DRIFT , FLOAT, GLIDE
and more electronic controll systems
By Henryk Szubinski
year was 1995
basically known as a Hybrid example of the ALIEN look which made its debut without the automation in full format…..
The designer of the computer systems was a friend who let me try it out in the concept fase of its electronic circuitry…
A very smart friend…
the actual UNIT used on the add on mechanism which will fit over the general steering parameter and its basic wireing to the rest of mechanical steering is usually seen with the types of circuit runs of a vechicle by steering it with a remote controll to define the values of drive without a driver…
.this one had a basic disc controll system that connected to the radio player as a clipp on ..
HOW CAN THIS BE ADAPTED IN THE FUTURE OF FLYING CARS
had a friend who owned a 323 Mazda
the details were apparent only when trying to steer it..the extra drive controll system
as well as no learning of driving required…electronic steering takes over the problems of a automatic computer controlled type of controll of the driving environements…
This vechicle was defined as YUPPIE and cost about 30 000 Australian dollars at the time….Its basic projectability made it a advancement of its times…
The name was
computer systems like this one are defined as continuous / in continuous
as a type artificial Intelligence of steering
The failure of a function to be continuous
at a point is quantified by its oscillation.
the only definition of the continuity by oincontinuous means = occilations between the basics of the proto controll and the values to which the compressed A.I computator will work as the general basis for the responses on angles of inclinations as seen in projectives of A.I controll
. In mathematics, a continuous function is a function for which, intuitively, small changes in the input result in small changes in the output. Otherwise, a function is said to be “discontinuous”. A continuous function with a continuous inverse function is called “bicontinuous“. An intuitive (though imprecise) idea of continuity is given by the common statement that a continuous function is a function whose graph can be drawn without lifting the chalk from the blackboard.
Continuity of functions is one of the core concepts of topology, which is treated in full generality below. The introductory portion of this article focuses on the special case where the inputs and outputs of functions are real numbers. In addition, this article discusses the definition for the more general case of functions between twometric spaces. In order theory, especially in domain theory, one considers a notion of continuity known as Scott continuity. Other forms of continuity do exist but they are not discussed in this article.
As an example, consider the function h(t) which describes the height of a growing flower at time t. This function is continuous. In fact, there is a dictum of classical physics which states that in nature everything is continuous. By contrast, if M(t) denotes the amount of money in a bank account at time t, then the function jumps whenever money is deposited or withdrawn, so the function M(t) is discontinuous. (However, if one assumes a discrete set as the domain of function M, for instance the set of points of time at 4:00 PM on business days, then M becomes continuous function, as every function whose domain is a discrete subset of reals is.)
Continuity can also be defined in terms of oscillation: a function ƒ is continuous at a point x0 if and only if the oscillation is zero; in symbols, ωf(x0) = 0.A benefit of this definition is that it quantifies discontinuity: the oscillation gives how much the function is discontinuous at a point.
This definition is useful in descriptive set theory to study the set of discontinuities and continuous points – the continuous points are the intersection of the sets where the oscillation is less than ε (hence a Gδ set) – and gives a very quick proof of one direction of the Lebesgue integrability condition.
The oscillation is equivalence to the ε–δ definition by a simple re-arrangement, and by using a limit (lim sup, lim inf) to define oscillation: if (at a given point) for a given ε0 there is no δ that satisfies the ε–δ definition, then the oscillation is at least ε0, and conversely if for every ε there is a desired δ, the oscillation is 0. The oscillation definition can be naturally generalized to maps from a topological space to a metric space.