Dense-array ambient noise tomography is a powerful tool for achieving high-resolution subsurface imag-ing,significantly impacting geohazard prevention and control.Conventional dense-array studies,how-ever,require simu...Dense-array ambient noise tomography is a powerful tool for achieving high-resolution subsurface imag-ing,significantly impacting geohazard prevention and control.Conventional dense-array studies,how-ever,require simultaneous observations of numerous stations for extensive coverage.To conduct a comprehensive karst feature investigation with limited stations,we designed a new synchronous-asyn-chronous observation system that facilitates dense array observations.We conducted two rounds of asynchronous observations,each lasting approximately 24 h,in combination with synchronous backbone stations.We achieved wide-ranging coverage of the study area utilizing 197 nodal receivers,with an average station spacing of 7 m.The beamforming results revealed distinct variations in the noise source distributions between day and night.We estimated the source strength in the stationary phase zone and used a weighting scheme for stacking the cross-correlation functions(C ^(1) functions)to suppress the influ-ence of nonuniform noise source distributions.The weights were derived from the similarity coefficients between multicomponent C^(1)functions related to Rayleigh waves.We employed the cross-correlation of C ^(1) functions(C^(2)methods)to obtain the empirical Green’s functions between asynchronous stations.To eliminate artifacts in C ^(2) functions from higher-mode surface waves in C^(1)functions,we filtered the C^(1)functions on the basis of different particle motions linked to multimode Rayleigh waves.The dispersion measurements of Rayleigh waves obtained from both the C^(1)and C^(2)functions were utilized in surface wave tomography.The inverted three-dimensional(3D)shear-wave(S-wave)velocity model reveals two significant low-velocity zones at depths ranging from 40 to 60 m,which align well with the karst caves found in the drilling data.The method of short-term synchronous-asynchronous ambient noise tomography shows promise as a cost-effective and efficient approach for urban geohazard investigations.展开更多
Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centraliz...Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.展开更多
Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the dela...Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.展开更多
To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic ...To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.展开更多
Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned a...Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.展开更多
Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of ...Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of time and convergence speed,mak-ing it unsuitable for high real-time requirements.To address these issues,this paper proposes an Adap-tive Waiting time Asynchronous Federated Learn-ing(AWTAFL)based on Dueling Double Deep Q-Network(D3QN).The server dynamically adjusts the waiting time using the D3QN algorithm based on the current task progress and energy consumption,aim-ing to accelerate convergence and save energy.Addi-tionally,this paper presents a new federated learning global aggregation scheme,where the central server performs weighted aggregation based on the freshness and contribution of client parameters.Experimen-tal simulations demonstrate that the proposed algo-rithm significantly reduces the convergence time while ensuring model quality and effectively reducing en-ergy consumption in asynchronous federated learning.Furthermore,the improved global aggregation update method enhances training stability and reduces oscil-lations in the global model convergence.展开更多
This research focuses on detecting faults in flight vehicles with unstable subsystems operating asynchronously.By accounting for asynchronous switching,a switched model is established,and filters for fault detection(F...This research focuses on detecting faults in flight vehicles with unstable subsystems operating asynchronously.By accounting for asynchronous switching,a switched model is established,and filters for fault detection(FD)in unstable subsystems are developed.The FD challenge is then transformed into an H∞filtering issue.Utilizing the multiple discontinuous Lyapunov function(MDLF)approach and the mode-dependent average dwell time(MDADT)method,sufficient conditions are derived to ensure stability during both fast and slow switching.Furthermore,the existence and solutions for FD filters are provided through linear matrix inequalities(LMIs).The simulation outcomes demonstrated the excellent performance of the developed method in studied cases.展开更多
A novel asynchronous ACS(add-compare-select) processor for Viterbi decoder is described.It is controlled by local handshake signals instead of the globe clock.The circuits of asynchronous adder unit,asynchronous compa...A novel asynchronous ACS(add-compare-select) processor for Viterbi decoder is described.It is controlled by local handshake signals instead of the globe clock.The circuits of asynchronous adder unit,asynchronous comparator unit,and asynchronous selector unit are proposed.A full-custom design of asynchronous 4-bit ACS processor is fabricated in CSMC-HJ 0.6μm CMOS 2P2M mixed-mode process.At a supply voltage of 5V,when it operates at 20MHz,the power consumption is 75.5mW.The processor has no dynamic power consumption when it awaits an opportunity in sleep mode.The results of performance test of asynchronous 4-bit ACS processor show that the average case response time 19.18ns is only 82% of the worst-case response time 23.37ns.Compared with the synchronous 4-bit ACS processor in power consumption and performance by simulation,it reveals that the asynchronous ACS processor has some advantages than the synchronous one.展开更多
基金supported by the National Natural Science Foundation of China(41830103)the Project of Nanjing Center of China Geological Survey(DD20190281).
