into several sub-zones,and in each sub-zone,the mobile surface node adopting the NC protocol could switch adaptively between selective relay cooperation(SRC) and dynamic network coded cooperation(DNC) .The difference of SRC and DNC lies in whether or not the selected relay node combines the local data and the data overheard from undecoded node(s) to form network coded packets in the retransmission phase.The NC protocol could also be applied across the sub-zones due to the wiretap property.In addition,we investigate the effects of different mobile collection paths,collection area division and cooperative zone design for energy saving.The numerical results showthat the proposed NC protocol can effectively save energy compared with the traditional ARQ scheme.
基金supported by the National Natural Science Foundation of China(62071472,62101556)the Natural Science Foundation of Jiangsu province(BK20200650,BK20210489)the Future Network Scientific Research Fund Project(FNSRFP2021-YB-12)。
文摘Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.
基金supported in part by the program for "Industrial Io T and Emergency Collaboration" Innovative Research Team in CUMT (No.2020ZY002)in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,2021WLKXJ054Postgraduate Research&Practice Innovation Program of China University of Mining and Technology,KYCX21_2242
文摘Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs,and it relates to the network lifetime.In this paper,we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks(WSNs),including the formation of cooperative MIMO and relay link establishment.Firstly,the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with it.Particularly,from the perspective of the node’s energy consumption,the expected cooperative MIMO size and the selection of master node(MN) are proposed.Sequentially,to improve the coverage of the networks and prolong the network lifetime,relay links are established by relay selection algorithm that using matching theory.Finally,the simulation results show that the proposed data collection improves its efficiency,reduces the energy consumption of the master node,improves the networks’ coverage,and extends the network lifetime.
基金supported by the National Science and Technology Support Program of China (2015BAG10B01)the National Science Foundation of China under Grant No. 61232016, No.U1405254the PAPD fund
文摘Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.
基金supported by the National Natural Science Foundation of China under Grant No.21373259
文摘A new X-ray absorption fine structure(XAFS)data-collection system based on the Experimental Physics and Industrial Control System software environment has been established at the BL14W1 beamline of the Shanghai Synchrotron Radiation Facility. The system provides for automatic sequential analysis of multiple samples for continuous high-throughput(HT) measurements. Specifically, 8 sample pellets are loaded into an alumina holder,and a high-precision two-dimensional translation stage is programmed to switch these samples automatically for collecting the XAFS spectrum of each sample in sequence.Experimenters implement HT measurements via a graphical user interface developed with Control System Studio.Finally, the successful operation of the HT XAFS system is demonstrated by running experiments on two groups of copper–ceria catalysts, each of which contains 8 different powder samples.
文摘The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.
文摘There are several approaches to data collection, such as questionnaires, interviews, observations, focus groups and so on. This essay will look at two specific methods, interviews and questionnaires. It will involve individual strengths and weaknesses at first and then have a brief clarification of'why'and'when'to use. Finally, this essay will list out some problems that researchers might face during the process.
