Observation-driven integer-valued autoregressive models are widely used for modeling count time series exhibiting dynamic dependence,yet their performance critically depends on the way that thinning probabilities are ...Observation-driven integer-valued autoregressive models are widely used for modeling count time series exhibiting dynamic dependence,yet their performance critically depends on the way that thinning probabilities are linked to past observations.Most existing specifications rely on the logit link and may respond excessively to large counts.In this paper,we introduce a class of new observation-driven integer-valued autoregressive models using logarithmic and soft-clipping links that attenuate the influence of large observations.The proposed framework allows for stochastic covariates.Estimation is carried out using conditional maximum likelihood and conditional least squares methods.Simulation studies and two real data applications are used to illustrate the proposed models.展开更多
An observation-driven method for coordinated standoff target tracking based on Model Predictive Control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering o...An observation-driven method for coordinated standoff target tracking based on Model Predictive Control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering over a target.After acquiring a fusion estimate of the target state,each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix(FIM)determinant in the decentralized architecture.To facilitate observation optimization,only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff tracking.Additionally,a modified iterative scheme is introduced to improve the iterative efficiency,and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its target.Sufficient experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.展开更多
Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a sum...Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.展开更多
基金supported by the Scientific Research Project Funding from the Education Department of Jilin Province(Grant No.JJKH20261608KJ)by the National Natural Science Foundation of China(Grant No.12271206).
文摘Observation-driven integer-valued autoregressive models are widely used for modeling count time series exhibiting dynamic dependence,yet their performance critically depends on the way that thinning probabilities are linked to past observations.Most existing specifications rely on the logit link and may respond excessively to large counts.In this paper,we introduce a class of new observation-driven integer-valued autoregressive models using logarithmic and soft-clipping links that attenuate the influence of large observations.The proposed framework allows for stochastic covariates.Estimation is carried out using conditional maximum likelihood and conditional least squares methods.Simulation studies and two real data applications are used to illustrate the proposed models.
基金supported in part by the National Natural Science Foundation of China(Nos.62022092 and 61790550).
文摘An observation-driven method for coordinated standoff target tracking based on Model Predictive Control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering over a target.After acquiring a fusion estimate of the target state,each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix(FIM)determinant in the decentralized architecture.To facilitate observation optimization,only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff tracking.Additionally,a modified iterative scheme is introduced to improve the iterative efficiency,and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its target.Sufficient experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.
文摘Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.
