The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time ...Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.展开更多
The paper deals with the problem of the asymptotic stability for general continuous nonlinear networked control systems （NCSs）. Based on Lyapunov stability theorem combined with improved Razumikhin technique, the su...The paper deals with the problem of the asymptotic stability for general continuous nonlinear networked control systems （NCSs）. Based on Lyapunov stability theorem combined with improved Razumikhin technique, the sufficient conditions of asymptotic stability for the system are derived. With the proposed method, the estimate of maximum allowable delay bound （MADB） for linear networked control system is also given. Compared to the other methods, the proposed method gives a much less conservative MADB and more general results. Numerical examples and some simulations are worked out to demonstrate the effectiveness and performance of the proposed method.展开更多
Security measures are urgently required to mitigate the recent rapid increase in network security attacks.Although methods employing machine learning have been researched and developed to detect various network attack...Security measures are urgently required to mitigate the recent rapid increase in network security attacks.Although methods employing machine learning have been researched and developed to detect various network attacks effectively,these are passive approaches that cannot protect the network from attacks,but detect them after the end of the session.Since such passive approaches cannot provide fundamental security solutions,we propose an active approach that can prevent further damage by detecting and blocking attacks in real time before the session ends.The proposed technology uses a two-level classifier structure:the first-stage classifier supports real-time classification,and the second-stage classifier supports accurate classification.Thus,the proposed approach can be used to determine whether an attack has occurred with high accuracy,even under heavy traffic.Through extensive evaluation,we confirm that our approach can provide a high detection rate in real time.Furthermore,because the proposed approach is fast,light,and easy to implement,it can be adopted in most existing network security equipment.Finally,we hope to mitigate the limitations of existing security systems,and expect to keep networks faster and safer from the increasing number of cyber-attacks.展开更多
The energy consumption of the information and communication technology sector has become a significant portion of the total global energy consumption, warranting research efforts to attempt to reduce it. The pre-requi...The energy consumption of the information and communication technology sector has become a significant portion of the total global energy consumption, warranting research efforts to attempt to reduce it. The pre-requisite for effectual energy management is the availability of the current power consumption values from network devices. Previous works have attempted to estimate and model the consumption values or have measured it using intrusive approaches such as using an in-line power meter. Recent trends suggest that information models are being increasingly used in all aspects of network management. This paper presents a framework developed for enabling the collection of real-time power consumption information from the next generation of networking hardware non-intrusively by employing information models. The experiment results indicate that it is feasible to gather power consumption data using standardized IETF information models, or non-standard customized information models, or through abstracting and exposing the information in a uniform format when no support for the required information models exists. Functional validation of the proposed framework is performed and the results from this research could be leveraged to make energy-efficient network management decisions.展开更多
This article mainly introduces the multi-layer distributed C/S architecture of system design scheme. Its working principle is the client program runs automatically after the computer starts, and establish communicatio...This article mainly introduces the multi-layer distributed C/S architecture of system design scheme. Its working principle is the client program runs automatically after the computer starts, and establish communication with the application server. The network administrator can monitor and intelligent management of the client computer through the server program, the computer will execute the corresponding operation according to the server to send command instructions. The system realize the main module of the whole system framework, network monitoring data initialization module, network data transmission module, image coding and decoding module, the advantages of system make full use of existing LAN resources, timely delivery and manaRement information.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core cros...Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.展开更多
Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imagin...Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applic...Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.展开更多
In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i...In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.展开更多
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectual...[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectualization. [Method] Based on the application characteristics of Wireless Sensor Network （WSN）, the intelligent greenhouse monitoring system was designed. And for the incompleteness strategy of load balancing in the Low-Energy Adaptive Clustering Hierarchy （LEACH）, a Real- time Threshold Routing Algorithm （RTRA） was proposed. [Result] The performance of network lifetime and network delay of RTRA were tested in MATLAB and found that, within the same testing environment, RTRA can save nodes energy consumption, prolong network lifetime, and had better real-time performance than LEACH. The al- gorithm satisfies the crops＇ requirements on real-time and energy efficiency in the greenhouse system. [Conclusion] For the good performance on real-time, the de- signed intelligent greenhouse real-time monitoring system laid the foundation for the research and development of agricultural informatization and intellectualization.展开更多
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by the National Natural Science Foundation of China(Grant No.U1636208,No.61862008,No.61902013)the Beihang Youth Top Talent Support Program(Grant No.YWF-21-BJJ-1039)。
文摘Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.
