A simple ballistic movement and two of its attributes (namely, reversal in time and synchronization with external events) are formulated. A three-dimensional, three-link musculoskeletal arm is subjected to a fast ball...A simple ballistic movement and two of its attributes (namely, reversal in time and synchronization with external events) are formulated. A three-dimensional, three-link musculoskeletal arm is subjected to a fast ballistic type movement. The central components of the movement from hippocampal, cerebellar, basal ganglia and reticular formation structures that may be involved in timing are identified. The role of agonist muscles and spinal reflexes in the execution of ballistic movements (namely, in fast starts and fast stops) is discussed. The needed three time intervals are constructed in real time and can be coordinated with external events. Delaying or advancing in time, synchronization, time scaling and inverting events in time relative to the movement is formulated. Digital computer simulations are presented to test the behavior of the formulated neural and spinal processing and demonstrate the behavior of the arm under such control.展开更多
Spacecraft collision avoidance procedures have become an essential part of satellite operations.Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can ...Spacecraft collision avoidance procedures have become an essential part of satellite operations.Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures.Such measures can be aided by the development of suitable machine learning(ML)models that predict,for example,the evolution of the collision risk over time.In October 2019,in an attempt to study this opportunity,the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages(CDMs),which was collected from 2015 to 2019.This dataset was used in the Spacecraft Collision Avoidance Challenge,which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects.This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.展开更多
文摘A simple ballistic movement and two of its attributes (namely, reversal in time and synchronization with external events) are formulated. A three-dimensional, three-link musculoskeletal arm is subjected to a fast ballistic type movement. The central components of the movement from hippocampal, cerebellar, basal ganglia and reticular formation structures that may be involved in timing are identified. The role of agonist muscles and spinal reflexes in the execution of ballistic movements (namely, in fast starts and fast stops) is discussed. The needed three time intervals are constructed in real time and can be coordinated with external events. Delaying or advancing in time, synchronization, time scaling and inverting events in time relative to the movement is formulated. Digital computer simulations are presented to test the behavior of the formulated neural and spinal processing and demonstrate the behavior of the arm under such control.
文摘Spacecraft collision avoidance procedures have become an essential part of satellite operations.Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures.Such measures can be aided by the development of suitable machine learning(ML)models that predict,for example,the evolution of the collision risk over time.In October 2019,in an attempt to study this opportunity,the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages(CDMs),which was collected from 2015 to 2019.This dataset was used in the Spacecraft Collision Avoidance Challenge,which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects.This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.
