The neuromechanics of human motion are generally represented in the literature by feedforward control mechanisms: the brain sends a control signal to a part of the body to move, and motion ensues. Thus neuromechanical commands for motion are influenced by control signals from neurocognitive inputs. However, feedback also exists from the neuromechanical system to the neurocognitive one, so that subsequent decisions related to motor commands are influenced by the motion itself. Recent work suggests that accounting for bidirectional feedback, both from neurocognitive to neuromechanical systems and from neuromechanical to neurocognitive ones, allows for more robust accounts of behavior. In this paper, we describe a neurocognitive model, a neuromechanical model, and a simple bidirectional feedback mechanism to couple the two systems. We then show that the behavior of the combined system is determined by the interaction of coupling strength and properties of the mechanical and cognitive models.

Accurate failure prediction in conjunction with efficient process migration facilities including some Cloud constructs can enable failure avoidance in large-scale high performance computing (HPC) platforms. In this work we demonstrate a prototype system that incorporates our probabilistic failure prediction system with virtualization mechanisms and techniques to provide a whole system approach to failure avoidance. This work utilizes a failure scenario based on a real-world HPC case study.

This paper applies a task-level approach to the control of holonomically constrained shoulder models. These models include a biomechanical representation based on human physiology and a robotic design based on a parallel-serial structure. Both models involve complex kinematically coupled motion between the shoulder girdle and the humerus. This coupled motion has a significant impact on the resulting humeral pointing dynamics associated with arm movement. The constrained task-level control approach implemented here characterizes and exploits the kinematically coupled nature of these systems by casting the constrained dynamics into a task-level control framework. Examples are presented which illustrate the effectiveness of this approach

This paper presents an operational space control approach for the general class of holonomically constrained multibody systems. As a point of departure, the general formulation of constrained dynamical systems is addressed using multiplier and minimization approaches. The constrained dynamics problem is interpreted with respect to its underlying symmetry with task space dynamics. A framework for constrained operational space control is then presented which casts the general formulation of constrained multibody systems into a task space setting. This provides a means of exploiting natural task-level control structures within the constrained environment. A set of examples illustrate this control implementation.