Abstract: Machine visualization is an area of interest with fast
and progressive development. We present a method of machine
visualization which will be applicable in real industrial conditions
according to current needs and demands. Real factory data were
obtained in a newly built research plant. Methods described in this
paper were validated on a case study. Input data were processed and
the virtual environment was created. The environment contains
information about dimensions, structure, disposition, and function.
Hardware was enhanced by modular machines, prototypes, and
accessories. We added functionalities and machines into the virtual
environment. The user is able to interact with objects such as testing
and cutting machines, he/she can operate and move them. Proposed
design consists of an environment with two degrees of freedom of
movement. Users are in touch with items in the virtual world which
are embedded into the real surroundings. This paper describes development of the virtual environment. We
compared and tested various options of factory layout virtualization
and visualization. We analyzed possibilities of using a 3D scanner in
the layout obtaining process and we also analyzed various virtual
reality hardware visualization methods such as: Stereoscopic (CAVE)
projection, Head Mounted Display (HMD) and augmented reality
(AR) projection provided by see-through glasses.
Abstract: Measures of complexity and entropy have not converged to a single quantitative description of levels of organization of complex systems. The need for such a measure is increasingly necessary in all disciplines studying complex systems. To address this problem, starting from the most fundamental principle in Physics, here a new measure for quantity of organization and rate of self-organization in complex systems based on the principle of least (stationary) action is applied to a model system - the central processing unit (CPU) of computers. The quantity of organization for several generations of CPUs shows a double exponential rate of change of organization with time. The exact functional dependence has a fine, S-shaped structure, revealing some of the mechanisms of self-organization. The principle of least action helps to explain the mechanism of increase of organization through quantity accumulation and constraint and curvature minimization with an attractor, the least average sum of actions of all elements and for all motions. This approach can help describe, quantify, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self organization to improve their quality. It can be applied to other complex systems from Physics, Chemistry, Biology, Ecology, Economics, Cities, network theory and others where complex systems are present.