FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2029386311> ?p ?o ?g. }

• W2029386311 abstract "Abstract Most signals reaching the mammalian brain are noisy, weak, and degraded so that the corresponding data that are carried by the signals are themselves incomplete and overlapping, and, more likely that not, the product of convolution with nonlinear sources. The attempt to deconvolve these signals so as extracts the maximum meaningful information and make the best possible decisions usually leads to problems that are mathematically known as ill-possible and ill-conditioned. That is, there may exist insufficient information from which to draw unique conclusions, and simultaneously, small uncertainties within the datasets may lead to mutual inconsistencies within the competing hypotheses. How the brain processes signals and attemts to learn from them is a mystery. Under the best of circumstances, the brain can usually perform well when solving problems involving deductive inferencing. However, when attempting to form decisions from incomplete or ambigous pieces of information, if often falls prey to what is referred to as “cognitive illusions”. This article illustrates the potential for powerful artificial intelligence (AI) techniques when used in the analysis not only of the formidable problems that now exist in the NASA earth science programs, but also those to be encountered in the future Mission to Planet Earth (MTPE) and Earth Observing System (EOS) programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, stongly emphasize the synergetic relation between data and information. In particular, we address a complex, nonlinear system of under-determined and ill-conditioned equations that arise from the conditions of insufficient and overlapping data. The specific problem involves the estimation of the earth's vertical atmospheric ozone profile over 92 layers from 12 solar backscattered ultraviolet (SBUV) radiation data values. To accomplish this, we employ a given atmospheric radiative transfer function to transform a known ozone profile into SBUV equivalent, single-scattering data values ranging from 255.7 to 339.9 nm. We then use these simulated data values together with the known total ozone value and the radiative transfer function, to retrieve the known ozone profile. An analysis of this problem shows that while the data may fully specify the likelihood of a profile, the a priori information is often dismissed as being not as fully cogent as the data. In this application, a maximum entropy-derived Bayesian method is introduced. This method fully utilizes the evidence of prior information and makes logical assignments of numerical values to probabilities from the measured data. Since the number of levels over which the ozone is distributed is greater than the number of measured radiances, the problem of inferring the profile is nonlinear, and since the is an ill-posed one. In addition, the given profile is nonlinear, and since the transfer function is itself dependent on the profile, the information passed from the profile plane to the data plane is expressed as a Fredholm integral equation of the first kind. The results obtained are seen to compare favorably with those determined by the standard optimal statistical technique used by atmospheric chemists. Ozones retrieval appears to be well suited to an induction inference analysis that encompasses both logical and probability-based reasoning." @default.
• W2029386311 created "2016-06-24" @default.
• W2029386311 creator A5013428399 @default.
• W2029386311 creator A5033884913 @default.
• W2029386311 creator A5048001625 @default.
• W2029386311 creator A5054901847 @default.
• W2029386311 creator A5065198559 @default.
• W2029386311 date "1994-09-01" @default.
• W2029386311 modified "2023-09-27" @default.
• W2029386311 title "Maximum entropy-driven bayesian reasoning in data classification" @default.
• W2029386311 cites W1496923911 @default.
• W2029386311 cites W1605238095 @default.
• W2029386311 cites W1663018970 @default.
• W2029386311 cites W1977633159 @default.
• W2029386311 cites W1984619476 @default.
• W2029386311 cites W1993835430 @default.
• W2029386311 cites W1995875735 @default.
• W2029386311 cites W1997322675 @default.
• W2029386311 cites W2007994197 @default.
• W2029386311 cites W2018490621 @default.
• W2029386311 cites W2024060531 @default.
• W2029386311 cites W2025130480 @default.
• W2029386311 cites W2030121615 @default.
• W2029386311 cites W2032558547 @default.
• W2029386311 cites W2040623864 @default.
• W2029386311 cites W2046942789 @default.
• W2029386311 cites W2049003095 @default.
• W2029386311 cites W2050129620 @default.
