A measurement-theoretic analysis of the fuzzy logic model of perception.

The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. FLMP has an impressive history of fitting factorial data, suggesting that its probabilistic form is valid. The authors raise questions about the underlying processing assumptions of FLMP. Although FLMP parameters are interpreted as fuzzy logic truth values, the authors demonstrate that for several factorial designs widely used in choice experiments, most desirable fuzzy truth value properties fail to hold under permissible rescalings, suggesting that the fuzzy logic interpretation may be unwarranted. The authors show that FLMP's choice rule is equivalent to a version of G. Rasch's (1960) item response theory model, and the nature of FLMP measurement scales is transparent when stated in this form. Statistical inference theory exists for the Rasch model and its equivalent forms. In fact, FLMP can be reparameterized as a simple 2-category logit model, thereby facilitating interpretation of its measurement scales and allowing access to commercially available software for performing statistical inference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Scanning Acoustic Microscopy of SCS-6 Silicon Carbide Fiber

Scanning acoustic microscopy of SCS-6 silicon carbide fiber reveals large radial variations in acoustic reflectivity associated with the chemical composition and microstructure of a given fiber region. Rayleigh wave fringe patterns observed in each of five subregions are used to calculate the average Young modulus of that subregion. The Young modulus is found to increase monotonically from 40 GPa in the carbon core to a value of 413 GPa in the stoichiometric SiC region. The effective Young modulus of the fiber as a whole is estimated from the moduli of the individual regions and it is compared with mechanical measurements reported in the literature.     

Blood pressure estimation and beliefs among normotensives and hypertensives.

14 medicated hypertensive, 15 nonmedicated mild hypertensive, 39 normotensive, and 13 hypotensive adults participated in a 1–2 hr laboratory experiment that assessed each S's symptoms, moods, and estimates of systolic blood pressure (SBP) relative to actual SBP levels. Several self-reports and autonomic measures were collected during and after each of 22 tasks. Within-S correlations indicated that all Ss could estimate SBP at levels greater than chance. Further, 68% of the Ss evidenced at least 1 significant symptom–SBP correlation. Although medicated hypertensives believed they could estimate their BP more accurately than other groups, they were actually no more accurate than the other Ss. They also evidenced far fewer empirically derived symptom–SBP and emotion–SBP correlations than any other group. Overall, blood pressure (BP) beliefs were largely inaccurate. If these erroneous beliefs can be eliminated, Ss may be able to estimate BP fluctuations more accurately. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An explicit finite difference method for finite-time observers

In this paper we present a numerical method for estimating the current state of a nonlinear control system. We use finite differences to discretize a modified version of the finite-time observer equations in James. The discretized equations are simple and easily programmed. The convergence and accuracy of the scheme is proved, and the scheme enjoys a number of important properties: availability of rate of convergence estimates, good robustness characteristics, and the ability to handle certain types of discontinuities in the observations. The major disadvantage is that the number of grid points required increases exponentially with the number of state dimensions.     

REENGINEERING DONE RIGHT Intermediate Solutions That Are Cost-Effective

Effective systems development techniques attract a lot of attention, but little has been done to effectively address systems maintenance. This article describes an effective, proven approach to resurrecting existing systems and Increasing their useful life, using a system that employs both software tools and a rigorous methodology.     

The aditi deductive database system

Deductive databases generalize relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive system based on the client-server model; it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses relational technology for efficiency in the management of disk-based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.     

Efficient simulation of hierarchical stochastic activity network models

Stochastic extensions to Petri nets have gained widespread acceptance as a method for describing the dynamic behavior of discrete-event systems. Both simulation and analytic methods have been proposed to solve such models. This paper describes a set of efficient procedures for simulating models that are represented as stochastic activity networks (SANs, a variant of stochastic Petri nets) and composed SAN-based reward models (SBRMs). Composed SBRMs are a hierarchical representation for SANs, in which individual SAN models can be replicated and joined together with other models, in an iterative fashion. The procedures exploit the hierarchical structure and symmetries introduced by the replicate operation in a composed SBRM to reduce the cost of future event list management. The procedures have been implemented as part of a larger performance-dependability modeling package known asUltraSAN, and have been applied to real, large-scale applications. This work was supported in part by the Digital Equipment Corporation Faculty Program: Incentives for Excellence.