The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a t...Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.展开更多
In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its as...In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own bandwidth.We aim at maximizing the overall common throughput in a finite time period.Such a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial optimizations.To efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial optimization.Specifically,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating optimization.In the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate schemes.In particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design subproblem.Simulation results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.展开更多
The space-air-ground integrated network(SAGIN)is an important sixth-generation(6G)scenario that is enabled by dynamic spot beam forming by a phased array antenna(PAA).The extremely high mobility of satellites and more...The space-air-ground integrated network(SAGIN)is an important sixth-generation(6G)scenario that is enabled by dynamic spot beam forming by a phased array antenna(PAA).The extremely high mobility of satellites and more complicated radio resource control(RRC)have brought introduced a new challenge,and the issue of determining appropriate moments and procedures for executing handover(HO)for all users in a coverage area is urgent.The existing research considers the users as an entirety,and it determines the HO moment under the assumption that all of the satellite subpoints(SSP)pass through the centre of the cell.However,when using this scheme,the HO failure ratio(HOFR)would experience great degradation caused by the imbalance between the unified HO moments and the uncertain spatial distribution of users’(SDU)spatial-temporal variation.This paper proposes a novel HO moment determination method for a low-orbit satellite internet network(LEO-SIN).The rules of SDU variance under SSP motion are first proposed,and they calculate dynamic UE requests within the constraints of the footprint boundary and with SSP motions.Then,we first formulate the problems of multiuser-directed graphs for HO moment determination and prove that it is a nondeterministic polynomial-time(NP)hard problem.An animal survival algorithm based on the Dingo of algorithm(DOA)is proposed to solve the above problems.Multiuser fused directed graphs are first designed to determine HO moments based on the rules of SDU variation and the animal survival algorithm.The simulations show that the proposed method has a better HO performance for LEO-SIN.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of t...This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.展开更多
In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel ...In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.展开更多
Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performa...Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.展开更多
Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a locatio...Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.展开更多
Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often l...Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.展开更多
Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing r...Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.展开更多
Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,there...Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,thereby worsening the environmental conditions for human health.Extensive research has been conducted in recent years to enhance the fire reaction of traditional asphalt mixtures for the road pavements used in tunnels.The addition of the Flame Retardants(FRs)in conventional asphalt mixtures appears to be promising.Nevertheless,the potential effects of the FRs in terms of the reduction in consequences on tunnel users in the event of a large fire do not seem to have been sufficiently investigated by using fluid dynamics analysis as a computational tool.Given this gap of knowledge,this article aims to quantitatively evaluate whether the use of flame-retarded asphalt mixtures,as opposed to traditional ones without FRs,might mitigate the adverse effects on the safety of evacuees and fire brigade by performing numerical analyses in the case of a tunnel fire.To achieve this goal,3D Computational Fluid Dynamics(CFD)models,which were executed using the Fire Dynamics Simulator(FDS)tool,were established in the case of a major fire of a Heavy Goods Vehicle(HGV)characterized by a maximum Heat Release Rate(HRRmax)of 100 MW.The people evacuation process was also simulated,and the Evac tool was used.Compared to the traditional asphalt pavements without FRs,the simulation findings indicated that the addition of the FRs causes a reduction in CO and CO_(2)levels in the tunnel during the aforementioned fire,with a minor number of evacuees being exposed to the risk of incapacity to self-evacuate,as well as certain safety benefits for the operability of the firefighters entering the tunnel downstream of the fire when the tunnel is naturally ventilated.展开更多
With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce opera...With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.展开更多
Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patien...Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patients admitted to the hospital from July 2023 to September 2024 were enrolled.These patients were randomly assigned to a control group and an intervention group,with 52 patients in each group.The control group received routine nursing care,while the intervention group received an“Internet+”nursing intervention based on user profilingin addition to routine care.The intervention period lasted for one month following discharge.Before and one month after the intervention,the Fear of Progression Questionnaire-Short Form(FOP-Q-SF),the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF),Chinese Posttraumatic Growth Inventory(C-PTGI),and the Functional Assessment of Cancer Therapy-Breast(FACT-B)were applied to assess the effects of interventions.Results:A total of 104 patients were analyzed.After the intervention,FOP-Q-SF and FCRI-SF scores were significantlylower in the intervention group compared to the control group,with statistical significance(t=3.98,P<0.001;t=-7.59,P<0.001),and Cohen’s d of 0.781 and 1.49,respectively.Additionally,CPTGI and FACT-B scores in the intervention group were significantly higher than those in the control group(t=-6.534,P<0.001;t=-4.579,P<0.001),with Cohen’s d of 0.585 and 0.656.Conclusions:An“Internet+”nursing model based on user profilingcould reduce postoperative breast cancer patients fear of disease progression and cancer recurrence,also enhancing posttraumatic growth and overall quality of life.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
