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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.
In a recent decade, many
DNA sequencing projects are developed on cells, plants and animals over the
world into huge DNA databases. Researchers notice that mammalian genomes encoding
thousands of large noncoding RNAs (lncRNAs), interact with chromatin regulatory
complexes, and are thought to play a role in localizing these complexes to
target loci across the genome. It is a challenge target using higher
dimensional tools to organize various complex interactive properties as
visual maps. In this paper, a Pseudo DNA Variant MapPDVM is proposed following
Cellular Automata to represent multiple
maps that use four Meta symbols as well as DNA or RNA representations. The system architecture of key components and the core mechanism on the PDVM are described.
Key modules, equations and their I/O parameters are discussed. Applying the
PDVM, two sets of real DNA sequences from both the sample human (noncoding DNA) and corn (coding DNA) genomes are
collected in comparison with two sets of pseudo DNA sequences generated by a
stream cipher HC-256 under different modes to show their intrinsic properties in higher levels of similar relationships
among relevant DNA sequences on 2D maps. Sample 2D maps are listed
and their characteristics are illustrated under a controllable environment. Various distributions can be observed on both
noncoding and coding conditions from their symmetric properties on 2D maps.