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ISO 860 harmonization of concepts and terminology

Posted on March 15, 2021 by Kenneth Udut Leave a comment

ISO 860 harmonization of concepts and terminology

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« ISO 704. Terminology and standards for standards of terminology basically. What I’ve been calling ontologies (the computer science term, not the philosophy term), ISO 704 calls: “Concept System”. Example:

(In the end, or at the start really, it’s all stipulative -ken) »

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Looking at von Kármán, who worked in aerodynamics and, well… – boundary conditions (my interest) – large deflection of elastic plates – model of lattice dynamics for a crystal (used in QM) – also interest – potential flow – turbulence, combustion, boundary layers, flow, velocity… etc and I’m generally ok with the notion of binary 0/1 as a pragmatic solution for “effective range” (0 is an effective range, 1 is an effective range) – yet what about the / part? Vagueness then comes to mind and I remember this: “A range of application is a range of properties to whose instantiations the word can competently be applied. (E.g., a range of ‘tall’ is a set of heights, a range of ‘adult’ a set of ages.” The Multiple Range Theory of Vagueness Diana Raffman Chapter 4 gets to the heart of the theory: the reference of a vague word. The existence of multiple equally competent , arbitrarily different ways of applying a vague word — a feature of its use — is taken to be evidence of multiple ranges of application in its semantics. A range of application is a range of properties to whose instantiations the word can competently be applied. (E.g., a range of ‘tall’ is a set of heights, a range of ‘adult’ a set of ages.) The fundamental idea is that a vague word has multiple ranges of application relative to a given context, and it applies to (is true of) a different set of items relative to each range of application. Chapter 4 develops a multiple range semantics for vague terms, and also advances a definition of vagueness and resolution of the sorites paradox.
interesting to me is even-ness of the sampling. The invisible rolling wheel picking up continuous ink from the environment and depositing at once per full turn for sampling is a theoretical construct that nearly corresponds with reality but not quite entirely (noise and it’s never robust enough one time through) So, all kinds of tricks are used to improve the sampling process, resulting in greater and greater quality of product. and yet, there’s still no little tiny wheels spinning around. But we base an awful lot on them.
ah! Here it was. It’s been a lot of years. I hit the Dirac comb and I just stopped cold. Had to move on to other topics. https://en.wikipedia.org/wiki/Dirac_comb “The Dirac comb function allows one to represent both continuous and discrete phenomena, such as sampling and aliasing, in a single framework of continuous Fourier analysis on tempered distributions, without any reference to Fourier series. The Fourier transform of a Dirac comb is another Dirac comb. Owing to the Convolution Theorem on tempered distributions which turns out to be the Poisson summation formula, in signal processing, the Dirac comb allows modelling sampling by multiplication with it, but it also allows modelling periodization by convolution with it.”
In music, the behavior of waves when you play a chord (parallel) versus taking the same notes and playing them in an arpeggio (serial). The experience is both similar and different. Or, when you try to swap time and space / space and time with Fourier transforms or other decomposition methods. Still magical to me.
The connection and distinction between serial and parallel

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