Landmark-based morphometric analysis in selected species of serranid fishes (Perciformes : Teleostei)

Morphological differences among 6 species of marine fishes belonging to 2 subfamilies of the family Serranidae (Serraninae: Dules auriga, Diplectrum formosum, and D, radiale; Epinephelinae: Epinephelus marginatus, Mycteroperca acutirostris, and M. bonaci) were studied by the geometric morphometric method of thin-plate splines and multivariate analysis of partial-warp scores. The decomposition of shape variation into uniform and nonaffine components of shape change indicate that major differences among species are related to both components of shape variation. Significant differences were found among species with respect to the uniform components, but there is no clear separation of taxonomic groups related to these components, and species are instead separated on the basis of body height and caudal peduncle length. Non-uniform changes in body shape, in turn, clearly differentiate the species of Serraninae and Epinephelinae. These shape changes are probably related to differences in habitat and feeding habits among the species.

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.