The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network sele...The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.展开更多
Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation...Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.展开更多
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation....For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.展开更多
As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheles...As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheless,the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data(e.g.,variance and covariance),while more information is neglected.In the context of information theoretic learning,correntropy demonstrates the capacity to grab more information from data.Therefore,under the guidelines of the maximum correntropy criterion,this paper proposes a correntropy-based echo state network(CESN)in which the first-order and higher-order information of data is captured,promoting robustness to noise.Furthermore,an incremental learning algorithm for the CESN is presented,which has the expertise to update the CESN when new data arrives,eliminating the need to retrain the network from scratch.Finally,experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.展开更多
Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve ef...Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve efficient,secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world.As a key component of the CPSs,the wireless network is vulnerable to various malicious attacks due to its openness[1].DoS attack is one of the most common attacks,characterized of simple execution and significant destructiveness[2].To mitigate the economic losses and environmental damage caused by DoS attacks,it is crucial to model and investigate data transmissions in CPSs.展开更多
The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data...The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.展开更多
As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system perform...As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability.Unlike traditional methods that rely on static alternating internal resistance(SAIR)measurements in an open-circuit state,this study presents a real-time state of charge(SOC)estimation method combining dynamic alternating internal resistance(DAIR)with artificial neural networks(ANN).The system simultaneously measures electrochemical impedance various frequencies,discharge C-rate,and battery surface temperature during the∣Z∣atdischarge process,using these parameters for ANN training.The ANN,leveraging its superior nonlinear system modeling capabilities,effectively captures the complex nonlinear relationships between AC impedance and SOC through iterative training.Compared to other machine learning approaches,the proposed ANN features a simpler architecture and lower computational overhead,making it more suitable for integration into battery management system(BMS)microcontrollers.In tests conducted with Samsung batteries using lithium cobalt oxide cathode material,the method achieved an overall average error of merely 0.42%in self-validation,with mean absolute errors(MAE)for individual SOCs not exceeding 1%.Secondary validation demonstrated an overall average error of 1.24%,with MAE for individual SOCs below 2.5%.This integrated DAIR-ANN approach not only provides enhanced estimation accuracy but also simplifies computational requirements,offering a more effective solution for battery management in practical applications.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(...Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(CSI)as real-valued inputs,potentially discarding useful information embedded in the original CSI.In addition,existing positioning models generally face the contradiction between computational complexity and positioning accuracy.To address these issues,we combine graph neural network(GNN)with complex-valued neural network(CVNN)to construct a lightweight indoor positioning model named CGNet.CGNet employs complexvalued convolution operation to directly process the original CSI data,fully exploiting the correlation between real and imaginary parts of CSI while extracting local features.Subsequently,the feature values are treated as nodes,and conditional position encoding(CPE)module is applied to add positional information.To reduce the number of connections in the graph structure and lower themodel complexity,feature information is mapped to an efficient graph structure through a dynamic axial graph construction(DAGC)method,with global features extracted usingmaximum relative graph convolution(MRConv).Experimental results show that,on the CTW dataset,CGNet achieves a 10%improvement in positioning accuracy compared to existing methods,while the number of model parameters is only 0.8 M.CGNet achieves excellent positioning accuracy with very few parameters.展开更多
The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagg...The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping parameters.The addressed neural networks have a finite number of mode...This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping parameters.The addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov process.By construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square.The criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.展开更多
Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protec...Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained data.Compared with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.展开更多
Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investiga...Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.展开更多
Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was pr...Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.展开更多
It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of col...It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.展开更多
Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial con...Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.展开更多
基金National Key Research and Development Program of China(No.2022YFB3903404,2024YFC3015403)National Natural Science Foundation of China(NSFC No.42271431,42271425)Hubei Province Major Science and Technology Innovation Program(2024BAA011)。
文摘The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.
基金supported byNationalNatural Science Foundation of China,GrantNo.62402046the Beijing Forestry University Science and Technology Innovation Project under Grant No.BLX202358.
文摘Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by the National Natural Science Foundation of China(62176214).
文摘For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.
基金supported in part by the National Natural Science Foundation of China(62176109,62476115)the Fundamental Research Funds for the Central Universities(lzujbky-2023-ey07,lzujbky-2023-it14)+1 种基金the Natural Science Foundation of Gansu Province(24JRRA488)the Supercomputing Center of Lanzhou University
文摘As a category of recurrent neural networks,echo state networks(ESNs)have been the topic of in-depth investigations and extensive applications in a diverse array of fields,with spectacular triumphs achieved.Nevertheless,the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data(e.g.,variance and covariance),while more information is neglected.In the context of information theoretic learning,correntropy demonstrates the capacity to grab more information from data.Therefore,under the guidelines of the maximum correntropy criterion,this paper proposes a correntropy-based echo state network(CESN)in which the first-order and higher-order information of data is captured,promoting robustness to noise.Furthermore,an incremental learning algorithm for the CESN is presented,which has the expertise to update the CESN when new data arrives,eliminating the need to retrain the network from scratch.Finally,experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.