• W2029386311 cites W2061687365 @default.
• W2029386311 cites W2071505846 @default.
• W2029386311 cites W2080278412 @default.
• W2029386311 cites W2122886072 @default.
• W2029386311 cites W2126163471 @default.
• W2029386311 cites W2127541064 @default.
• W2029386311 cites W2138545866 @default.
• W2029386311 cites W2146220607 @default.
• W2029386311 cites W2159080219 @default.
• W2029386311 cites W2764433274 @default.
• W2029386311 cites W2978725006 @default.
• W2029386311 doi "https://doi.org/10.1016/0736-5853(94)90021-3" @default.
• W2029386311 hasPublicationYear "1994" @default.
• W2029386311 type Work @default.
• W2029386311 sameAs 2029386311 @default.
• W2029386311 citedByCount "2" @default.
• W2029386311 countsByYear W20293863112016 @default.
• W2029386311 crossrefType "journal-article" @default.
• W2029386311 hasAuthorship W2029386311A5013428399 @default.
• W2029386311 hasAuthorship W2029386311A5033884913 @default.
• W2029386311 hasAuthorship W2029386311A5048001625 @default.
• W2029386311 hasAuthorship W2029386311A5054901847 @default.
• W2029386311 hasAuthorship W2029386311A5065198559 @default.
• W2029386311 hasConcept C107673813 @default.
• W2029386311 hasConcept C119857082 @default.
• W2029386311 hasConcept C154945302 @default.
• W2029386311 hasConcept C160234255 @default.
• W2029386311 hasConcept C2776214188 @default.
• W2029386311 hasConcept C41008148 @default.
• W2029386311 hasConcept C49937458 @default.
• W2029386311 hasConcept C9679016 @default.
• W2029386311 hasConceptScore W2029386311C107673813 @default.
• W2029386311 hasConceptScore W2029386311C119857082 @default.
• W2029386311 hasConceptScore W2029386311C154945302 @default.
• W2029386311 hasConceptScore W2029386311C160234255 @default.
• W2029386311 hasConceptScore W2029386311C2776214188 @default.
• W2029386311 hasConceptScore W2029386311C41008148 @default.
• W2029386311 hasConceptScore W2029386311C49937458 @default.
• W2029386311 hasConceptScore W2029386311C9679016 @default.
• W2029386311 hasLocation W20293863111 @default.
• W2029386311 hasOpenAccess W2029386311 @default.
• W2029386311 hasPrimaryLocation W20293863111 @default.
• W2029386311 hasRelatedWork W133559736 @default.
• W2029386311 hasRelatedWork W1491288126 @default.
• W2029386311 hasRelatedWork W1909325438 @default.
• W2029386311 hasRelatedWork W1964518441 @default.
• W2029386311 hasRelatedWork W2075608909 @default.
• W2029386311 hasRelatedWork W2089904831 @default.
• W2029386311 hasRelatedWork W2100645164 @default.
• W2029386311 hasRelatedWork W2119062301 @default.
• W2029386311 hasRelatedWork W2147997398 @default.
• W2029386311 hasRelatedWork W2160945024 @default.
• W2029386311 hasRelatedWork W2181446350 @default.
• W2029386311 hasRelatedWork W2189215483 @default.
• W2029386311 hasRelatedWork W2544448998 @default.
• W2029386311 hasRelatedWork W2626667510 @default.
• W2029386311 hasRelatedWork W2738169846 @default.
• W2029386311 hasRelatedWork W2781872495 @default.
• W2029386311 hasRelatedWork W2949638400 @default.
• W2029386311 hasRelatedWork W29664458 @default.
• W2029386311 hasRelatedWork W630157649 @default.
• W2029386311 hasRelatedWork W2184474422 @default.
• W2029386311 isParatext "false" @default.
• W2029386311 isRetracted "false" @default.
• W2029386311 magId "2029386311" @default.
• W2029386311 workType "article" @default.