• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.701-709
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• 2015

Image stitching method is a technique for obtaining an high-resolution image by combining two or more images. In X-ray image for clinical diagnosis, the size of the imaging region taken by one shot is limited due to the field-of-view of the equipment. Therefore, in order to obtain a high-resolution image including large regions such as a whole body, the synthesis of multiple X-ray images is required. In this paper, we propose a rapid stitching method of digital X-ray images using template-based registration. The proposed algorithm use principal component analysis(PCA) and k-nearest neighborhood(k-NN) to determine the location of input images before performing a template-based matching. After detecting the overlapping position using template-based matching, we synthesize input images by alpha blending. To improve the computational efficiency, reduced images are used for PCA and k-NN analysis. Experimental results showed that our method was more accurate comparing with the previous method with the improvement of the registration speed. Our stitching method could be usefully applied into the stitching of 2D or 3D multiple images.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• Korean journal of applied entomology
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• v.32 no.3
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• pp.354-371
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• 1993

Behavior of adult females of the smoky brown cockroach was observed for 10-15 days continuously by using computer and the automatic sensoring system. Under the light condition of l2L~ 12D, individual variations were generally higher and the periodicity appeared less in terms of the presence time at the micro-habitats and the locomotory activity. The smoky brown cockroach appeared 20.42, 11.50, 6.31 and 2.66 % in a day in averages respectively at the shelter and the places for other individuals, feeding and drinking. It stayed 20.29 % in a day at the shelter when food, water, and other individuals were not supplied. Visiting rates were higher at the feeding and dnnking places than at. the ot.her micro-habitats. The degree of t.he locomotory activity was relatively lower when food, water, and other individuals were not supplied t.han when they were supplied. The Pnncipal Component Analysis (PCA) on the presence at the micro-habitats showed that. t.he presence pattern for each mdividual appeared differently (Q mode) while the differem time zones were grouped to the photophase and scotophase (R mode). When food, water, and other individuals were supplied the degree of grouping was higher at the shelter than at the places for feeding and drinking. When the act.ivityand the presence time at the different micro-habitats were jointly analyzed by PCA, the achvity of the female smoky brown cockroach appeared in relation with the presence time ( %) at the places for feeding and other individuals.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.10-17
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• 2007

The echo signal on ultrasonic transducer is a mixed signal from tissues, blood vessel walls, blood cells and noise. In this mixed-signal, the signal reflected from tissues and blood vessel walls is called clutter. It is necessary to extract pure blood signal from this mixed-signal, when measuring blood flow velocity with medical ultrasonic system The quality of measured blood flow velocity is highly dependent on sufficient attenuation of the clutter signals. In this paper, we suggest a clutter rejection method using ICA For simulation, the echo signals are generated by Field n ultrasonic simulation program In this echo signals, independent signals are separated by using ICA Then the blood signal is obtained from the separated signals. Blood flow velocity is measured by 2D autocorrelation method. We compare ICA clutter rejection method with PCA-based eigen filter method using both measured blood flow velocity profiles by 2D autocorrelation. In simulation results, ICA clutter rejection method can be better applied measuring blood flow velocity in noisy echo signals.

• Korean Journal of Crystallography
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• v.2 no.2
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• pp.22-27
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• 1991

4-Amino-n-(2,6-dimethyk4-pyrimidnyl) benzenesulfonamide, C12H14N402. Unit cell parameters are a =12.626, b=11.262, c=9.375, a:b:r=90°, V =1333.07h3, D,at=1.390 g /cm3, and λ(Cu-Ka)=1.5418, The space group is Pca21, Orthorhombic. The final R factor of 1068 unique observed reflections is R=0.040. Two pair of molecules which related by symmetry operation has strong hydrogen bond. One is between H(N2) and N(3), and the other is H(NIA) and 0(1).

