A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weigh...A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weights of reservoir parameters through sample training and genetic algorithm was used to optimize the initial connection weights of nerve cells in case the neural network fell into a local minimum. Additionally, subordinate functions of each parameter were established to normalize the actual values of parameters of coalbed methane reservoirs in the range between zero and unity. Eventually, evaluation values of all coalbed methane reservoirs could be obtained by using the comprehensive evaluation method, which is the basis to rank the coalbed methane reservoirs in the order of exploitation priority. The greater the evaluation value, the higher the exploitation priority. The ranking method was verified in this paper by ten exploited coalbed methane reservoirs in China. The evaluation results are in agreement with the actual exploitation cases. The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts. Furthermore, the probability of falling into local minima is reduced, because genetic the algorithm is used to optimize the neural network system.展开更多
An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage faciliti...An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.展开更多
This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the cli...This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.展开更多
In Japanese 'e-government' policy, called 'e-Japan', the 'administrative document management system' is functioning as information searching systems. On the other hand, this system has also gen...In Japanese 'e-government' policy, called 'e-Japan', the 'administrative document management system' is functioning as information searching systems. On the other hand, this system has also generated the problem that it is not fully functioning as a means for the information sharing in a governmental agency. So, the purpose of this research is to find how the administrative document management system can function as information sharing in administrative organization. For this purpose, this paper considers the current status and some problems firstly. And secondary, this paper proposes the idea and constructs some information systems using administrative official Website. This is the method and approach of this research. As a conclusion, this proposal information system junctions as information sharing support systems.展开更多
Abstract： Real-time digital service and mul- timedia service upstream transmission in Dig- ital Signal Processing （DSP）-based Orthogo- nal Frequency Division Multiplexing-Passive Optical Network （OFDM-PON） is exp...Abstract： Real-time digital service and mul- timedia service upstream transmission in Dig- ital Signal Processing （DSP）-based Orthogo- nal Frequency Division Multiplexing-Passive Optical Network （OFDM-PON） is experimen- tally demonstrated with Centralised Light Sou- rce （CLS） configuration in this paper. After transmitted over 25 km Standard Single Mode Fibre （SSMF） with -16.5 dBm optical power at receiver, the Bit Error Rate （BER） is 9.5 ×10^-11. The implementations of digital domain up-conversion and down-conversion based on Field Programmable Gate Array （FPGA） are int- roduced, which can reduce the cost of In-ph- ase and Quadrature （IQ） radio frequency mix- ers utilised at transmitter and receiver. A car- rier synchronization algorithm is implemented for compensating carrier offset. A channel eq- ualization algorithm is adopted for compen- sating the damage of channel. A new structure of Frequency Synchronization Unit （FSU） des- igned in FPGA is also proposed to cope with the frequency shifting at receiver.展开更多
Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,th...Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.展开更多
The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit ...The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol(BGP).Studying the ecological evolution of BGP network is of great significance for anal...The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol(BGP).Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet.This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years,and innovatively studies the relationship between Country-Level BGP network and economy,breaking through the limitations of traditional research that only focuses on BGP network.The results revealed that the number of global BGP networks has increased by nearly 23 times and that network interconnection has increased nearly 80 times over in 24 years.It was found that the growth of the global BGP network ecosystem has slowed overall due to major global security events,although the BGP network ecosystem in some Southeast Asian countries is developing against the trend.At the same time,there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension;there is a strong positive correlation in the spatial dimension,but the trend is weakening year by year.展开更多
Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Different from the adaptive signal processing at the receiver in adaptive radar,...Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Different from the adaptive signal processing at the receiver in adaptive radar,the cognitive radar realizes closedloop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment.As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart,the wireless networked cognitive radar(WNCR)attracts increasing research.This article firstly reviews the concept and development of cognitive radar,especially the related researches of networked cognitive radar.Then,the co-design of cognitive radar and communication is investigated.Although the communication quality between radar sensing nodes is the premise of detection,tracking,imaging and anti-jamming performance of the WNCR,the latest researches seldom consider the communication architecture design for WNCR.Therefore,this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application,and the corresponding guidelines are proposed to inspire future research.展开更多
Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic partic...Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.展开更多
