• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.701-709
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• 2010

This paper presents an efficient signature recognition method by using the hybrid similarity criterion, which is in inverse proportion to distance and in proportion to correlation between the images. The distance is applied to express the spacial property of image, and the correlation is also applied to express the statistical property. The proposed criterion provides the robust recognition to both the geometrical variations such as position, size, and rotation and the shape variation. The normalized cross-correlation(NCC), which is calculated by considering 4 directions based on the histogram of binary image, is applied to express rapidly and accurately the similarity between the images. The proposed method has been applied to the problem for recognizing the 20 truck images of 288*288 pixels and the 105(3 persons * 35 images) signature images of 256*256 pixels, respectively. The experimental results show that the proposed method has a superior recognition performance that appears the image characters well. Especially, the hybrid criterion of NCC and ordinal distance has a superior recognition performance to the hybrid criterion using city-block or Euclidean distance.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.742-751
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• 2011

This paper presents a robust 3D model hashing dependent on key and parameter by using heat kernel signature (HKS), which is special shape feature descriptor, In the proposed hashing, we calculate HKS coefficients of local and global time scales from eigenvalue and eigenvector of Mesh Laplace operator and cluster pairs of HKS coefficients to 2D square cells and calculate feature coefficients by the distance weights of pairs of HKS coefficients on each cell. Then we generate the binary hash through binarizing the intermediate hash that is the combination of the feature coefficients and the random coefficients. In our experiment, we evaluated the robustness against geometrical and topological attacks and the uniqueness of key and model and also evaluated the model space by estimating the attack intensity that can authenticate 3D model. Experimental results verified that the proposed scheme has more the improved performance than the conventional hashing on the robustness, uniqueness, model space.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.463-470
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• 2014

Stealth technology of combat aircraft is most significant capability in recent air battlefield. As the detector of IR missiles is being developed, IR stealth capability which is evaluated by IR signature level become more important than it was in previous generation. Among IR signature of aircraft from various sources, aerodynamic heating dominates in long-wavelength IR spectrum of $8{\sim}12{\mu}m$. Skin temperature change by aerodynamic heating which is derived by effects of Mach number and structure. The 4th and 5th generation aircraft are selected for calculation of the skin temperature, and its height and velocity in numerical conditions are 10,000 m and Ma 0.9~1.9 respectively. Aircraft skin temperature is calculated by computing convection of fluid and conduction, convection and radiation of surface. As the aircraft accelerates to higher Mach number, maximum skin temperature increases more rapidly than average temperature and temperature distribution changes in more sharp, interactive ways. The 4th generation aircraft whose shape is more complex than that of the 5th generation aircraft have complicated temperature distribution. On the other hand, the 5th generation aircraft whose shape is relatively simple shows plain temperature distribution and lower skin temperature in terms of both average and maximum value.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

• Journal of computational fluids engineering
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• v.3 no.1
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• pp.100-107
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• 1998

A numerical analysis of thermal stress over temperature variations near the crystal-melt interface is carried out for a floating-zone growth of Cadmium Telluride (CdTe). Thermocapillary convection determines crystal-melt interfacial shape and signature of temperature in the crystal. Large temperature gradients near the crystal-melt interface yield excessive thermal stresses in a crystal, which affect the dislocations of the crystal. Based on the assumption that the crystal is elastic and isotropic, thermal stresses in a crystal are computed and the effects of operating conditions are investigated. The results show that the extreme thermal stresses are concentrated near the interface of a crystal and the radial and the tangential stresses are the dominant ones. Concentrated heating profile increases the stresses within the crystal, otherwise, the pulling rate decreases the stresses.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.5
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• pp.621-627
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• 1999

This paper presents Raman scattering of liquid water to obtain the characteristics with variation of temperature. Very clear Stokes-Raman signals were observed, which shows H-O vibration stretching and H-O-H vibration bending. The obtained spectrum were processed by FFT filter to extract the noise and base. The spectral shape of the H-O stretching provided a various sensitive signature which allowed temperature to be determined by a curve-fitting technique. Those are Maximum Intensity, Maximum Wave Length, FWHM(Full Width at Half Maximum), PMCR(Polymer Monomer Concentration Ratio) and TSIR(Temperature Sensitive Intensity Ratio). TSIR method shows the highest accuracy of $0.1^{\circ}C$ in mean error and $0.32^{\circ}C$ In maximum error.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.6 s.150
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• pp.693-699
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• 2006

This paper proposes a design optimization technique for ship RCS signature reductions using Taguchi method. The proposed technique comprises of i)evaluating initial RCS signatures, ii)defining critical areas which should be modified as design parameters, and threat factors which can't be controlled artificially as noise parameters, and finally iv)finding optimum parameters via analyzing signal to noise ratios for designated characteristics. We applied the technique to a model ship and found that it is suitable for radar stealth designs. In addition, the proposed technique is applicable to submarine designs against sonar threats.