Object Motion Measure
An Object Motion Measure is a physical measure of an apparent change in spatial dimension of a physical object with temporal dimension.
- AKA: Time-Varying Position.
- Example(s):
- Linear Motion.
- Circular Motion.
- Periodic Motion, such as harmonic motion.
- Random Motion, such as Brownian motion.
- …
- Counter-Example(s):
- See: Trajectory, Velocity, Acceleration, Time, Physical Laws, Newton's Laws of Motion, Equation of Motion.
References
2015
- (Wikipedia, 2015) ⇒ http://www.wikiwand.com/en/Motion_(physics)
- QUOTE: In physics, motion is a change in position of an object with respect to time. Motion is typically described in terms of displacement, distance (scalar), velocity, acceleration, time and speed. Motion of a body is observed by attaching a frame of reference to an observer and measuring the change in position of the body relative to that frame.
If the position of a body is not changing with respect to a given frame of reference the body is said to be at rest, motionless, immobile, stationary, or to have constant (time-invariant) position. An object's motion cannot change unless it is acted upon by a force, as described by Newton's first law. Momentum is a quantity which is used for measuring motion of an object. An object's momentum is directly related to the object's mass and velocity, and the total momentum of all objects in an isolated system (one not affected by external forces) does not change with time, as described by the law of conservation of momentum.
- QUOTE: In physics, motion is a change in position of an object with respect to time. Motion is typically described in terms of displacement, distance (scalar), velocity, acceleration, time and speed. Motion of a body is observed by attaching a frame of reference to an observer and measuring the change in position of the body relative to that frame.
2013
- (Feynman et al., 2013) ⇒ Feynman, Richard P., Robert B. Leighton, and Matthew Sands. The Feynman Lectures on Physics, Desktop Edition Volume I. Vol. 1. Basic books, 2013. ⇒ http://www.feynmanlectures.caltech.edu/I_08.html
2009
- (Monreale et al., 2009) ⇒ Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. (2009). “WhereNext: A Location Predictor on Trajectory Pattern Mining.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557091
- QUOTE: The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data. This side effect provides the opportunity for innovative methods that analyze the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process.