In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Se...In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.展开更多
This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknow...This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.展开更多
A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DF...A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.展开更多
Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commo...Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph d...Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph distillation methods address this challenge by extracting a smaller,reduced graph,ensuring that GNNs trained on both the original and reduced graphs show similar performance.Existing methods,however,primarily optimize the feature matrix of the reduced graph and rely on correlation information from GNNs,while neglecting the original graph’s structure and redundant nodes.This often results in a loss of critical information within the reduced graph.To overcome this limitation,we propose a graph distillation method guided by network symmetry.Specifically,we identify symmetric nodes with equivalent neighborhood structures and merge them into“super nodes”,thereby simplifying the network structure,reducing redundant parameter optimization and enhancing training efficiency.At the same time,instead of relying on the original node features,we employ gradient descent to match optimal features that align with the original features,thus improving downstream task performance.Theoretically,our method guarantees that the reduced graph retains the key information present in the original graph.Extensive experiments demonstrate that our approach achieves significant improvements in graph distillation,exhibiting strong generalization capability and outperforming existing graph reduction methods.展开更多
Friendship paradox states that individuals are likely to have fewer friends than their friends do,on average.Despite of its wide existence and appealing applications in real social networks,the mathematical understand...Friendship paradox states that individuals are likely to have fewer friends than their friends do,on average.Despite of its wide existence and appealing applications in real social networks,the mathematical understanding of friendship paradox is very limited.Only few works provide theoretical evidence of single-step and multi-step friendship paradoxes,given that the neighbors of interest are onehop and multi-hop away from the target node.However,they consider non-evolving networks,as opposed to the topology of real social networks that are constantly growing over time.We are thus motivated to present a first look into friendship paradox in evolving networks,where newly added nodes preferentially attach themselves to those with higher degrees.Our analytical verification of both single-step and multistep friendship paradoxes in evolving networks,along with comparison to the non-evolving counterparts,discloses that“friendship paradox is even more paradoxical in evolving networks”,primarily from three aspects:1)we demonstrate a strengthened effect of single-step friendship paradox in evolving networks,with a larger probability(more than 0.8)of a random node’s neighbors having higher average degree than the random node itself;2)we unravel higher effectiveness of multi-step friendship paradox in seeking for influential nodes in evolving networks,as the rate of reaching the max degree node can be improved by a factor of at least Θ(t^(2/3))with t being the network size;3)we empirically verify our findings through both synthetic and real datasets,which suggest high agreements of results and consolidate the reasonability of evolving model for real social networks.展开更多
Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in dif...Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in different networks cannot be compared directly.This paper proposes a network selection algorithm for heterogeneous network.Firstly,the concept of equivalent bandwidth is proposed,through which the actual resources occupied by users with certain QoS requirements in different networks can be compared directly.Then the concept of network applicability is defined to express the abilities of networks to support different services.The proposed network selection algorithm first evaluates whether the network has enough equivalent bandwidth required by the user and then prioritizes network with poor applicability to avoid the situation that there are still residual resources in entire network,but advanced services can not be admitted.The simulation results show that the proposed algorithm obtained better performance than the baselines in terms of reducing call blocking probability and improving network resource utilization efficiency.展开更多
This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid s...This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.展开更多
基金EU-China Energy and Environment Programme(Europe Aid/120723/D/SV/CN)Research Fund for the Doctoral Program of Higher Education of China(20030425001)
文摘A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weights of reservoir parameters through sample training and genetic algorithm was used to optimize the initial connection weights of nerve cells in case the neural network fell into a local minimum. Additionally, subordinate functions of each parameter were established to normalize the actual values of parameters of coalbed methane reservoirs in the range between zero and unity. Eventually, evaluation values of all coalbed methane reservoirs could be obtained by using the comprehensive evaluation method, which is the basis to rank the coalbed methane reservoirs in the order of exploitation priority. The greater the evaluation value, the higher the exploitation priority. The ranking method was verified in this paper by ten exploited coalbed methane reservoirs in China. The evaluation results are in agreement with the actual exploitation cases. The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts. Furthermore, the probability of falling into local minima is reduced, because genetic the algorithm is used to optimize the neural network system.
