• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.602-609
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• 2003

This paper describes the fault location calculation using neuro-fuzzy systems in combined transmission lines with underground power cables. Neuro-fuzzy systems used in this paper are composed of two parts for fault section and fault location. First, neuro-fuzzy system discriminates the fault section between overhead and underground with normalized detail coefficient obtained by wavelet transform. Normalized detail coefficients of voltage and current in half cycle information are used for the inputs of neuro-fuzzy system. As the result of neuro-fuzzy system for fault section, impedance of selected fault section is calculated and it is used as the inputs of the neuro-fuzzy systems for fault location. Neuro-fuzzy systems for fault location also consist of two parts. One calculates the fault location of overhead, and the other does for underground. Fault section is completely classified and neuro-fuzzy system for fault location calculates the distance from the relaying point. Neuro-fuzzy systems proposed in this paper shows the excellent results of fault section and fault location.

• 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.14 no.5
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• pp.642-647
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• 2004

The relations of fuzzy entropy, distance measure, and similarity measure are discussed in this paper. For the purpose of reliable signal selection, the fuzzy entropy is proposed by a distance measure. Properness of the proposed entropy is verified by the definition of the entropy measure. Fourier and Wavelet transform are applied to the stator current signal to obtain the fault features of an induction motor. Membership functions for 3-phase currents are obtained by the Bootstrap method and Central Limit Theorem. Finally, the proposed entropy is applied to measure the fault signal of an induction machine, and the fuzzy entropy values of phase currents are illustrated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.165-170
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• 2011

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.128-133
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• 1998

In this paper, we present a parallel framework for tracking moving objects. Parallel framework consists largely of two parts:Search Space Reduction(SSR) and Tracking(TR). SSR is further composed of fuzzy clustering and prediction based on Kalman filter. TR is done by boundarymatching using the Hausdorff distance based on distance transform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.782-791
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• 2005

In this paper, path planning and tracking problems are mentioned to guarantee efficient and safe navigation of autonomous mobile robots. We focus on the path planning and also deal with the path tracking and obstacle avoidance. We improved the conventional distance transform (DT) algorithm for the path planning. Using the improved DT algorithm, we obtain paths with shorter distances compared to the conventional DT algorithm. In the stage of the Path tracking, we employ the fuzzy logic controller to conduct the path tracking behavior and obstacle avoidance behavior. Through computer simulation studies, we show the effectiveness of the Nosed navigational algorithm for autonomous mobile robots.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.295-299
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• 2005

In this paper, path planning and tracking problems are mentioned to guarantee efficient and safe navigation of autonomous mobile robots. We focus on the path planning and also deal with the path tracking and obstacle avoidance. We improved the conventional distance transform (DT) algorithm for the path planning. Using the improved DT algorithm, we obtain paths with shorter distances compared to the conventional DT algorithm. In the stage of the path tracking, we employ the fuzzy logic controller to conduct the path tracking behavior and obstacle avoidance behavior. Through computer simulation studies, we show the effectiveness of the proposed navigational algorithm for autonomous mobile robots.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.295-300
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• 2005

We introduce an x-ray micro tomography system capable of high resolution imaging of a local region inside a small animal. By combining two kinds of projection data, one from a full field-of-view (FOV) scan of the whole body and the other from a limited FOV scan of the region of interest, we have obtained zoomed-in images of the region of interest without any contrast a nomalies. We have integrated a micro tomography system using a micro-focus x-ray source, a $1248\times1248$ flat-panel x-ray detector, and a precision scan mechanism. Using the cross-sectional images taken with the zoom-in micro tomography system, we measured trabecular thicknesses of femur bones in postmortem rats. To compensate the limited spatial resolution in the zoom-in micro tomography images, we used the fuzzy distance transform for the calculation of the trabecular thickness. To validate the trabecular thickness measurement with the zoom-in micro tomography images, we compared the measurement results with the ones obtained from the conventional micro tomography images of the extracted bone samples.

• 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.