5 resultados para Fuzzy Measure
em Brock University, Canada
Resumo:
Objectlve:--This study examined the intraclass reliability· of different measures of the
excitability of the Hoffmann reflex, derived from stimulus-response curves. The slope of the
regression line of the H-reflex stimulus-response curve advocated by Funase et al. (1994) was
also compared to the peak of the first derivative of the H-reflex stimulus-response curve
(dHIdVmax), a new measure introduced in this investigation. A secondary purpose was to explore
the possibility of mood as a covariate when measuring excitability of the H-reflex arc.
Methods: The H-reflex amplitude at a stimulus intensity corresponding to 5% of the
maximum M-wave (Mmax) is an established measure that was used as an additional basis of
comparison. The H-reflex was elicited in the soleus for 24 subjects (12 males and 12 females)
on five separate days. Vibration was applied to the Achilles tendon prior to stimulation to test
the sensitivity of the measures on test day four. The means of five evoked potentials at each
gradually increasing intensity, from below H-reflex threshold to above Mmax, were used to create
both the H-reflex and M-wave stimulus response curves for each subject across test days. The
mood of the subjects was assessed using the Subjective Exercise Experience Scale (SEES) prior
to the stimulation protocol each day.
Results: There was a modest decrease in all H-reflex measures from the first to third test day,
but it was non-significant (P's>0.05). All measures of the H-reflex exhibited a profound
reduction following vibration on test day four, and then returned to baseline levels on test day
five (P's<0.05). The intraclass correlation coefficient (ICC) for H-reflex amplitude at 5% of
Mmax was 0.85. The ICC for the slope of the regression line was 0.79 while it was 0.89 for
dH/dVmax. Maximum M-wave amplitude had an ICC of 0.96 attesting to careful methodological
controls. The SEES subscales of fatigue and psychological well-being remained unchanged
IV
across the five days. The psychological distress subscale (P
Resumo:
As a result of increased acid precipitation, the pH of a large
number of Canadian Shield lakes has been falling. Prior to this study
there was no documentation available to explain the history of lake
acidification for the Algoma area lakes. In order to obtain this
information the diatom inferred pH technique was developed in this
study.
During two field seasons, July 1981 and July 1982, short sediment
cores (circa 25-30 cm) were collected from 28 study lakes located north
of Lake Superior, District Algoma, Ontario. The surface sediment
diatoms (0-1 cm) from each of these lakes were carefully identified,
enumerated, and classified in terms of their pH indicator status.
The surface sediment diatom analysis indicated that lake pH is one of
the most important factors affecting the species composition and
relative abundance of diatom populations. Thus diatom assemblages can
be sensitive indicators of lake acidification. When Nygaard's index
alpha was plotted against observed lake pH, a statistically
significant relationship resulted (r=-0.89; p=
Resumo:
Heyting categories, a variant of Dedekind categories, and Arrow categories provide a convenient framework for expressing and reasoning about fuzzy relations and programs based on those methods. In this thesis we present an implementation of Heyting and arrow categories suitable for reasoning and program execution using Coq, an interactive theorem prover based on Higher-Order Logic (HOL) with dependent types. This implementation can be used to specify and develop correct software based on L-fuzzy relations such as fuzzy controllers. We give an overview of lattices, L-fuzzy relations, category theory and dependent type theory before describing our implementation. In addition, we provide examples of program executions based on our framework.
Resumo:
Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.
Resumo:
Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.