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Korean Chemical Engineering Research
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• v.50 no.4
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• pp.684-689
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• 2012

Poly(acrylic acid) (PAA) microspheres is one of the widely-used polymeric materials for the bio-field application and the electric materials. For the synthesis of PAA microspheres, the polymerization technique using surfactants is applied. After the synthesis, the purification and separation processes are required for the removal of surfactant. When general organic solvents were used, many problems, such as huge amount of waste solvent, additional separation processes, and the possibility of residual media, were occurred. Thus, High-pressure Soxhlet extraction using liquid $CO_2$ was developed to solve these problems. In this study, High-pressure Soxhlet extraction of the synthesized PAA microspheres using liquid $CO_2$ was conducted for the removal of Monasil PCA which is used for the dispersion polymerization of acrylic acid in compressed liquid Dimethyl ether (DME). The morphology of the extracted PAA particles was checked by field emission scanning electron microscopy (FE-SEM) and the residual concentration of Monasil PCA was analyzed by inductively coupled plasma - Optical Emission Spectrometer (ICP-OES). For studying the effect of the solvent effect, Soxhlet extraction was conducted using n-hexane, liquid DME, and liquid $CO_2$. In case of n-hexane, some extracted PAA microspheres were produced. However, deformation was also occurred due to the high thermal energy of n-hexane vapor. Liquid DME could not remove Monasil PCA. When using liquid $CO_2$, the extracted PAA microspheres which were free for the residual solvent were produced without deformation. For finding the optimum operating condition, high-pressure Soxhlet extraction was conducted for 8 hours with changing the temperature of reboiler and condenser. When the extractor temperature is $19.6{\pm}0.2^{\circ}C$ and the pressure is $51.5{\pm}0.5$ bar, the best removal efficiency was obtained.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1281-1281
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• 2001

Near-infrared (NIR) spectra have bean measured for high-density (HDPE), linear low-density (LLDPE), and low-density (LDPE) polyethylene in pellet or thin films. The obtained spectra have been analyzed by conventional spectroscopic analysis methods and chemometrics. By using the second derivative, principal component analysis (PCA), and two-dimensional (2D) correlation analysis, we could separate many overlapped bands in the NIR. It was found that the intensities of some bands are sensitive to density and crystallinity of PE. This may be the first time that such bands in the NIR region have ever been discussed. Correlations of such marker bands among the NIR spectra have also been investigated. This sort of investigation is very important not only for further understanding of vibration spectra of various of PE but also for quality control of PE by vibrational spectroscopy. Figure 1 (a) and (b) shows a NIR reflectance spectrum of one of the LLDPE samples and that of PE, respectively. Figure 2 shows a PC weight loadings plot of factor 1 for a score plot of PCA for the 16 kinds of LLDPE and PE based upon their 51 NIR spectra in the 1100-1900 nm region. The PC loadings plot separates the bands due to the $CH_3$ groups and those arising form the $CH_2$ groups, allowing one to make band assignments. The 2D correlation analysis is also powerful in band enhancement, and the band assignments based upon PCA are in good agreement with those by the 2D correlation analysis.(Figure omitted). We have made a calibration model, which predicts the density of LLDPE by use of partial least square (PLS) regression. From the loadings plot of regression coefficients for the model , we suggest that the band at 1542, 1728, and 1764 nm very sensitive to the changes in density and crystalinity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.416-424
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• 2017

In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.145-155
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• 2023

Objectives: A fit test panel is needed to identify the fit performance of a respirator and its face seal. This is a criterion for selecting subjects that can represent the facial characteristics of users. Although anthropometry data has been developed for people in United States and China it is not yet present in Korea. This study aimed to develop a Korean fit test panel and test headform. Methods: For the 7th and 8th waves of the Size Korea anthropometry data, facial measurements of 11,429 people aged 15 to 69 years were used for analysis. PCA and bivariate panel were classified using the ISO16976-2:2022(E) anthropometrics analysis method. Based on this result, a static headform was developemed and a fit test chamber was constructed. Results: Of the 11,429 Korean people used for principal component analysis, 11,300 were included in the ellipse, marking an acceptance rate of 98.87% on PCA panel. The face types were classified into five types. Among them, a large, medium, and small static headform were printed using a 3D printer. In addition, 10,985 people (96.12%) were included in the bivariate panel based on face length and face width. The y-axis (face length) boundary was 97.87 to 134.59 mm, and the x-axis (face width) boundary was 120.75 to 158.23 mm. Conclusions: Compared to the ISO analysis, the Korean principal component was narrower in the width item (PC1) and longer in the length item (PC2). For the future, it is necessary to conduct a fit test using the developed headform and chamber device to confirm the usefulness of this Korean test panel. Therefore, this study is considered valuable as basic research for Korean test panels.