Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Compl...Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.展开更多
In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computi...In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.展开更多
Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The stand...Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The standard evolution and industry development are summarized.There are various positioning systems according to the scenarios,cost and accuracy.However,there is a basic positioning system framework including information extraction,measurement and calculation.In particular,the detailed positioning technologies mainly including cellular positioning and Local Area Network(LAN) positioning are listed and compared to provide a reference for practical applications.Finally,we summarize the challenges of indoor positioning and give a3-phase evolution route.展开更多
To solve the contradiction between the increasing demand of diverse vehicular wireless applications and the shortage of spectrum resource, a novel cognitive cooperative vehicular ad-hoc network （CC- VANET） framework...To solve the contradiction between the increasing demand of diverse vehicular wireless applications and the shortage of spectrum resource, a novel cognitive cooperative vehicular ad-hoc network （CC- VANET） framework is proposed in this paper. Firstly, we develop an adaptive cognitive spectrum sensing （ACSS） mechanism which can help to trigger and adjust the spectrum sensing window according to network traffic load status and communication quality. And then, Generalized Nash Bargaining SoLution （GNBS）, which can achieve a good tradeoff between efficiency and weighted fairness, is proposed to formulate the asymmetric inter- cell resource allocation. Finally, GNBS- Safety （GNBS-S） scheme is developed to enhance the Quality of Service （QoS） of safety applications, especially in the heavy load status, where the bandwidth demanded and supplied cannot be matched well. Furthermore, the primary user activity （PUA） which can cause rate loss to secondary users, is also considered to alleviate its influence to fairness. Simulation results indicate that the proposed CC-VANET scheme can greatly improve the spectrum efficiency and reduce the transmission delay and packet loss rate on the heavy contention status. And GNBS spectrum allocation scheme outperforms both the Max-rain and Max-rate schemes, and canenhance the communication reliability of safety service considerably in CC-VANET.展开更多
Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consis...Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consists of two allocation stages, dominant resource fair(DRF) allocation stage and max-min fairness(MMF) allocation stage. The proposed DMMF scheme exhibits desirable properties, including share incentive, strategy proofness, envy freeness and Pareto optimality. Meanwhile, DMMF can improve the allocation efficiency and reach 100% allocation efficiency.展开更多
With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation method...With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.展开更多
A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （...A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.展开更多
DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of c...DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.展开更多
With the rapid development of the WLAN,the 802. 11 s mesh network is emerging.Reliability,adaptability and scalability are the most important attributes of a mesh network.However,the security in an 802.11s mesh networ...With the rapid development of the WLAN,the 802. 11 s mesh network is emerging.Reliability,adaptability and scalability are the most important attributes of a mesh network.However,the security in an 802.11s mesh network is not well defined or specified,and there is no standard method to authenticate a mesh point that is creating a mesh link.In this paper,we propose solutions for the authentication of mesh points.For the Basic Infrastructure Security Mode,we combine the 802.1X/EAP and neighbor graph to realize the fast mutual authentication between a new mesh point and every its mesh link.We prove that our scheme maintains the security of the standardized EAP authentication algorithm.At the same time,the simulation result shows that the authentication latency of our scheme is much lower than that of the scheme in[2].In the Basic Decentralized Security Mode(BDSM),802.11s mesh networks are very similar to ad hoc networks,but they are different in several aspects.We first analyze the differences between them.Based on this analysis, we investigate the main authentication methods used in ad hoc networks and select the pairwise key pre-distribution model and identity-based model for the WLAN mesh network.展开更多
Synchronization is a process that describes the coherent dynamics of a large ensemble of interacting units.The study of explosive synchronization transition attracts considerable attention.Here,I report the explosive ...Synchronization is a process that describes the coherent dynamics of a large ensemble of interacting units.The study of explosive synchronization transition attracts considerable attention.Here,I report the explosive transition within the framework of a mobile network,while each oscillator is controlled by global-order parameters of the system.Using numerical simulation,I find that the explosive synchronization(ES)transition behavior can be controlled by simply adjusting the fraction of controlled oscillators.The influences of some parameters on explosive synchronization are studied.Moreover,due to the presence of the positive feedback mechanism,I prevent the occurrence of the synchronization of continuous-phase transition and make phase transition of the system a first-order phase transition accompanied by a hysteresis loop.展开更多
Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the compu...Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the computational concept of Decision-Making of cognition intelligence,its implementation framework adapting to foreseen innovations on networks and services,and its empirical evaluations are key techniques to guarantee the generationagnostic intelligence evolution of wireless communication networks.In this paper,we propose an Intelligent Decision Making(IDM)framework,acting as the role of network brain,based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network.Besides,usage scenarios and simulation demonstrate the generality and efficiency of IDM.We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.展开更多
In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with ...In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.展开更多
Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coup...The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.展开更多
Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present ...Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present an efficient constant-round group key exchange protocol, which is provably secure under the intractability of computation Diffie-Hellman problem. Our protocol is a contributory key exchange with perfect forward secrecy and has only two communication rounds. So it is more efficient than other protocols. Moreover, our protocol provides a method to design efficient constant-round group key exchange protocols and most secret sharing schemes could be adopted to construct our protocol.展开更多
As an innovative software application mode,Software as a service(SaaS) shows many attractive advantages.Migrating legacy system to SaaS can make outdated systems revived.In the process of migration,the existing valuab...As an innovative software application mode,Software as a service(SaaS) shows many attractive advantages.Migrating legacy system to SaaS can make outdated systems revived.In the process of migration,the existing valuable components need to be discovered and reused in order that the target system could be developed/integrated more efficiently.An innovative approach is proposed in this paper to extract the reusable components from legacy systems.Firstly,implementation models of legacy system are recovered through reverse engineering.Secondly,function models are derived by vertical clustering,and then logical components are discovered by horizontal clustering based on the function models.Finally,the reusable components with specific feature descriptions are extracted.Through experimental verification,the approach is considered to be efficient in reusable component discovery and to be helpful to migrating legacy system to SaaS.展开更多
Understanding cardiac blood flow behaviors is of importance for cardiovascular research and clinical assessment of ventricle functions.Patient-specific Echo-based left ventricle(LV)fluid-structure interaction(FSI)mode...Understanding cardiac blood flow behaviors is of importance for cardiovascular research and clinical assessment of ventricle functions.Patient-specific Echo-based left ventricle(LV)fluid-structure interaction(FSI)models were introduced to perform ventricle mechanical analysis,investigate flow behaviors,and evaluate the impact of myocardial infarction(MI)and hypertension on blood flow in the LV.Echo image data were acquired from 3 patients with consent obtained:one healthy volunteer(P1),one hypertension patient(P2),and one patient who had an inferior and posterior myocardial infarction(P3).The nonlinear Mooney-Rivlin model was used for ventricle tissue with material parameter values chosen to match echo-measure LV volume data.Using the healthy case as baseline,LV with MI had lower peak flow velocity(30%lower at beginejection)and hypertension LV had higher peak flow velocity(16%higher at begin-filling).The vortex area(defined as the area with vorticity>0)for P3 was 19%smaller than that of P1.The vortex area for P2 was 12%smaller than that of P1.At peak of filling,the maximum flow shear stress(FSS)for P2 and P3 were 390%higher and 63%lower than that of P1,respectively.Meanwhile,LV stress and strain of P2 were 41%and 15%higher than those of P1,respectively.LV stress and strain of P3 were 36%and 42%lower than those of P1,respectively.In conclusion,FSI models could provide both flow and structural stress/strain information which would serve as the base for further cardiovascular investigations related to disease initiation,progression,and treatment strategy selections.Large-scale studies are needed to validate our findings.展开更多
This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the...This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the scenario of cooperation in both network layer and terminal layer,an open queuing system model,which is aiming to depict the characteristics of packet loss rate of wireless communication networks,is proposed to optimize the traffic allocation results.The analysis and numerical simulations indicate that the proposed scheme achieves inter-networking load balance tominimize the whole transmission delay and expands the communication ability of single-mode terminals to support high data rate traffics.展开更多
Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,en...Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,endothelium-absent and non-ruptured but often-inflamed plaques based on histological findings.Even though there is efficient imaging technologies to detect the eroded plaque in vivo and tailored treatment strategy has also been developed for ACScaused by erosion in clinics,the pathogenesis mechanisms that cause plaque erosion are not fully understood.It is widely postulated that thrombus formation and endothelial apoptosis(the precursors of plaque erosion)have closed association with biomechanical conditions in the coronary vessel.Revealing of the mechanical conditions in the eroded plaque could advance our knowledge in understanding the formation of plaque erosion.To this end,patient-specific OCT-based fluid-structure interaction(FSI)models were developed to investigate the plaque biomechanical conditions and investigate the impact of erosioninduced inflammation on biomechanical