PubMed · 11759709
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
D Scalise. 2001. Code yellow.. https://pubmed.ncbi.nlm.nih.gov/11759709/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET). The second study used data from a laboratory controller-in-the-loop simulation of Free Flight conditions. Instead of assigning each potential conflict event as a signal (conflict) or non-signal, each event was defined as a signal (conflict) to some fuzzy degree between 0 and 1 by mapping distance into the range [0, 1]. Each event was also given a fuzzy membership, [0, 1], in the set 'response', based on the perceived probability of a conflict or on the colour-coded alert severity. Fuzzy SDT generally reduced the computed false alarm rate for both the human and machine conflict systems, partly because conflicts just outside the conflict criterion used in conventional SDT, were defined by fuzzy SDT as a signal worthy of some attention. The results illustrate the potential of fuzzy SDT to provide, especially in exploratory data analysis, a more complete picture of performance in aircraft conflict detection and many other applications. Alternative analytic methods also using fuzzy SDT concepts are discussed.
Explore the source record for details and available documents.
Explore the source record for details and available documents.