Background Exploring how immersive technologies can simulate and assess user experiences in designed environments is an important topic in architectural research.In this study,a multisensory virtual reality(VR)system ...Background Exploring how immersive technologies can simulate and assess user experiences in designed environments is an important topic in architectural research.In this study,a multisensory virtual reality(VR)system developed to support the study of human-built environment interactions under multimodal conditions(visual,olfactory,and auditory)was evaluated.Methods The effectiveness of the system was tested by conducting in-depth user studies using a mixed-method approach to provide quantitative and qualitative evidence.The results of the case study were discussed,key features of the proposed prototype were assessed,and limitations and opportunities for future studies were identified.Results Findings showed that multisensory elements can deepen participants’sense of presence,increase engagement levels,and enrich overall user experience in immersive environments.Integrating olfactory stimuli into virtual representations of architectural spaces revealed how multisensory feedback informs spatial perception and supports the development of more responsive and human-centered design strategies.Conclusions This study contributes to the emerging field of sensory architecture,aiming to move beyond visual simulation toward a richer embodied understanding of space.The proposed approach provides valuable insights into the development of multisensory VR environments in architecture,enabling future research and immersive research methodologies in wider fields.展开更多
User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribut...User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user＇s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.展开更多
In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personal...In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.展开更多
The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interest...The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金Supported by STI 2030 Major Projects of China(2021ZD0200400).
文摘Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.
基金supported by Beijing Natural Science Fund–Haidian Original Innovation Joint Fund(L232040 and L232045).
文摘In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own bandwidth.We aim at maximizing the overall common throughput in a finite time period.Such a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial optimizations.To efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial optimization.Specifically,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating optimization.In the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate schemes.In particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design subproblem.Simulation results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.
基金supported by the China National Key Research and Development Plan(2022YFB2902605)the Beijing Natural Science Foundation(4252008)+2 种基金the 13th 5-Year Plan Civil Aerospace Technology Advance Research Project(D030301)the Hebei Province High-level Talent Funding Project(B2021003032)the New Technology Research University Cooperation Project(SKX212010010).
文摘The space-air-ground integrated network(SAGIN)is an important sixth-generation(6G)scenario that is enabled by dynamic spot beam forming by a phased array antenna(PAA).The extremely high mobility of satellites and more complicated radio resource control(RRC)have brought introduced a new challenge,and the issue of determining appropriate moments and procedures for executing handover(HO)for all users in a coverage area is urgent.The existing research considers the users as an entirety,and it determines the HO moment under the assumption that all of the satellite subpoints(SSP)pass through the centre of the cell.However,when using this scheme,the HO failure ratio(HOFR)would experience great degradation caused by the imbalance between the unified HO moments and the uncertain spatial distribution of users’(SDU)spatial-temporal variation.This paper proposes a novel HO moment determination method for a low-orbit satellite internet network(LEO-SIN).The rules of SDU variance under SSP motion are first proposed,and they calculate dynamic UE requests within the constraints of the footprint boundary and with SSP motions.Then,we first formulate the problems of multiuser-directed graphs for HO moment determination and prove that it is a nondeterministic polynomial-time(NP)hard problem.An animal survival algorithm based on the Dingo of algorithm(DOA)is proposed to solve the above problems.Multiuser fused directed graphs are first designed to determine HO moments based on the rules of SDU variation and the animal survival algorithm.The simulations show that the proposed method has a better HO performance for LEO-SIN.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
文摘This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.
基金supported by the National Key Research and Development Program of China under Grant 2018YFA0701601by the Program of Jiangsu Province under Grant NTACT-2024-Z-001.
文摘In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.
基金supported in part by the Key Technologies R&D Program of Jiangsu under Grants BE2023022 and BE2023022-2National Natural Science Foundation of China under Grants 62471204, 62531015+2 种基金Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24KJA510003Shanghai Kewei 24DP1500500the Fundamental Research Funds for the Central Universities under Grant 2242025K30025
文摘Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.
基金supported by the National Natural Science Foundation of China(61901341).
文摘Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
基金supported by the National Key Project of the National Natural Science Foundation of China under Grant No.U23A20305.
文摘Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.
