Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV...Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.展开更多
With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the ...With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’...This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’compliance.A network information management model for MHD patients was constructed around three management schemes:“software reminders+follow-up guidance”,“dietary records+self-management reminders”,and“dialysis plan+precise weight management”.These schemes were respectively used to optimize anemia management,control the risk of hyperphosphatemia,and improve toxin clearance efficiency.A controlled experiment was conducted,with an experimental group and a control group set up for comparative practice.The results showed that the network information management model can effectively improve patients’anemia,help alleviate mineral metabolism disorders and the accumulation of small-molecule toxins,and exert a positive impact on patients’treatment compliance.展开更多
Light-field multispectral radiation thermometry has emerged as a promising non-contact technique for twodimensional surface temperature measurement.However,its performance is still limited by temperature inversion alg...Light-field multispectral radiation thermometry has emerged as a promising non-contact technique for twodimensional surface temperature measurement.However,its performance is still limited by temperature inversion algorithms.In this work,we propose LFMP(light-field multispectral physics-embedded network),a physicsinformed neural network framework designed for temperature inversion in light-field multispectral thermography.The framework explicitly incorporates Planck's law and a reference temperature model into its architecture,thereby enforcing physical consistency and enhancing interpretability.展开更多
This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture de...This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture design of MPLS VPN technology in the ocean information communication network and the important role of RD value and RT value in the VPN instances,the matching strategies of import RT and export RT of different VPN instances are verified through experiments.展开更多
Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neur...Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.展开更多
Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence t...Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.展开更多
A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks.On the basis of traditional multi-path routing,the algorith...A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks.On the basis of traditional multi-path routing,the algorithm uses a random linear network coding strategy to code data pack-ets.Code number is determined by the next hop link status and the number of current received packets sent by the upstream node together.The algorithm improves retransmission and cache mechanisms through using redundancy caused by network coding.Meanwhile,the algorithm also adopts the flow distribution strategy based on time delay to balance network load.Simulation results show that the proposed routing algorithm can effectively improve packet delivery rate,reduce packet delay,and enhance network performance.展开更多
Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the ...Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.展开更多
In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by ...In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).展开更多
Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to dem...Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.展开更多
Frequent inter-satellite link(ISL)handovers will induce service interruption in large-scale space information networks,since traditional distributed/centralized routing strategy-based route convergence/update will con...Frequent inter-satellite link(ISL)handovers will induce service interruption in large-scale space information networks,since traditional distributed/centralized routing strategy-based route convergence/update will consume considerable time(compared with ground networks)derived from long ISL delay and flooding between hundreds or even thousands of satellites.During the network convergence/update stage,the lack of up-to-date forwarding information may cause severe packet loss.Considering the fact that ISL handovers for close-to-earth constellation are predictable and all the ISL handover information could be stored in each satellite during the network initialization,we propose a self-update routing scheme based on open shortest path first(OSPF-SUR)to address the slow route convergence problem caused by frequent ISL handovers.First,for predictable ISL handovers,forwarding tables are updated according to locally stored ISL handover information without link state advertisement(LSA)flooding.Second,for unexpected ISL failures,flooding could be triggered to complete route convergence.In this manner,network convergence time is radically descended by avoiding unnecessary LSA flooding for predictable ISL handovers.Simulation results show that the average packet loss rate caused by ISL handovers is reduced by 90.5%and 61.3%compared with standard OSPF(with three Hello packets confirmation)and OSPF based on interface state(without three Hello packets confirmation),respectively,during a period of topology handover.And the average endto-end delay is also decreased by 47.6%,9.6%,respectively.The packet loss rate of the proposed OSPF-SUR does not change along with the increase of the frequency of topology handovers.展开更多
Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this pape...Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking （ICN） concept for multimedia delivery in urban vehicular networks. By leveraging the 1CN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud （V2C） approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.展开更多
Considering the secure authentication problem for equipment support information network,a clustering method based on the business information flow is proposed. Based on the proposed method,a cluster-based distributed ...Considering the secure authentication problem for equipment support information network,a clustering method based on the business information flow is proposed. Based on the proposed method,a cluster-based distributed authentication mechanism and an optimal design method for distributed certificate authority( CA)are designed. Compared with some conventional clustering methods for network,the proposed clustering method considers the business information flow of the network and the task of the network nodes,which can decrease the communication spending between the clusters and improve the network efficiency effectively. The identity authentication protocols between the nodes in the same cluster and in different clusters are designed. From the perspective of the security of network and the availability of distributed authentication service,the definition of the secure service success rate of distributed CA is given and it is taken as the aim of the optimal design for distributed CA. The efficiency of providing the distributed certificate service successfully by the distributed CA is taken as the constraint condition of the optimal design for distributed CA. The determination method for the optimal value of the threshold is investigated. The proposed method can provide references for the optimal design for distributed CA.展开更多
Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthr...Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.展开更多
The necessity to construct the network management information system of 3PLs agricultural supply chain is analyzed,showing that 3PLs can improve the overall competitive advantage of agricultural supply chain.3PLs chan...The necessity to construct the network management information system of 3PLs agricultural supply chain is analyzed,showing that 3PLs can improve the overall competitive advantage of agricultural supply chain.3PLs changes the homogeneity management into specialized management of logistics service and achieves the alliance of the subjects at different nodes of agricultural products supply chain.Network management information system structure of agricultural products supply chain based on 3PLs is constructed,including the four layers(the network communication layer,the hardware and software environment layer,the database layer,and the application layer)and 7 function modules(centralized control,transportation process management,material and vehicle scheduling,customer relationship,storage management,customer inquiry,and financial management).Framework for the network management information system of agricultural products supply chain based on 3PLs is put forward.The management of 3PLs mainly includes purchasing management,supplier relationship management,planning management,customer relationship management,storage management and distribution management.Thus,a management system of internal and external integrated agricultural enterprises is obtained.The network management information system of agricultural products supply chain based on 3PLs has realized the effective sharing of enterprise information of agricultural products supply chain at different nodes,establishing a long-term partnership revolving around the 3PLs core enterprise,as well as a supply chain with stable relationship based on the supply chain network system,so as to improve the circulation efficiency of agricultural products,and to explore the sales market for agricultural products.展开更多
The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides incre...The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.展开更多
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported in part by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxmX0024,CSTB2022NSCQMSX0600)+5 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscx-dxwtBX0053)。
文摘Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.
基金supported by the National Key Research and Development Program of China(2020YFB1807700)Innovation Capability Support Program of Shaanxi(2024RS-CXTD-01).
文摘With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
文摘This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’compliance.A network information management model for MHD patients was constructed around three management schemes:“software reminders+follow-up guidance”,“dietary records+self-management reminders”,and“dialysis plan+precise weight management”.These schemes were respectively used to optimize anemia management,control the risk of hyperphosphatemia,and improve toxin clearance efficiency.A controlled experiment was conducted,with an experimental group and a control group set up for comparative practice.The results showed that the network information management model can effectively improve patients’anemia,help alleviate mineral metabolism disorders and the accumulation of small-molecule toxins,and exert a positive impact on patients’treatment compliance.
基金National Natural Science Foundation of China(12572325,12172222,22227901,62305184)Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515012932)Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20241202123919027)。
文摘Light-field multispectral radiation thermometry has emerged as a promising non-contact technique for twodimensional surface temperature measurement.However,its performance is still limited by temperature inversion algorithms.In this work,we propose LFMP(light-field multispectral physics-embedded network),a physicsinformed neural network framework designed for temperature inversion in light-field multispectral thermography.The framework explicitly incorporates Planck's law and a reference temperature model into its architecture,thereby enforcing physical consistency and enhancing interpretability.
文摘This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture design of MPLS VPN technology in the ocean information communication network and the important role of RD value and RT value in the VPN instances,the matching strategies of import RT and export RT of different VPN instances are verified through experiments.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1604200)National Natural Science Foundation of China(Grant No.12261131495)+1 种基金Beijing Municipal Science and Technology Commission,Adminitrative Commission of Zhongguancun Science Park(Grant No.Z231100006623006)Institute of Systems Science,Beijing Wuzi University(Grant No.BWUISS21)。
文摘Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.
文摘Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.
基金supported by the National Natural Science Foundation of China(No.60929003)
文摘A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks.On the basis of traditional multi-path routing,the algorithm uses a random linear network coding strategy to code data pack-ets.Code number is determined by the next hop link status and the number of current received packets sent by the upstream node together.The algorithm improves retransmission and cache mechanisms through using redundancy caused by network coding.Meanwhile,the algorithm also adopts the flow distribution strategy based on time delay to balance network load.Simulation results show that the proposed routing algorithm can effectively improve packet delivery rate,reduce packet delay,and enhance network performance.
基金supported by the China Postdoctoral Science Foundation (Grant No.2020M673687)。
文摘Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.
基金supported in part by the Project of International Cooperation and Exchanges NSFC under Grant No.61860206005in part by the Joint Funds of the NSFC under Grant No.U22A2003.
文摘In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).
基金This project was supported by the National "863" High-Tech Research and Development Program of China(2002AA7170)
文摘Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.