conditions.In vivo OCTand Biplane X-ray angiographic data of eroded coronary plaque were acquired from one male patient(age:64). OCT images were segmented manually with external elastic membrane contour and the trailing edge of the lipid-rich necrotic core(lipid)assumed to have positive remodeling ratio 1.1.Locations with luminal surface having direct contact with intraluminal thrombus on OCT images were identified erosion sites.Fusion of OCT and biplane X-ray angiographic data were performed to obtain the 3D coronary geometry.OCT-based FSI models with pre-shrink-stretch process and anisotropic material properties were constructed following previously established procedures.To reflect tissue weakening caused by erosion-induced inflammation,the material stiffness of plaque intima at the erosion site was adjust to one tenth of un-eroded fibrous plaque tissue.Three FSI models were constructed to investigate the impacts of inflammation and lipid component on plaque biomechanics:M1,without erosion(this means plaque intima at the erosion sites were not softened)and without inclusion of lipid component;M2,with erosion but no lipid;M3,with erosion and inclusion of lipid.FSI models were solved by ADINA to obtain the biomechanical conditions at peak blood pressure including plaque wall stress/strain(PWS/PWSn)and flow wall shear stress(WSS).The average values of three biomechanical conditions at the erosion sites and at the fibrous cap overlaying lipid component were calculated from three models for analysis.The results of M1 and M2 were compared to investigate the impact of erosion-induced inflammation on plaque biomechanics.Mean PWS value decreases from 49.98 kPa to 18.83 kPa(62.32%decrease)while Mean PWSn value increases from 0.123 1 to 0.138 4(12%increase)as the material stiffness becomes 10times soft.Comparing M2 and M3 at the cap sites,M3(with inclusion of lipid)will elevates mean PWS and PWSn values by48.59%and 16.09%,respectively.The impacts of erosion and lipid on flow shear stress were limited(<2%).To conclude,erosion-induced inflammation would lead to lower stress distribution but larger strain distribution,while lipid would elevate both stress and strain conditions.This shows the influence of erosion and lipid component has impacts on stress/strain cal-culations which are closely related to plaque assessment.展开更多
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金funded by the National Natural Science Foundation of China(Grant No.52222708)。
文摘Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.
基金This work was supported by the National Key R&D Program of China No.2019YFB1802800.
文摘In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.
基金supported by the National Key Research and Development Plan under grant No. 2016YFB0502000
文摘Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The standard evolution and industry development are summarized.There are various positioning systems according to the scenarios,cost and accuracy.However,there is a basic positioning system framework including information extraction,measurement and calculation.In particular,the detailed positioning technologies mainly including cellular positioning and Local Area Network(LAN) positioning are listed and compared to provide a reference for practical applications.Finally,we summarize the challenges of indoor positioning and give a3-phase evolution route.
基金supported in part by program for National Natural Science Foundation of China under Grant No.61271184863 Program of China under Grant No.2013AA013301+1 种基金New Century Excellent Talents in University(NCET-11-0594)Open Fund of the State Key Laboratory of Integrated Services Networks(No.ISN12-03)
文摘To solve the contradiction between the increasing demand of diverse vehicular wireless applications and the shortage of spectrum resource, a novel cognitive cooperative vehicular ad-hoc network （CC- VANET） framework is proposed in this paper. Firstly, we develop an adaptive cognitive spectrum sensing （ACSS） mechanism which can help to trigger and adjust the spectrum sensing window according to network traffic load status and communication quality. And then, Generalized Nash Bargaining SoLution （GNBS）, which can achieve a good tradeoff between efficiency and weighted fairness, is proposed to formulate the asymmetric inter- cell resource allocation. Finally, GNBS- Safety （GNBS-S） scheme is developed to enhance the Quality of Service （QoS） of safety applications, especially in the heavy load status, where the bandwidth demanded and supplied cannot be matched well. Furthermore, the primary user activity （PUA） which can cause rate loss to secondary users, is also considered to alleviate its influence to fairness. Simulation results indicate that the proposed CC-VANET scheme can greatly improve the spectrum efficiency and reduce the transmission delay and packet loss rate on the heavy contention status. And GNBS spectrum allocation scheme outperforms both the Max-rain and Max-rate schemes, and canenhance the communication reliability of safety service considerably in CC-VANET.
基金supported by the National High-Tech R&D Program (863 Program) No. 2015AA01A705the National Natural Science Foundation of China under Grant No. 61572072+1 种基金the National Science and Technology Major Project No. 2015ZX03001041Fundamental Research Funds for the Central Universities "Research on the System of Personalized Education using Big Data"
文摘Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consists of two allocation stages, dominant resource fair(DRF) allocation stage and max-min fairness(MMF) allocation stage. The proposed DMMF scheme exhibits desirable properties, including share incentive, strategy proofness, envy freeness and Pareto optimality. Meanwhile, DMMF can improve the allocation efficiency and reach 100% allocation efficiency.
基金supported by the National Nature Science Foundation of China(NSFC 60622110,61471220,91538107,91638205)National Basic Research Project of China(973,2013CB329006),GY22016058
文摘With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.
