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The first was the thermal vacuum, a chamber in which the temperature can be rapidly phased from 150º down to -150º. He put together a gauntlet of tests, all designed to approximate some calamity the pieces might encounter in space. So Ragan bought a Rolex, a Longines, and an Omega, and set about finding which ones were up to NASA’s standards.
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Ragan was tasked with finding one that could survive the trip. There would be a digital clock in the cabin of the Apollo Lunar Module, but the astronauts were all military men, accustomed to reading and working with mechanical watches. Kennedy had announced his mission of going to the moon before the end of the decade, and the newly-minted class of astronauts had requested wristwatches. When NASA engineer Jim Ragan rescued the Rolex from the thermal vacuum, he discovered the second hand had twisted closely against the minute hand like a vine climbing a tree. And what watch weaves a better yarn than one that went to the freaking moon? The watch market, both at retail and in the secondary market, is flourishing-in no small part thanks to customers obsession with things like honesty and storytelling and provenance. The one on my wrist is standard-issue stainless steel, but the back is engraved with the same phrase that's appeared on countless Speedmasters produced since 1969: “The First Watch Worn on the Moon.” Omega and its Speedmaster are proof that a watch brand-because collectors lap up mythical stories, red-white-and-blue-tinged nostalgia, and proven astronaut-grade quality-can be built largely on a single seven-word phrase. This year, Omega is selling 1,014 of those “Moonshine Gold” editions, the same amount sold 50 years ago. With our method it can give guidelines on appropriate fuzzy membership functions selection for improving classification accuracy.Clooney’s is gold, modeled after the ones awarded during a victory-lap Houston dinner the Apollo 11 crew attended with the president to celebrate the successful moon mission in 1969. Experiments on different datasets show that different membership functions do result in different classification performances when threshold has been set in advance. For different fuzzy membership functions, we propose the method to select the appropriate one based on user-demand in our new fuzzy rough set model. With different user demands, varied thresholds in DTRS are firstly set to decide with probability an object can be classified to the positive region. Thus we can derive the new decision rules. calculating method containing in the expected losses expression of the DTRS model.
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We combine fuzzy sets with decision-theoretic rough set (DTRS) theory and propose a new equation by employing the fuzzy membership functions to change the posterior probability. The decision rough set model (DTRS) is the kind of probabilistic rough set model with proper cost functions. The main contribution of the paper is to clarify the difference between fuzzy sets and rough sets, and unify several independent works which deal with similar ideas in different settings or notations.įuzzy rough sets are generalization of rough sets to handle fuzziness and uncertainty existed in data. Instead of using a similarity relation, we can start with fuzzy granules which make a fuzzy partition of the reference scale. Another idea is to turn the equivalence relation into a fuzzy similarity relation, for the modeling of coarseness, as already proposed by Farinas del Cerro and Prade. Shafer's concept of coarsened belief functions also belongs to the same line of thought. We then come close to Caianiello's C-calculus. First, one may think of deriving the upper and lower approximations of a fuzzy set, when a reference scale is coarsened by means of an equivalence relation. Seen this way, it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems. In this paper we argue that both notions aim to different purposes.
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The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set, sometimes with a view to prove that one is more general, or, more useful than the other.