文摘Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve efficient,secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world.As a key component of the CPSs,the wireless network is vulnerable to various malicious attacks due to its openness[1].DoS attack is one of the most common attacks,characterized of simple execution and significant destructiveness[2].To mitigate the economic losses and environmental damage caused by DoS attacks,it is crucial to model and investigate data transmissions in CPSs.
基金funded by the Research on SOC/SOH Joint Estimation Technology of Electric Vehicle Battery System State Based on Online Parameter Identification Project(2019)the National Natural Science Foundation of China(Grant No.51877120)。
文摘The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.
文摘As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability.Unlike traditional methods that rely on static alternating internal resistance(SAIR)measurements in an open-circuit state,this study presents a real-time state of charge(SOC)estimation method combining dynamic alternating internal resistance(DAIR)with artificial neural networks(ANN).The system simultaneously measures electrochemical impedance various frequencies,discharge C-rate,and battery surface temperature during the∣Z∣atdischarge process,using these parameters for ANN training.The ANN,leveraging its superior nonlinear system modeling capabilities,effectively captures the complex nonlinear relationships between AC impedance and SOC through iterative training.Compared to other machine learning approaches,the proposed ANN features a simpler architecture and lower computational overhead,making it more suitable for integration into battery management system(BMS)microcontrollers.In tests conducted with Samsung batteries using lithium cobalt oxide cathode material,the method achieved an overall average error of merely 0.42%in self-validation,with mean absolute errors(MAE)for individual SOCs not exceeding 1%.Secondary validation demonstrated an overall average error of 1.24%,with MAE for individual SOCs below 2.5%.This integrated DAIR-ANN approach not only provides enhanced estimation accuracy but also simplifies computational requirements,offering a more effective solution for battery management in practical applications.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
文摘Deep learning has been recognized as an effective method for indoor positioning.However,most existing real-valued neural networks(RVNNs)treat the two constituent components of complex-valued channel state information(CSI)as real-valued inputs,potentially discarding useful information embedded in the original CSI.In addition,existing positioning models generally face the contradiction between computational complexity and positioning accuracy.To address these issues,we combine graph neural network(GNN)with complex-valued neural network(CVNN)to construct a lightweight indoor positioning model named CGNet.CGNet employs complexvalued convolution operation to directly process the original CSI data,fully exploiting the correlation between real and imaginary parts of CSI while extracting local features.Subsequently,the feature values are treated as nodes,and conditional position encoding(CPE)module is applied to add positional information.To reduce the number of connections in the graph structure and lower themodel complexity,feature information is mapped to an efficient graph structure through a dynamic axial graph construction(DAGC)method,with global features extracted usingmaximum relative graph convolution(MRConv).Experimental results show that,on the CTW dataset,CGNet achieves a 10%improvement in positioning accuracy compared to existing methods,while the number of model parameters is only 0.8 M.CGNet achieves excellent positioning accuracy with very few parameters.
基金The National Natural Science Foundation of China (No.50422283)the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)
文摘The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金Project supported by the 2010 Yeungnam University Research Grant
文摘This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping parameters.The addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov process.By construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square.The criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.
基金Acknowledgements Project supported by the National Natural Science Foundation of China （Grant No.60932003）, the National High Technology Development 863 Program of China （Grant No.2007AA01Z452, No. 2009AA01 Z118 ）, Project supported by Shanghai Municipal Natural Science Foundation （Grant No.09ZRI414900）, National Undergraduate Innovative Test Program （091024812）.
文摘Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained data.Compared with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.
基金jointly supported by the National Natural Science Foundation of China(No.61175008)State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System of China(No.CEMEE2014K0301A)the Natural Science Foundation of Jiangsu Province of China(No.BK20140896)
文摘Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.
基金Supported by the National High Technology and Development Program Foundation of China under Grant No. 2002AA420090.
文摘Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.
基金financially supported by grants from the National Natural Science Foundation of China,No.61170136,61373101,61472270,and 61402318Natural Science Foundation(Youth Science and Technology Research Foundation)of Shanxi Province,No.2014021022-5Shanxi Provincial Key Science and Technology Projects(Agriculture),No.20130311037-4
文摘It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.
基金supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009)High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30)
文摘Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.
