The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passi...This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identi...In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.展开更多
We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulati...We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.展开更多
Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrai...Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To ad...In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.展开更多
Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on li...Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T^(2) statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RI...Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.展开更多
The human brain is a complex intelligent system composed of tens of billions of neurons interconnected through synapses,and its intricate network structure has consistently attracted numerous scientists to explore the...The human brain is a complex intelligent system composed of tens of billions of neurons interconnected through synapses,and its intricate network structure has consistently attracted numerous scientists to explore the mysteries of brain functions.However,most existing studies have only verified the biological mimicry characteristics of memristors at the single neuron-synapse level,and there is still a lack of research on memristors simulating synaptic coupling between neurons in multi-neuron networks.Based on this,this paper uses discrete memristors to couple dual discrete Rulkov neurons,and adds synaptic crosstalk between the two discrete memristors to form a neuronal network.A memristor-coupled dual-neuron map,called the Rulkov-memristor-Rulkov(R-M-R)map,is constructed to simulate synaptic connections between neurons in biological tissues.Then,the equilibrium points of the R-M-R map are studied.Subsequently,the effect of parameter variations on the dynamic performance of the R-M-R map is comprehensively analyzed using bifurcation diagram,phase diagram,Lyapunov exponent spectrum(LEs),firing diagram,and spectral entropy(SE)complexity algorithms.In the RM-R map,diverse categories of periodic,chaotic,and hyperchaotic attractors,as well as different states of firing patterns,can be observed.Additionally,different types of state transitions and coexisting attractors are discovered.Finally,the feasibility of the model in digital circuits is verified using a DSP hardware platform.In this study,the coupling principle of biological neurons is simulated,the chaotic dynamic behavior of the R-M-R map is analyzed,and a foundation is laid for deciphering the complex working mechanisms of the brain.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models s...Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.展开更多
Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual ta...Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual tasks such as segmentation and object detection[2].展开更多
A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each n...A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.展开更多
Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the i...Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.展开更多
In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area netwo...In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area network to wideband code division multiple access）. Efficient vertical handoff should allocate network resources efficiently and maintain good quality of service （QoS） for the end users. The objective of this work is to determine conditions under which vertical handoff can be performed. The channel usage situation of each access network is formulated as a birth-death process with the objective of predicting the avaliable bandwidth and the blocking probability. A reward function is used to capture the network bandwidth and the blocking probability is expressed as a cost function. An end user will access the certain network which maximizes the total function defined as the combination of the reward fimction and the cost function. Simulation results show that the proposed algorithm can significantly improve the network performance, including higher bandwidth for end users and lower new call blocking and handoff call blocking probability for networks.展开更多
For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce i...For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.展开更多
This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consens...This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported in part by the National Key Research and Development Program under Grant(2021YFB2900300)by the National Natural Science Foundation of China(NSFC)under Grants 61971127,61871122by the Southeast University-China Mobile Research Institute Joint Innovation Center,and by the Major Key Project of PCL(PCL2021A01-2).
文摘This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
基金supported by Hebei North University Doctoral Research Fund Project(No.BSJJ202315)the Youth Research Fund Project of Higher Education Institutions in Hebei Province(No.QN2024146).
文摘In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.
基金supported by the National Natural Science Foundation of China(Nos.62171204,62171129,62001192).
文摘We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.
文摘Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by the National Natural Science Foundation of China under Grant No.61701100.
文摘In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.
基金supported in part by the National Natural Science Foundation of China(62573387)the Natural Science Foundation of Zhejiang province,China(LY24F030004)the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y).
文摘Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T^(2) statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2242022k60004in part by the National Natural Science Foundation of China(NSFC)under Grants 62261160576,624B2036,W2421087,62422105+1 种基金in part by the Young Elite Scientists Sponsorship Program by CAST 2022QNRC001,and the“Zhishan”Scholars Programs of Southeast Universityin part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022,BE2023022-1 and BE2023022-2.
