GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a lar...The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in s...Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global scale.The emerging mega-constellations enable new opportunities for realizing deterministic delay globally.As one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of hops.Moreover,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by routing.This paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of DETSPACE.The effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.展开更多
Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact A...Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact ACL injury prevention in sports,it is necessary to elucidate how the ground reaction force(GRF)acting point(center of pressure(CoP))in the stance foot produces combined knee VL+IR moments in risky maneuvers,such as cuttings.However,the effects of the GRF acting point on the development of the combined knee VL+IR moment in cutting are still unknown.Methods:We first established the deterministic mechanical condition that the CoP position relative to the tibial rotational axis differentiates the GRF vector’s directional probability for developing the combined knee VL+IR moment,and theoretically predicted that when the CoP is posterior to the tibial rotational axis,the GRF vector is more likely to produce the combined knee VL+IR moment than when the CoP is anterior to the tibial rotational axis.Then,we tested a stochastic aspect of our theory in a lab-controlled in vivo experiment.Fourteen females performed 60˚cutting under forefoot/rearfoot strike conditions(10 trials each).The positions of lower limb markers and GRF data were measured,and the knee moment due to GRF vector was calculated.The trials were divided into anterior-and posterior-CoP groups depending on the CoP position relative to the tibial rotational axis at each 10 ms interval from 0 to 100 ms after foot strike,and the occurrence rate of the combined knee VL+IR moment was compared between trial groups.Results:The posterior-CoP group showed significantly higher occurrence rates of the combined knee VL+IR moment(maximum of 82.8%)at every time point than those of the anterior-CoP trials,as theoretically predicted by the deterministic mechanical condition.Conclusion:The rearfoot strikes inducing the posterior CoP should be avoided to reduce the risk of non-contact ACL injury associated with the combined knee VL+IR stress.展开更多
Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonom...Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.展开更多
Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most...Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172318 and 42377186)the National Key R&D Program of China(Grant No.2023YFC3007201).
文摘The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金This work is supported by National Key Research and Development Plan of China(2022YFB3105204).
文摘Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global scale.The emerging mega-constellations enable new opportunities for realizing deterministic delay globally.As one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of hops.Moreover,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by routing.This paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of DETSPACE.The effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.
基金supported by the Grant-in-Aid for Young Scientists(B)Project(Grant No.24700716)funded by the Ministry of Education,Culture,Sports,Science and Technology,Japan.
文摘Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact ACL injury prevention in sports,it is necessary to elucidate how the ground reaction force(GRF)acting point(center of pressure(CoP))in the stance foot produces combined knee VL+IR moments in risky maneuvers,such as cuttings.However,the effects of the GRF acting point on the development of the combined knee VL+IR moment in cutting are still unknown.Methods:We first established the deterministic mechanical condition that the CoP position relative to the tibial rotational axis differentiates the GRF vector’s directional probability for developing the combined knee VL+IR moment,and theoretically predicted that when the CoP is posterior to the tibial rotational axis,the GRF vector is more likely to produce the combined knee VL+IR moment than when the CoP is anterior to the tibial rotational axis.Then,we tested a stochastic aspect of our theory in a lab-controlled in vivo experiment.Fourteen females performed 60˚cutting under forefoot/rearfoot strike conditions(10 trials each).The positions of lower limb markers and GRF data were measured,and the knee moment due to GRF vector was calculated.The trials were divided into anterior-and posterior-CoP groups depending on the CoP position relative to the tibial rotational axis at each 10 ms interval from 0 to 100 ms after foot strike,and the occurrence rate of the combined knee VL+IR moment was compared between trial groups.Results:The posterior-CoP group showed significantly higher occurrence rates of the combined knee VL+IR moment(maximum of 82.8%)at every time point than those of the anterior-CoP trials,as theoretically predicted by the deterministic mechanical condition.Conclusion:The rearfoot strikes inducing the posterior CoP should be avoided to reduce the risk of non-contact ACL injury associated with the combined knee VL+IR stress.
基金supported in part by the projects of the National Natural Science Foundation of China(62376059,41971340)Fujian Provincial Department of Science and Technology(2023XQ008,2023I0024,2021Y4019),Fujian Provincial Department of Finance(GY-Z230007,GYZ23012)Fujian Key Laboratory of Automotive Electronics and Electric Drive(KF-19-22001).
文摘Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.
文摘Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.
