In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rat...In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem,we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.展开更多
Accurate dynamic modeling of racing drones,characterized by high speed and maneuverability,is challenging due to model uncertainty stemming from personalized modifications and frequent in-flight collisions.Although de...Accurate dynamic modeling of racing drones,characterized by high speed and maneuverability,is challenging due to model uncertainty stemming from personalized modifications and frequent in-flight collisions.Although deep neural networkbased methods have shown some effectiveness,they struggle with online adaptability as the system and environment change,and they present difficulties in analysis.To address these challenges,we propose a novel semi-adaptive spectrally normalized neural network(SASNNet)to characterize model uncertainty.SASNNet learns long-term features representing inherent operational dynamics through offline training,while online training enables it to capture short-term features reflecting system changes,enhancing its adaptability.Additionally,spectral normalization is integrated into the training process to improve SASNNet's Lipschitz stability,and an adaptive parameter update rule is designed to accelerate the model response.Building on this uncertainty characterization approach,we develop a control compensation method for trajectory tracking in racing drones.We validate the proposed method through physics-engine-based simulations,with results demonstrating high modeling accuracy,enhanced adaptability,and fast response speed.展开更多
Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue,so the trained model cannot well generalize to the unseen data without calibrating the logs....Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue,so the trained model cannot well generalize to the unseen data without calibrating the logs.In this paper,we formulated the geophysical logs calibration problem and give its statistical explanation,and then exhibited an interpretable machine learning method,i.e.,Unilateral Alignment,which could align the logs from one well to another without losing the physical meanings.The involved UA method is an unsupervised feature domain adaptation method,so it does not rely on any labels from cores.The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.展开更多
基金supported in part by the National High Technology Research and Development Program of China(863 Program)(2014AA06A503)the National Natural Science Foundation of China(61422307,61673350,61673361)+1 种基金the Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of Chinathe Youth Top-notch Talent Support Program and the 1000-talent Youth Program and the Youth Yangtze River Scholarship
文摘In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem,we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.
基金supported by the National Key R&D Program of China(Grant Nos.2022YFB3206900,2022YFB3206904)the Anhui Postdoctoral Scientific Research Program Foundation(Grant No.2024A777)+1 种基金the National Natural Science Foundation of China(Grant No.62203420)the China Postdoctoral Science Foundation(Grant No.2024M753148)。
文摘Accurate dynamic modeling of racing drones,characterized by high speed and maneuverability,is challenging due to model uncertainty stemming from personalized modifications and frequent in-flight collisions.Although deep neural networkbased methods have shown some effectiveness,they struggle with online adaptability as the system and environment change,and they present difficulties in analysis.To address these challenges,we propose a novel semi-adaptive spectrally normalized neural network(SASNNet)to characterize model uncertainty.SASNNet learns long-term features representing inherent operational dynamics through offline training,while online training enables it to capture short-term features reflecting system changes,enhancing its adaptability.Additionally,spectral normalization is integrated into the training process to improve SASNNet's Lipschitz stability,and an adaptive parameter update rule is designed to accelerate the model response.Building on this uncertainty characterization approach,we develop a control compensation method for trajectory tracking in racing drones.We validate the proposed method through physics-engine-based simulations,with results demonstrating high modeling accuracy,enhanced adaptability,and fast response speed.
基金Supported in part by the National Natural Science Foundation of China under Grant 61903353in part by the SINOPEC Programmes for Science and Technology Development under Grant PE19008-8.
文摘Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue,so the trained model cannot well generalize to the unseen data without calibrating the logs.In this paper,we formulated the geophysical logs calibration problem and give its statistical explanation,and then exhibited an interpretable machine learning method,i.e.,Unilateral Alignment,which could align the logs from one well to another without losing the physical meanings.The involved UA method is an unsupervised feature domain adaptation method,so it does not rely on any labels from cores.The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.