基金supported by the science and technology foundation of Guizhou province[2022]general 013the science and technology foundation of Guizhou province[2022]general 014+1 种基金the science and technology foundation of Guizhou province GCC[2022]016-1the educational technology foundation of Guizhou province[2022]043.
文摘Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.
文摘Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,thereby worsening the environmental conditions for human health.Extensive research has been conducted in recent years to enhance the fire reaction of traditional asphalt mixtures for the road pavements used in tunnels.The addition of the Flame Retardants(FRs)in conventional asphalt mixtures appears to be promising.Nevertheless,the potential effects of the FRs in terms of the reduction in consequences on tunnel users in the event of a large fire do not seem to have been sufficiently investigated by using fluid dynamics analysis as a computational tool.Given this gap of knowledge,this article aims to quantitatively evaluate whether the use of flame-retarded asphalt mixtures,as opposed to traditional ones without FRs,might mitigate the adverse effects on the safety of evacuees and fire brigade by performing numerical analyses in the case of a tunnel fire.To achieve this goal,3D Computational Fluid Dynamics(CFD)models,which were executed using the Fire Dynamics Simulator(FDS)tool,were established in the case of a major fire of a Heavy Goods Vehicle(HGV)characterized by a maximum Heat Release Rate(HRRmax)of 100 MW.The people evacuation process was also simulated,and the Evac tool was used.Compared to the traditional asphalt pavements without FRs,the simulation findings indicated that the addition of the FRs causes a reduction in CO and CO_(2)levels in the tunnel during the aforementioned fire,with a minor number of evacuees being exposed to the risk of incapacity to self-evacuate,as well as certain safety benefits for the operability of the firefighters entering the tunnel downstream of the fire when the tunnel is naturally ventilated.
基金grateful for the financial support from the National Key R&D Program of China(2023YFB2407300).
文摘With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.
基金funded by the 2023 Hospital Management Innovation Research Project by the Jiangsu Hospital Association(No.JSYGY-2-2023-551)。
文摘Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patients admitted to the hospital from July 2023 to September 2024 were enrolled.These patients were randomly assigned to a control group and an intervention group,with 52 patients in each group.The control group received routine nursing care,while the intervention group received an“Internet+”nursing intervention based on user profilingin addition to routine care.The intervention period lasted for one month following discharge.Before and one month after the intervention,the Fear of Progression Questionnaire-Short Form(FOP-Q-SF),the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF),Chinese Posttraumatic Growth Inventory(C-PTGI),and the Functional Assessment of Cancer Therapy-Breast(FACT-B)were applied to assess the effects of interventions.Results:A total of 104 patients were analyzed.After the intervention,FOP-Q-SF and FCRI-SF scores were significantlylower in the intervention group compared to the control group,with statistical significance(t=3.98,P<0.001;t=-7.59,P<0.001),and Cohen’s d of 0.781 and 1.49,respectively.Additionally,CPTGI and FACT-B scores in the intervention group were significantly higher than those in the control group(t=-6.534,P<0.001;t=-4.579,P<0.001),with Cohen’s d of 0.585 and 0.656.Conclusions:An“Internet+”nursing model based on user profilingcould reduce postoperative breast cancer patients fear of disease progression and cancer recurrence,also enhancing posttraumatic growth and overall quality of life.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
文摘Background Exploring how immersive technologies can simulate and assess user experiences in designed environments is an important topic in architectural research.In this study,a multisensory virtual reality(VR)system developed to support the study of human-built environment interactions under multimodal conditions(visual,olfactory,and auditory)was evaluated.Methods The effectiveness of the system was tested by conducting in-depth user studies using a mixed-method approach to provide quantitative and qualitative evidence.The results of the case study were discussed,key features of the proposed prototype were assessed,and limitations and opportunities for future studies were identified.Results Findings showed that multisensory elements can deepen participants’sense of presence,increase engagement levels,and enrich overall user experience in immersive environments.Integrating olfactory stimuli into virtual representations of architectural spaces revealed how multisensory feedback informs spatial perception and supports the development of more responsive and human-centered design strategies.Conclusions This study contributes to the emerging field of sensory architecture,aiming to move beyond visual simulation toward a richer embodied understanding of space.The proposed approach provides valuable insights into the development of multisensory VR environments in architecture,enabling future research and immersive research methodologies in wider fields.
基金Supported by the National Natural Science Foundation of China(71473183,71503188)
文摘User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user＇s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.
基金The National Natural Science Foundation of China(No60573090,60673139)
文摘In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.
文摘The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.