文摘A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.
基金The support of the first and fourth authors is given by National Key R&D Program of China,2018YFB0905200.The support for the second and third authors is coming from BIRD171227/17 project of the University of Padova.
文摘DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.
基金supported by National Natural Science Foundation of China(Grant No.60633020, 90204012,60573035,60573036)
文摘With the rapid development of the WLAN,the 802. 11 s mesh network is emerging.Reliability,adaptability and scalability are the most important attributes of a mesh network.However,the security in an 802.11s mesh network is not well defined or specified,and there is no standard method to authenticate a mesh point that is creating a mesh link.In this paper,we propose solutions for the authentication of mesh points.For the Basic Infrastructure Security Mode,we combine the 802.1X/EAP and neighbor graph to realize the fast mutual authentication between a new mesh point and every its mesh link.We prove that our scheme maintains the security of the standardized EAP authentication algorithm.At the same time,the simulation result shows that the authentication latency of our scheme is much lower than that of the scheme in[2].In the Basic Decentralized Security Mode(BDSM),802.11s mesh networks are very similar to ad hoc networks,but they are different in several aspects.We first analyze the differences between them.Based on this analysis, we investigate the main authentication methods used in ad hoc networks and select the pairwise key pre-distribution model and identity-based model for the WLAN mesh network.
基金the Natural Science Foundation of Jiangsu Province,China(Grant No.20KJB470030).
文摘Synchronization is a process that describes the coherent dynamics of a large ensemble of interacting units.The study of explosive synchronization transition attracts considerable attention.Here,I report the explosive transition within the framework of a mobile network,while each oscillator is controlled by global-order parameters of the system.Using numerical simulation,I find that the explosive synchronization(ES)transition behavior can be controlled by simply adjusting the fraction of controlled oscillators.The influences of some parameters on explosive synchronization are studied.Moreover,due to the presence of the positive feedback mechanism,I prevent the occurrence of the synchronization of continuous-phase transition and make phase transition of the system a first-order phase transition accompanied by a hysteresis loop.
基金supported by National Key Research and Development Project 2018YFE0205503Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the computational concept of Decision-Making of cognition intelligence,its implementation framework adapting to foreseen innovations on networks and services,and its empirical evaluations are key techniques to guarantee the generationagnostic intelligence evolution of wireless communication networks.In this paper,we propose an Intelligent Decision Making(IDM)framework,acting as the role of network brain,based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network.Besides,usage scenarios and simulation demonstrate the generality and efficiency of IDM.We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.
文摘In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
基金supported by the National Science Fund for Excellent Youth Scholars of China(52222708)the National Natural Science Foundation of China(51977007)。
文摘The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.
基金Supported by the National Natural Science Foundation of China (90204012, 60573035, 60573036) and the University IT Research Center Project of Korea
文摘Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present an efficient constant-round group key exchange protocol, which is provably secure under the intractability of computation Diffie-Hellman problem. Our protocol is a contributory key exchange with perfect forward secrecy and has only two communication rounds. So it is more efficient than other protocols. Moreover, our protocol provides a method to design efficient constant-round group key exchange protocols and most secret sharing schemes could be adopted to construct our protocol.
基金supported by National Natural Science Foundation of China(No.61262082,No.61462066)Key Project of Chinese Ministry of Education(No.212025)+1 种基金Inner Mongolia Science Foundation for Distinguished Young Scholars(No.2012JQ03)Inner Mongolia Natural Science Foundation of Inner Mongolia(No.2012MS0922)
文摘As an innovative software application mode,Software as a service(SaaS) shows many attractive advantages.Migrating legacy system to SaaS can make outdated systems revived.In the process of migration,the existing valuable components need to be discovered and reused in order that the target system could be developed/integrated more efficiently.An innovative approach is proposed in this paper to extract the reusable components from legacy systems.Firstly,implementation models of legacy system are recovered through reverse engineering.Secondly,function models are derived by vertical clustering,and then logical components are discovered by horizontal clustering based on the function models.Finally,the reusable components with specific feature descriptions are extracted.Through experimental verification,the approach is considered to be efficient in reusable component discovery and to be helpful to migrating legacy system to SaaS.