文摘Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.
基金supported by the National Natural Science Foundation of China(Grant No.62571079)the Technological Innovation Projects in the Field of Artificial Intelligence in Liaoning Province(Grant No.2023JH26/10300011)+1 种基金the Basic Scientific Research Projects in the Department of Education of Liaoning Province(Grant No.LJ212410152049)the Liaoning Provincial Science and Technology Plan Joint Project(Grant No.2025-BSLH-041)。
文摘The human brain is a complex intelligent system composed of tens of billions of neurons interconnected through synapses,and its intricate network structure has consistently attracted numerous scientists to explore the mysteries of brain functions.However,most existing studies have only verified the biological mimicry characteristics of memristors at the single neuron-synapse level,and there is still a lack of research on memristors simulating synaptic coupling between neurons in multi-neuron networks.Based on this,this paper uses discrete memristors to couple dual discrete Rulkov neurons,and adds synaptic crosstalk between the two discrete memristors to form a neuronal network.A memristor-coupled dual-neuron map,called the Rulkov-memristor-Rulkov(R-M-R)map,is constructed to simulate synaptic connections between neurons in biological tissues.Then,the equilibrium points of the R-M-R map are studied.Subsequently,the effect of parameter variations on the dynamic performance of the R-M-R map is comprehensively analyzed using bifurcation diagram,phase diagram,Lyapunov exponent spectrum(LEs),firing diagram,and spectral entropy(SE)complexity algorithms.In the RM-R map,diverse categories of periodic,chaotic,and hyperchaotic attractors,as well as different states of firing patterns,can be observed.Additionally,different types of state transitions and coexisting attractors are discovered.Finally,the feasibility of the model in digital circuits is verified using a DSP hardware platform.In this study,the coupling principle of biological neurons is simulated,the chaotic dynamic behavior of the R-M-R map is analyzed,and a foundation is laid for deciphering the complex working mechanisms of the brain.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金supported in part by the Pioneer Research and Development Program of Zhejiang(2025C01021)Zhejiang Province Postdoctoral Research Project Selection Fund(ZJ2025061)+3 种基金the National Science and Technology Major Project-Intelligent Manufacturing Systems and Robotics of China(2025ZD1602000,2025ZD1601800)the National Natural Science Foundation of China(61933015,62273030,62573387)the Natural Science Foundation of Zhejiang province,China(LY24F030004)the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y)。
文摘Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.
基金the National Natural Science Foundation of China(61966037,61833005,61463052)China Postdoctoral Science Foundation(2017M621586)+1 种基金Program of Yunnan Key Laboratory of Intelligent Systems and Computing(202205AG070003)Postgraduate Science Foundation of Yunnan University(2021Y263)。
文摘Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual tasks such as segmentation and object detection[2].
基金Supported by the Natural Science Foundation ofLiaoning Province (20042020)
文摘A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.
基金the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China under contract No.201105033
文摘Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.
基金Project(20040533035) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject (50275150) supported by the National Natural Science Foundation of China
文摘In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area network to wideband code division multiple access）. Efficient vertical handoff should allocate network resources efficiently and maintain good quality of service （QoS） for the end users. The objective of this work is to determine conditions under which vertical handoff can be performed. The channel usage situation of each access network is formulated as a birth-death process with the objective of predicting the avaliable bandwidth and the blocking probability. A reward function is used to capture the network bandwidth and the blocking probability is expressed as a cost function. An end user will access the certain network which maximizes the total function defined as the combination of the reward fimction and the cost function. Simulation results show that the proposed algorithm can significantly improve the network performance, including higher bandwidth for end users and lower new call blocking and handoff call blocking probability for networks.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61173052)the China Postdoctoral Scinece Foundation(Grant No.2014M561363)
文摘For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.
基金supported in part by the National Natural Science Foundation of China (NSFC)(61703086, 61773106)the IAPI Fundamental Research Funds (2018ZCX27)
文摘This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.
