Information Science

Automated Monitoring With a BCP Fault-Decision Test

Fault-detection events are evaluated to reduce the incidence of false alarms. The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault.

Posted in: Information Sciences, Briefs

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Vector-Ordering Filter Procedure for Data Reduction

The essential characteristics of original large sets of data are preserved. The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated-monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers.

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Remote Sensing and Information Technology for Large Farms

Timely data on spatial and temporal variations in fields help farmers manage crops. A method of applying remote sensing (RS) and information- management technology to help large farms produce at maximum efficiency is undergoing development. The novelty of the method does not lie in the concept of "precision agriculture," which involves variation of seeding, of application of chemicals, and of irrigation according to the spatially and temporally local variations in the growth stages and health of crops and in the chemical and physical conditions of soils. The novelty also does not lie in the use of RS data registered with other data in a geographic information system (GIS) to guide the use of precise agricultural techniques. Instead, the novelty lies in a systematic approach to overcoming obstacles that, heretofore, have impeded the timely distribution of reliable, relevant, and sufficient GIS data to support day-to-day, acre-to-acre decisions concerning the application of precise agricultural techniques to increase production and decrease cost.

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Developments at the Advanced Design Technologies Testbed

A report presents background and historical information, as of August 1998, on the Advanced Design Technologies Testbed (ADTT) at Ames Research Center. The ADTT is characterized as an activity initiated to facilitate improvements in aerospace design processes; provide a proving ground for product-development methods and computational software and hardware; develop "bridging" methods, software, and hardware that can facilitate integrated solutions to design problems; and disseminate lessons learned to the aerospace and information-technology communities.

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Acoustical Applications of the HHT Method

A document discusses applications of a method based on the Huang-Hilbert transform (HHT). The method was described, without the HHT name, in "Analyzing Time Series Using EMD and Hilbert Spectra" (GSC-13817), NASA Tech Briefs, Vol. 24, No. 10 (October 2000), page 63. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis.

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Detecting Moving Targets by Use of Soliton Resonances

Faint targets moving uniformly would be distinguished from background clutter. A proposed method of detecting moving targets in scenes that include cluttered or noisy backgrounds is based on a soliton-resonance mathematical model. The model is derived from asymptotic solutions of the cubic Schroedinger equation for a one-dimensional system excited by a position-and-time- dependent externally applied potential. The cubic Schroedinger equation has general significance for time-dependent dispersive waves. It has been used to approximate several phenomena in classical as well as quantum physics, including modulated beams in nonlinear optics, and superfluids (in particular, Bose-Einstein condensates). In the proposed method, one would take advantage of resonant interactions between (1) a soliton excited by the position-and-time-dependent potential associated with a moving target and (2) "eigen-solitons," which represent dispersive waves and are solutions of the cubic Schroedinger equation for a time-independent potential.

Posted in: Information Sciences, Briefs, TSP

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Finite-Element Methods for Real-Time Simulation of Surgery

Some accuracy is traded for computational speed. Two finite-element methods have been developed for mathematical modeling of the time-dependent behaviors of deformable objects and, more specifically, the mechanical responses of soft tissues and organs in contact with surgical tools. These methods may afford the computational efficiency needed to satisfy the requirement to obtain computational results in real time for simulating surgical procedures as described in "Simulation System for Training in Laparoscopic Surgery" (NPO-21192) on page 31 in this issue of NASA Tech Briefs.

Posted in: Information Sciences, Briefs, TSP

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