In this paper,a reliability enhanced and density adaptive data disseminating scheme is proposed for Vehicular Ad hoc NETworks(VANETs).The distributed on demand inquiring and responding mechanism is employed to get nod...In this paper,a reliability enhanced and density adaptive data disseminating scheme is proposed for Vehicular Ad hoc NETworks(VANETs).The distributed on demand inquiring and responding mechanism is employed to get nodes' connectivity information.The announcing-listening process is also designed to find the nodes with bigger additional degree to rebroadcast,by which the relaying node is selected freely from density's influence.Simultaneously,a reliability parameter is designed to choose redundant relays for each hop.According to the importance of the broadcast,the parameter is set by the source node properly.Simulation results show that the scheme has achieved good performances such as low forwarding ratio,short latency and low load.The broadcast coverage ratio is ensured against the influence of key link errors and relaying nodes failure by paying suitable additional communication.展开更多
Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction an...Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.展开更多
The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computi...The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally ass...Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally assume the lognormal distributions of seismic demands and resistance parameters,uncertainties are inevitably induced in the seismic fragility analysis.In this paper,the Copula function and adaptive bandwidth kernel density estimation method(ABKDE)are used to establish a novel multidimensional seismic fragility analysis framework.Based on the results of incremental dynamic analysis for subway station structures,ABKDE is adopted to establish single-parameter seismic fragility curves for both the maximum inter-story drift ratio(MIDR)and cumulated dissipated hysteretic energy(CDHE),respectively.Subsequently,the Copula function is used to formulate a bivariate seismic fragility function considering the correlations among seismic demand measures and establish the corresponding fragility curves.Finally,comparative analyses are conducted to evaluate seismic fragility curves using Copula-based dual and single-parameter damage models as well as the traditional damage models.It is found that the seismic fragility analysis method using the Copula function has the ability to gain a comprehensive consideration of the MIDR and CDHE during the damage process of subway station structures.Moreover,this newly developed seismic fragility analysis framework can capture the influence of the correlation between deformation and energy under various peak ground accelerations on structural damage.Thus,this framework can provide a scientific basis for predicting structural damage in subway stations subjected to varying intensities of ground motion while considering multiple damage indicators.展开更多
Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, rou...Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.展开更多
In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)equations.It is well known that solutions of such equations are probability densit...In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)equations.It is well known that solutions of such equations are probability density functions,and thus our approach relies on modelling the target solutions with the temporal normalizing flows.The temporal normalizing flow is then trained based on the TFP loss function,without requiring any labeled data.Being a machine learning scheme,the proposed approach is mesh-free and can be easily applied to high dimensional problems.We present a variety of test problems to show the effectiveness of the learning approach.展开更多
基金Supported by the 863 Hi-Tech Research and Development Program of China(No.2008AA01A201)
文摘In this paper,a reliability enhanced and density adaptive data disseminating scheme is proposed for Vehicular Ad hoc NETworks(VANETs).The distributed on demand inquiring and responding mechanism is employed to get nodes' connectivity information.The announcing-listening process is also designed to find the nodes with bigger additional degree to rebroadcast,by which the relaying node is selected freely from density's influence.Simultaneously,a reliability parameter is designed to choose redundant relays for each hop.According to the importance of the broadcast,the parameter is set by the source node properly.Simulation results show that the scheme has achieved good performances such as low forwarding ratio,short latency and low load.The broadcast coverage ratio is ensured against the influence of key link errors and relaying nodes failure by paying suitable additional communication.
基金supported by the National Key Research and Development Plan(Grant No.2023YFB3712400)the National Key Research and Development Plan(Grant No.2020YFB1713600).
文摘Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.
基金supported by the National Natural Science Foundation of China under Grant Nos.52105136,51975028China Postdoctoral Science Foundation under Grant[No.2021M690290]the National Science and TechnologyMajor Project under Grant No.J2019-IV-0002-0069.
文摘The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
基金supported by the National Natural Science Foundation of China(Grant Nos.52178315,and 51578100)the Fundamental Research Funds for the Central Universities(Grant No.3132023504)+1 种基金the Dalian Science and Technology Innovation Fund(Grant No.2022JJ12GX031)the Project of Shenyang Key Laboratory of Safety Evaluation and Disaster Prevention of Engineering Structures(Grant No.S230184).
文摘Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally assume the lognormal distributions of seismic demands and resistance parameters,uncertainties are inevitably induced in the seismic fragility analysis.In this paper,the Copula function and adaptive bandwidth kernel density estimation method(ABKDE)are used to establish a novel multidimensional seismic fragility analysis framework.Based on the results of incremental dynamic analysis for subway station structures,ABKDE is adopted to establish single-parameter seismic fragility curves for both the maximum inter-story drift ratio(MIDR)and cumulated dissipated hysteretic energy(CDHE),respectively.Subsequently,the Copula function is used to formulate a bivariate seismic fragility function considering the correlations among seismic demand measures and establish the corresponding fragility curves.Finally,comparative analyses are conducted to evaluate seismic fragility curves using Copula-based dual and single-parameter damage models as well as the traditional damage models.It is found that the seismic fragility analysis method using the Copula function has the ability to gain a comprehensive consideration of the MIDR and CDHE during the damage process of subway station structures.Moreover,this newly developed seismic fragility analysis framework can capture the influence of the correlation between deformation and energy under various peak ground accelerations on structural damage.Thus,this framework can provide a scientific basis for predicting structural damage in subway stations subjected to varying intensities of ground motion while considering multiple damage indicators.
文摘Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.
基金supported by the NSF of China(under grant numbers 12288201 and 11731006)the National Key R&D Program of China(2020YFA0712000)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA25010404).
文摘In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)equations.It is well known that solutions of such equations are probability density functions,and thus our approach relies on modelling the target solutions with the temporal normalizing flows.The temporal normalizing flow is then trained based on the TFP loss function,without requiring any labeled data.Being a machine learning scheme,the proposed approach is mesh-free and can be easily applied to high dimensional problems.We present a variety of test problems to show the effectiveness of the learning approach.