基金supported by Hi-Tech Research and Development (863) Program of China (No.2006AA04Z207)Research Fund for Doctorial Programof Higher Education of China (No.20060006018)+1 种基金International Cooperation Program of Science and Technology of China (No.2007DFA11530)the National Natural Science Foundation of China (No.60875072)
文摘The paper deals with the problem of the asymptotic stability for general continuous nonlinear networked control systems （NCSs）. Based on Lyapunov stability theorem combined with improved Razumikhin technique, the sufficient conditions of asymptotic stability for the system are derived. With the proposed method, the estimate of maximum allowable delay bound （MADB） for linear networked control system is also given. Compared to the other methods, the proposed method gives a much less conservative MADB and more general results. Numerical examples and some simulations are worked out to demonstrate the effectiveness and performance of the proposed method.
基金This work was supported in part by the Information Technology Research Center(ITRC)Support Program supervised by the Institute for Information and Communications Technology Planning and Evaluation(IITP)(IITP-2020-2016-0-00313),and in part by and the 2021 Yeungnam University Research Grant.
文摘Security measures are urgently required to mitigate the recent rapid increase in network security attacks.Although methods employing machine learning have been researched and developed to detect various network attacks effectively,these are passive approaches that cannot protect the network from attacks,but detect them after the end of the session.Since such passive approaches cannot provide fundamental security solutions,we propose an active approach that can prevent further damage by detecting and blocking attacks in real time before the session ends.The proposed technology uses a two-level classifier structure:the first-stage classifier supports real-time classification,and the second-stage classifier supports accurate classification.Thus,the proposed approach can be used to determine whether an attack has occurred with high accuracy,even under heavy traffic.Through extensive evaluation,we confirm that our approach can provide a high detection rate in real time.Furthermore,because the proposed approach is fast,light,and easy to implement,it can be adopted in most existing network security equipment.Finally,we hope to mitigate the limitations of existing security systems,and expect to keep networks faster and safer from the increasing number of cyber-attacks.
文摘The energy consumption of the information and communication technology sector has become a significant portion of the total global energy consumption, warranting research efforts to attempt to reduce it. The pre-requisite for effectual energy management is the availability of the current power consumption values from network devices. Previous works have attempted to estimate and model the consumption values or have measured it using intrusive approaches such as using an in-line power meter. Recent trends suggest that information models are being increasingly used in all aspects of network management. This paper presents a framework developed for enabling the collection of real-time power consumption information from the next generation of networking hardware non-intrusively by employing information models. The experiment results indicate that it is feasible to gather power consumption data using standardized IETF information models, or non-standard customized information models, or through abstracting and exposing the information in a uniform format when no support for the required information models exists. Functional validation of the proposed framework is performed and the results from this research could be leveraged to make energy-efficient network management decisions.
文摘This article mainly introduces the multi-layer distributed C/S architecture of system design scheme. Its working principle is the client program runs automatically after the computer starts, and establish communication with the application server. The network administrator can monitor and intelligent management of the client computer through the server program, the computer will execute the corresponding operation according to the server to send command instructions. The system realize the main module of the whole system framework, network monitoring data initialization module, network data transmission module, image coding and decoding module, the advantages of system make full use of existing LAN resources, timely delivery and manaRement information.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
文摘Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.
文摘Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金supported by the National Key Research and Development Program of China(No.2023YFF0715103)-financial supportNational Natural Science Foundation of China(Grant Nos.62306237 and 62006191)-financial support+1 种基金Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-149 and 2021ZDLGY15-04)-financial support,NorthwestUniversity Graduate Innovation Project(No.CX2023194)-financial supportNatural Science Foundation of Shaanxi(No.2023-JC-QN-0750)-financial support.
文摘Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
基金the National Key Research and Development Program of China(No.2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
基金Supported by the Science and Technology Surface Project of Yunnan Province(2010ZC142)the Doctoral Foundation of Dali University(KYBS201015),the Scientific Research Program for College Students of Dali University~~
文摘[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectualization. [Method] Based on the application characteristics of Wireless Sensor Network （WSN）, the intelligent greenhouse monitoring system was designed. And for the incompleteness strategy of load balancing in the Low-Energy Adaptive Clustering Hierarchy （LEACH）, a Real- time Threshold Routing Algorithm （RTRA） was proposed. [Result] The performance of network lifetime and network delay of RTRA were tested in MATLAB and found that, within the same testing environment, RTRA can save nodes energy consumption, prolong network lifetime, and had better real-time performance than LEACH. The al- gorithm satisfies the crops＇ requirements on real-time and energy efficiency in the greenhouse system. [Conclusion] For the good performance on real-time, the de- signed intelligent greenhouse real-time monitoring system laid the foundation for the research and development of agricultural informatization and intellectualization.
