• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.457-464
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• 2018

This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn't need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

• Journal of Biomedical Engineering Research
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• v.27 no.4
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• pp.189-196
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• 2006

Micro computed tomography (micro-CT) has been used for in vivo animal study owing to its noninvasive and high spatial resolution capability. However, the sizes of existing detectors for micro-CT systems are too small to obtain whole-body images of a small animal object with $\sim$10 micron resolution and a part of its bones or other organs should be extracted. So, we have introduced the zoom-in micro-tomography technique which can obtain high-resolution images of a local region of an live animal object without extracting samples. In order to verify our zoom-in technique, we performed in vivo animal bone study. We prepared some SD (Sprague-Dawley) rats for making osteoporosis models. They were divided into control and ovariectomized groups. Again, the ovariectomized group is divided into two groups fed with normal food and with calcium-free food. And we took 3D tomographic images of their femurs with 20 micron resolution using our zoom-in tomography technique and observed the bone changes for 12 weeks. We selected ROI (region of interest) of a femur image and applied 2D FDT (fuzzy distance transform) to measure the trabecular bone thickness. The measured results showed obvious bone changes and big differences between control and ovariectomized groups. However, we found that the reliability of the measurement depended on the selection of ROI in a bone image for thickness calculation. So, we extended the method to 3D FDT technique. We selected 3D VOI (volume of interest) in the obtained 3D tomographic images and applied 3D FDT algorithm. The results showed that the 3D technique could give more accurate and reliable measurement.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.34-42
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• 1997

This paper deals with the recognition of closed planar shape using a three dimensional spectral feature vector which is derived from the FFT(Fast Fourier Transform) spectrum of contour sequence and fuzzy ARTMAP neural network classifier. Contour sequences obtained from 2-D planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The Fourier transform of contour sequence and spectrum analyzer are used as a means of feature selection and data reduction. The three dimensional spectral feature vectors are extracted by spectrum analyzer from the FFT spectrum. These spectral feature vectors are invariant to shape translation, rotation and scale transformation. The fuzzy ARTMAP neural network which is combined with two fuzzy ART modules is trained and tested with these feature vectors. The experiments including 4 aircrafts and 4 industrial parts recognition process are presented to illustrate the high performance of this proposed method in the recognition problems of noisy shapes.