基金supported by National Natural Science Foundation of China[grant number 51904316]provided by China University of Petroleum,Beijing[grant number2462021YJRC013,2462020YXZZ045]
文摘An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.
文摘This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.
文摘In Japanese 'e-government' policy, called 'e-Japan', the 'administrative document management system' is functioning as information searching systems. On the other hand, this system has also generated the problem that it is not fully functioning as a means for the information sharing in a governmental agency. So, the purpose of this research is to find how the administrative document management system can function as information sharing in administrative organization. For this purpose, this paper considers the current status and some problems firstly. And secondary, this paper proposes the idea and constructs some information systems using administrative official Website. This is the method and approach of this research. As a conclusion, this proposal information system junctions as information sharing support systems.
基金ACKNOWLEDGEMENT This work was supported in part by the Na- tional Natural Science Foundation of China under Grants No. 61271192, No. 60932004 the National High Technology Research and Development of China （863 Program） under Grant No. 2013AA013401 and the National Basic Research Program of China under Grant No. 2013CB329204.
文摘Abstract： Real-time digital service and mul- timedia service upstream transmission in Dig- ital Signal Processing （DSP）-based Orthogo- nal Frequency Division Multiplexing-Passive Optical Network （OFDM-PON） is experimen- tally demonstrated with Centralised Light Sou- rce （CLS） configuration in this paper. After transmitted over 25 km Standard Single Mode Fibre （SSMF） with -16.5 dBm optical power at receiver, the Bit Error Rate （BER） is 9.5 ×10^-11. The implementations of digital domain up-conversion and down-conversion based on Field Programmable Gate Array （FPGA） are int- roduced, which can reduce the cost of In-ph- ase and Quadrature （IQ） radio frequency mix- ers utilised at transmitter and receiver. A car- rier synchronization algorithm is implemented for compensating carrier offset. A channel eq- ualization algorithm is adopted for compen- sating the damage of channel. A new structure of Frequency Synchronization Unit （FSU） des- igned in FPGA is also proposed to cope with the frequency shifting at receiver.
基金supported by the Science and Technology Project of the State Grid Corporation of China(5400-202255158A-1-1-ZN).
文摘Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.
基金supported by the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20210347)。
文摘The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
文摘The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol(BGP).Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet.This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years,and innovatively studies the relationship between Country-Level BGP network and economy,breaking through the limitations of traditional research that only focuses on BGP network.The results revealed that the number of global BGP networks has increased by nearly 23 times and that network interconnection has increased nearly 80 times over in 24 years.It was found that the growth of the global BGP network ecosystem has slowed overall due to major global security events,although the BGP network ecosystem in some Southeast Asian countries is developing against the trend.At the same time,there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension;there is a strong positive correlation in the spatial dimension,but the trend is weakening year by year.
基金This work was supported by the National Natural Science Foundation of China under Grant No.91948303.
文摘Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Different from the adaptive signal processing at the receiver in adaptive radar,the cognitive radar realizes closedloop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment.As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart,the wireless networked cognitive radar(WNCR)attracts increasing research.This article firstly reviews the concept and development of cognitive radar,especially the related researches of networked cognitive radar.Then,the co-design of cognitive radar and communication is investigated.Although the communication quality between radar sensing nodes is the premise of detection,tracking,imaging and anti-jamming performance of the WNCR,the latest researches seldom consider the communication architecture design for WNCR.Therefore,this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application,and the corresponding guidelines are proposed to inspire future research.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305303)the Natural Science Foundation of Hunan Province of China(Grant Nos.2023JJ40520,2021JJ40444,and 2019JJ30019)+3 种基金the Research Foundation of Education Bureau of Hunan Province of China(Grant No.20A430)the Science and Technology Innovation Program of Hunan Province(Grant No.2020RC3054)the Natural Science Basic Research Plan in the Shaanxi Province of China(Grant No.2023-JC-QN-0015)the Doctoral Research Fund of University of South China。
文摘Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.