文摘Understanding cardiac blood flow behaviors is of importance for cardiovascular research and clinical assessment of ventricle functions.Patient-specific Echo-based left ventricle(LV)fluid-structure interaction(FSI)models were introduced to perform ventricle mechanical analysis,investigate flow behaviors,and evaluate the impact of myocardial infarction(MI)and hypertension on blood flow in the LV.Echo image data were acquired from 3 patients with consent obtained:one healthy volunteer(P1),one hypertension patient(P2),and one patient who had an inferior and posterior myocardial infarction(P3).The nonlinear Mooney-Rivlin model was used for ventricle tissue with material parameter values chosen to match echo-measure LV volume data.Using the healthy case as baseline,LV with MI had lower peak flow velocity(30%lower at beginejection)and hypertension LV had higher peak flow velocity(16%higher at begin-filling).The vortex area(defined as the area with vorticity>0)for P3 was 19%smaller than that of P1.The vortex area for P2 was 12%smaller than that of P1.At peak of filling,the maximum flow shear stress(FSS)for P2 and P3 were 390%higher and 63%lower than that of P1,respectively.Meanwhile,LV stress and strain of P2 were 41%and 15%higher than those of P1,respectively.LV stress and strain of P3 were 36%and 42%lower than those of P1,respectively.In conclusion,FSI models could provide both flow and structural stress/strain information which would serve as the base for further cardiovascular investigations related to disease initiation,progression,and treatment strategy selections.Large-scale studies are needed to validate our findings.
基金supported by the National Natural Science Foundation of China under Grant No.60971125National Major Project under Grant No.2011ZX03003-003-01
文摘This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the scenario of cooperation in both network layer and terminal layer,an open queuing system model,which is aiming to depict the characteristics of packet loss rate of wireless communication networks,is proposed to optimize the traffic allocation results.The analysis and numerical simulations indicate that the proposed scheme achieves inter-networking load balance tominimize the whole transmission delay and expands the communication ability of single-mode terminals to support high data rate traffics.
基金supported in part by NSFC ( 11672001,11802060)Jiangsu NSF ( BK20180352)Jiangsu Province Science and Technology Agency ( BE2016785)
文摘Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,endothelium-absent and non-ruptured but often-inflamed plaques based on histological findings.Even though there is efficient imaging technologies to detect the eroded plaque in vivo and tailored treatment strategy has also been developed for ACScaused by erosion in clinics,the pathogenesis mechanisms that cause plaque erosion are not fully understood.It is widely postulated that thrombus formation and endothelial apoptosis(the precursors of plaque erosion)have closed association with biomechanical conditions in the coronary vessel.Revealing of the mechanical conditions in the eroded plaque could advance our knowledge in understanding the formation of plaque erosion.To this end,patient-specific OCT-based fluid-structure interaction(FSI)models were developed to investigate the plaque biomechanical conditions and investigate the impact of erosioninduced inflammation on biomechanical conditions.In vivo OCTand Biplane X-ray angiographic data of eroded coronary plaque were acquired from one male patient(age:64). OCT images were segmented manually with external elastic membrane contour and the trailing edge of the lipid-rich necrotic core(lipid)assumed to have positive remodeling ratio 1.1.Locations with luminal surface having direct contact with intraluminal thrombus on OCT images were identified erosion sites.Fusion of OCT and biplane X-ray angiographic data were performed to obtain the 3D coronary geometry.OCT-based FSI models with pre-shrink-stretch process and anisotropic material properties were constructed following previously established procedures.To reflect tissue weakening caused by erosion-induced inflammation,the material stiffness of plaque intima at the erosion site was adjust to one tenth of un-eroded fibrous plaque tissue.Three FSI models were constructed to investigate the impacts of inflammation and lipid component on plaque biomechanics:M1,without erosion(this means plaque intima at the erosion sites were not softened)and without inclusion of lipid component;M2,with erosion but no lipid;M3,with erosion and inclusion of lipid.FSI models were solved by ADINA to obtain the biomechanical conditions at peak blood pressure including plaque wall stress/strain(PWS/PWSn)and flow wall shear stress(WSS).The average values of three biomechanical conditions at the erosion sites and at the fibrous cap overlaying lipid component were calculated from three models for analysis.The results of M1 and M2 were compared to investigate the impact of erosion-induced inflammation on plaque biomechanics.Mean PWS value decreases from 49.98 kPa to 18.83 kPa(62.32%decrease)while Mean PWSn value increases from 0.123 1 to 0.138 4(12%increase)as the material stiffness becomes 10times soft.Comparing M2 and M3 at the cap sites,M3(with inclusion of lipid)will elevates mean PWS and PWSn values by48.59%and 16.09%,respectively.The impacts of erosion and lipid on flow shear stress were limited(<2%).To conclude,erosion-induced inflammation would lead to lower stress distribution but larger strain distribution,while lipid would elevate both stress and strain conditions.This shows the influence of erosion and lipid component has impacts on stress/strain cal-culations which are closely related to plaque assessment.
