The human skull, composed of tabula extema, tabula intema, and a porous diploe sandwiched in between, is deformed with changing intracranial pressure （ICP）. Because the human skull＇s thickness is only 6 mm, it is s...The human skull, composed of tabula extema, tabula intema, and a porous diploe sandwiched in between, is deformed with changing intracranial pressure （ICP）. Because the human skull＇s thickness is only 6 mm, it is simplified as a thin-walled shell. The objective of this article is to analyze the strain of the thin-wailed shell by the stress-strain calculation of a human skull with changing ICP. Under the same loading conditions, using finite element analysis （FEA）, the strains of the human skull were calculated and the results were compared with the measurements of the simulative experiment in vitro. It is demonstrated that the strain of the thin-walled shell is totally measured by pasting the one-way strain foils on the exterior surface of the shell with suitable amendment for data. The amendment scope of the measured strain values of the thin-walled shell is from 13.04% to 22.22%.展开更多
The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaini...The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner.For example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections.Given the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of features.Each underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for identification.Therefore,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches.Using our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without mandibles.Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.展开更多
文摘The human skull, composed of tabula extema, tabula intema, and a porous diploe sandwiched in between, is deformed with changing intracranial pressure （ICP）. Because the human skull＇s thickness is only 6 mm, it is simplified as a thin-walled shell. The objective of this article is to analyze the strain of the thin-wailed shell by the stress-strain calculation of a human skull with changing ICP. Under the same loading conditions, using finite element analysis （FEA）, the strains of the human skull were calculated and the results were compared with the measurements of the simulative experiment in vitro. It is demonstrated that the strain of the thin-walled shell is totally measured by pasting the one-way strain foils on the exterior surface of the shell with suitable amendment for data. The amendment scope of the measured strain values of the thin-walled shell is from 13.04% to 22.22%.
基金The work of I.Yuadi and A.T.Asyhari has been supported in part by Universitas Airlangga through International Collaboration Funding(Mobility Staff Exchange).
文摘The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner.For example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections.Given the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of features.Each underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for identification.Therefore,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches.Using our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without mandibles.Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.