文摘Dense-array ambient noise tomography is a powerful tool for achieving high-resolution subsurface imag-ing,significantly impacting geohazard prevention and control.Conventional dense-array studies,how-ever,require simultaneous observations of numerous stations for extensive coverage.To conduct a comprehensive karst feature investigation with limited stations,we designed a new synchronous-asyn-chronous observation system that facilitates dense array observations.We conducted two rounds of asynchronous observations,each lasting approximately 24 h,in combination with synchronous backbone stations.We achieved wide-ranging coverage of the study area utilizing 197 nodal receivers,with an average station spacing of 7 m.The beamforming results revealed distinct variations in the noise source distributions between day and night.We estimated the source strength in the stationary phase zone and used a weighting scheme for stacking the cross-correlation functions(C ^(1) functions)to suppress the influ-ence of nonuniform noise source distributions.The weights were derived from the similarity coefficients between multicomponent C^(1)functions related to Rayleigh waves.We employed the cross-correlation of C ^(1) functions(C^(2)methods)to obtain the empirical Green’s functions between asynchronous stations.To eliminate artifacts in C ^(2) functions from higher-mode surface waves in C^(1)functions,we filtered the C^(1)functions on the basis of different particle motions linked to multimode Rayleigh waves.The dispersion measurements of Rayleigh waves obtained from both the C^(1)and C^(2)functions were utilized in surface wave tomography.The inverted three-dimensional(3D)shear-wave(S-wave)velocity model reveals two significant low-velocity zones at depths ranging from 40 to 60 m,which align well with the karst caves found in the drilling data.The method of short-term synchronous-asynchronous ambient noise tomography shows promise as a cost-effective and efficient approach for urban geohazard investigations.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFB3101100in part by the National Natural Science Foundation of China under Grant 62272123,62272102,62272124+2 种基金in part by the Project of High-level Innovative Talents of Guizhou Province under Grant[2020]6008in part by the Science and Technology Program of Guizhou Province under Grant No.[2020]5017,No.[2022]065in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105。
文摘Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
基金supported by National Key Research and Development Program of China(2022YFB3305900)National Natural Science Foundation of China(62394343,62394345)+1 种基金Major Science and Technology Projects of Longmen Laboratory(NO.LMZDXM202206)Shanghai Rising-Star Program under Grant 24QA2706100.
文摘Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.
文摘To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.
基金supported by the Hunan Provin〓〓cial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the National Natural Science Foundation of China(Grant No.12372189)。
文摘Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.
基金supported by the National Natural Science Foundation of China(62371082)Guangxi Science and Technology Project(AB24010317)+1 种基金Science and Technology Project of Chongqing Education Commission(KJZD-K202400606)Natural Science Foundation of Chongqing(CSTB2023NSCQ-MSX0726,CSTB2023NSCQ-LZX0014).
文摘Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of time and convergence speed,mak-ing it unsuitable for high real-time requirements.To address these issues,this paper proposes an Adap-tive Waiting time Asynchronous Federated Learn-ing(AWTAFL)based on Dueling Double Deep Q-Network(D3QN).The server dynamically adjusts the waiting time using the D3QN algorithm based on the current task progress and energy consumption,aim-ing to accelerate convergence and save energy.Addi-tionally,this paper presents a new federated learning global aggregation scheme,where the central server performs weighted aggregation based on the freshness and contribution of client parameters.Experimen-tal simulations demonstrate that the proposed algo-rithm significantly reduces the convergence time while ensuring model quality and effectively reducing en-ergy consumption in asynchronous federated learning.Furthermore,the improved global aggregation update method enhances training stability and reduces oscil-lations in the global model convergence.
基金the National Natural Science Foundation of China(Grant Nos.62303380,62176214,62101590,62003268)the Aeronautical Science Foundation of China(Grant No.201907053001).
文摘This research focuses on detecting faults in flight vehicles with unstable subsystems operating asynchronously.By accounting for asynchronous switching,a switched model is established,and filters for fault detection(FD)in unstable subsystems are developed.The FD challenge is then transformed into an H∞filtering issue.Utilizing the multiple discontinuous Lyapunov function(MDLF)approach and the mode-dependent average dwell time(MDADT)method,sufficient conditions are derived to ensure stability during both fast and slow switching.Furthermore,the existence and solutions for FD filters are provided through linear matrix inequalities(LMIs).The simulation outcomes demonstrated the excellent performance of the developed method in studied cases.
文摘A novel asynchronous ACS(add-compare-select) processor for Viterbi decoder is described.It is controlled by local handshake signals instead of the globe clock.The circuits of asynchronous adder unit,asynchronous comparator unit,and asynchronous selector unit are proposed.A full-custom design of asynchronous 4-bit ACS processor is fabricated in CSMC-HJ 0.6μm CMOS 2P2M mixed-mode process.At a supply voltage of 5V,when it operates at 20MHz,the power consumption is 75.5mW.The processor has no dynamic power consumption when it awaits an opportunity in sleep mode.The results of performance test of asynchronous 4-bit ACS processor show that the average case response time 19.18ns is only 82% of the worst-case response time 23.37ns.Compared with the synchronous 4-bit ACS processor in power consumption and performance by simulation,it reveals that the asynchronous ACS processor has some advantages than the synchronous one.