基金supported by the National Natural Science Foundation of China(Grant No.61971057).
文摘In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.
文摘This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.
文摘A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.
文摘Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
基金Project supported by the National Natural Science Foundation of China(Grant No.62176217)the Program from the Sichuan Provincial Science and Technology,China(Grant No.2018RZ0081)the Fundamental Research Funds of China West Normal University(Grant No.17E063).
文摘Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph distillation methods address this challenge by extracting a smaller,reduced graph,ensuring that GNNs trained on both the original and reduced graphs show similar performance.Existing methods,however,primarily optimize the feature matrix of the reduced graph and rely on correlation information from GNNs,while neglecting the original graph’s structure and redundant nodes.This often results in a loss of critical information within the reduced graph.To overcome this limitation,we propose a graph distillation method guided by network symmetry.Specifically,we identify symmetric nodes with equivalent neighborhood structures and merge them into“super nodes”,thereby simplifying the network structure,reducing redundant parameter optimization and enhancing training efficiency.At the same time,instead of relying on the original node features,we employ gradient descent to match optimal features that align with the original features,thus improving downstream task performance.Theoretically,our method guarantees that the reduced graph retains the key information present in the original graph.Extensive experiments demonstrate that our approach achieves significant improvements in graph distillation,exhibiting strong generalization capability and outperforming existing graph reduction methods.
基金supported by NSF China(No.61960206002,62020106005,42050105,62061146002)Shanghai Pilot Program for Basic Research–Shanghai Jiao Tong University.
文摘Friendship paradox states that individuals are likely to have fewer friends than their friends do,on average.Despite of its wide existence and appealing applications in real social networks,the mathematical understanding of friendship paradox is very limited.Only few works provide theoretical evidence of single-step and multi-step friendship paradoxes,given that the neighbors of interest are onehop and multi-hop away from the target node.However,they consider non-evolving networks,as opposed to the topology of real social networks that are constantly growing over time.We are thus motivated to present a first look into friendship paradox in evolving networks,where newly added nodes preferentially attach themselves to those with higher degrees.Our analytical verification of both single-step and multistep friendship paradoxes in evolving networks,along with comparison to the non-evolving counterparts,discloses that“friendship paradox is even more paradoxical in evolving networks”,primarily from three aspects:1)we demonstrate a strengthened effect of single-step friendship paradox in evolving networks,with a larger probability(more than 0.8)of a random node’s neighbors having higher average degree than the random node itself;2)we unravel higher effectiveness of multi-step friendship paradox in seeking for influential nodes in evolving networks,as the rate of reaching the max degree node can be improved by a factor of at least Θ(t^(2/3))with t being the network size;3)we empirically verify our findings through both synthetic and real datasets,which suggest high agreements of results and consolidate the reasonability of evolving model for real social networks.
文摘Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in different networks cannot be compared directly.This paper proposes a network selection algorithm for heterogeneous network.Firstly,the concept of equivalent bandwidth is proposed,through which the actual resources occupied by users with certain QoS requirements in different networks can be compared directly.Then the concept of network applicability is defined to express the abilities of networks to support different services.The proposed network selection algorithm first evaluates whether the network has enough equivalent bandwidth required by the user and then prioritizes network with poor applicability to avoid the situation that there are still residual resources in entire network,but advanced services can not be admitted.The simulation results show that the proposed algorithm obtained better performance than the baselines in terms of reducing call blocking probability and improving network resource utilization efficiency.
基金Project supported by Jilin Provincial Science and Technology Development Plan(Grant No.20220101137JC).
文摘This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.