基金the National Natural Science Foundations of China(Nos.61771074,62171059)。
文摘Frequent inter-satellite link(ISL)handovers will induce service interruption in large-scale space information networks,since traditional distributed/centralized routing strategy-based route convergence/update will consume considerable time(compared with ground networks)derived from long ISL delay and flooding between hundreds or even thousands of satellites.During the network convergence/update stage,the lack of up-to-date forwarding information may cause severe packet loss.Considering the fact that ISL handovers for close-to-earth constellation are predictable and all the ISL handover information could be stored in each satellite during the network initialization,we propose a self-update routing scheme based on open shortest path first(OSPF-SUR)to address the slow route convergence problem caused by frequent ISL handovers.First,for predictable ISL handovers,forwarding tables are updated according to locally stored ISL handover information without link state advertisement(LSA)flooding.Second,for unexpected ISL failures,flooding could be triggered to complete route convergence.In this manner,network convergence time is radically descended by avoiding unnecessary LSA flooding for predictable ISL handovers.Simulation results show that the average packet loss rate caused by ISL handovers is reduced by 90.5%and 61.3%compared with standard OSPF(with three Hello packets confirmation)and OSPF based on interface state(without three Hello packets confirmation),respectively,during a period of topology handover.And the average endto-end delay is also decreased by 47.6%,9.6%,respectively.The packet loss rate of the proposed OSPF-SUR does not change along with the increase of the frequency of topology handovers.
基金partially supported by the Fundamental Research Funds for the Central Universities under Grant No.2015JBM009the National Natural Science Foundation of China(NSFC) under Grant 61602030 U1404611,61301081+1 种基金the Project Funded by China Postdoctoral Science Foundation under Grant No.2016T90031,2015M570028 and 2015M580970the Program for Science & Technology Innovation Talents in the University of Henan Province under Grant No.16HASTIT035
文摘Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking （ICN） concept for multimedia delivery in urban vehicular networks. By leveraging the 1CN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud （V2C） approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.
基金National Natural Science Foundation of China(No.61271152)Natural Science Foundation of Hebei Province,China(No.F2012506008)the Original Innovation Foundation of Ordnance Engineering College,China(No.YSCX0903)
文摘Considering the secure authentication problem for equipment support information network,a clustering method based on the business information flow is proposed. Based on the proposed method,a cluster-based distributed authentication mechanism and an optimal design method for distributed certificate authority( CA)are designed. Compared with some conventional clustering methods for network,the proposed clustering method considers the business information flow of the network and the task of the network nodes,which can decrease the communication spending between the clusters and improve the network efficiency effectively. The identity authentication protocols between the nodes in the same cluster and in different clusters are designed. From the perspective of the security of network and the availability of distributed authentication service,the definition of the secure service success rate of distributed CA is given and it is taken as the aim of the optimal design for distributed CA. The efficiency of providing the distributed certificate service successfully by the distributed CA is taken as the constraint condition of the optimal design for distributed CA. The determination method for the optimal value of the threshold is investigated. The proposed method can provide references for the optimal design for distributed CA.
基金Project supported by the National Key Research and Development Program of China(Grant No.2020YFC1807905)the National Natural Science Foundation of China(Grant Nos.52079090 and U20A20316)the Basic Research Program of Qinghai Province(Grant No.2022-ZJ-704).
文摘Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.
文摘The necessity to construct the network management information system of 3PLs agricultural supply chain is analyzed,showing that 3PLs can improve the overall competitive advantage of agricultural supply chain.3PLs changes the homogeneity management into specialized management of logistics service and achieves the alliance of the subjects at different nodes of agricultural products supply chain.Network management information system structure of agricultural products supply chain based on 3PLs is constructed,including the four layers(the network communication layer,the hardware and software environment layer,the database layer,and the application layer)and 7 function modules(centralized control,transportation process management,material and vehicle scheduling,customer relationship,storage management,customer inquiry,and financial management).Framework for the network management information system of agricultural products supply chain based on 3PLs is put forward.The management of 3PLs mainly includes purchasing management,supplier relationship management,planning management,customer relationship management,storage management and distribution management.Thus,a management system of internal and external integrated agricultural enterprises is obtained.The network management information system of agricultural products supply chain based on 3PLs has realized the effective sharing of enterprise information of agricultural products supply chain at different nodes,establishing a long-term partnership revolving around the 3PLs core enterprise,as well as a supply chain with stable relationship based on the supply chain network system,so as to improve the circulation efficiency of agricultural products,and to explore the sales market for agricultural products.
基金supported by the Corporation Science and Technology Program of Global Energy Interconnection Group Ltd. (GEIGC-D-[2018]024)by the National Natural Science Foundation of China (61472042, 61772079)
文摘The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.
