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F. A. Gonzalez and D. Dasgupta, “Anomaly Detection Using Real-Valued Negative Selection,” Genetic Programming and Evolvable Machines, Vol. 4, No. 4, 2003, pp. 383-403.

has been cited by the following article:

• TITLE: Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

  AUTHORS: Rongshuai Li, Akira Mita, Jin Zhou

  KEYWORDS: Structural Health Monitoring; Adaptive Immune Clonal Selection Algorithm; Symbolic Time Series Analysis; Real-Valued Negative Selection; Building Structures

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.1, February 22, 2013

  ABSTRACT: This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.