IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security

Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security
View Sample PDF
Author(s): Miltiadis Alamaniotis (University of Utah, USA & Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA), Jason Young (Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA)and Lefteri H. Tsoukalas (Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA)
Copyright: 2015
Pages: 20
Source title: Research Methods: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-7456-1.ch046

Purchase

View Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security on the publisher's website for pricing and purchasing information.

Abstract

Analysis of acquired nuclear detector gamma-ray signals for recognition of present radioisotopic signatures is crucial to national security and security applications. Identification algorithms must be accurate and rapid. Artificial intelligence is a scientific field with a variety of tools suitable to implement automated processing of nuclear signals. The use of low resolution portable detectors to measure gamma-ray signals has found a wide use in security and safeguards applications. In this paper, the fuzzy logic based analysis methodology that has been previously developed is applied and assessed on a variety of nuclear signals obtained with a low resolution scintillation detector, and more particularly a sodium iodide (NaI) detector. Various types of fuzzy membership functions are employed and their performance is assessed with regard to the number of positive detections, misses, and false alarms. Furthermore, recorded results from the set of low resolution gamma ray signals are used to estimate the detection sensitivity for each membership function. Results demonstrate the overall effectiveness of the fuzzy logic based identifier, and consist of the main course for the assessment of each membership function. Furthermore, comparison of results designates the triangular membership function as the best membership shape for this type of detector signals.

Related Content

Tutita M. Casa, Fabiana Cardetti, Madelyn W. Colonnese. © 2024. 14 pages.
R. Alex Smith, Madeline Day Price, Tessa L. Arsenault, Sarah R. Powell, Erin Smith, Michael Hebert. © 2024. 19 pages.
Marta T. Magiera, Mohammad Al-younes. © 2024. 27 pages.
Christopher Dennis Nazelli, S. Asli Özgün-Koca, Deborah Zopf. © 2024. 31 pages.
Ethan P. Smith. © 2024. 22 pages.
James P. Bywater, Sarah Lilly, Jennifer L. Chiu. © 2024. 20 pages.
Ian Jones, Jodie Hunter. © 2024. 20 pages.
Body Bottom